Tenderness as well as bioactivity – enzymatic exploration of collagen

PhD thesis by

Yu Fu

July 2016

Department of Food Science

Aarhus University

Blichers Allé 20

8830 Tjele

Denmark

Main supervisor

Associate Professor Margrethe Therkildsen

Department of Food Science, Aarhus University.

Co-supervisor

Associate Professor Jette F. Young

Department of Food Science, Aarhus University.

Assessment committee

Associate Professor Merete Edelenbos (Chairman)

Department of Food Science, Aarhus University, Denmark

University Lecturer Per Ertbjerg

Department of Food and Environmental Sciences, University of Helsinki, Finland

Associate Professor Jeanette Otte

Department of Food Science, University of Copenhagen, Denmark

Preface

The work presented in this PhD thesis was performed at the Department of Food Science, Aarhus University from August 1st, 2013 – July 31st, 2016. This project was financially supported by Future Food Innovation, regional consortium of Central Denmark, Graduate School of Science & Technology (GSST) at Aarhus University and Department of Food Science, Aarhus University. As part of the PhD project, a 3-month stay at Department of Nutritional Sciences, University of Manitoba (Canada) was implemented under the supervision of Prof. Rotimi Aluko.

The PhD project has resulted in four research papers. Paper I & II have been published in International Journal of Food Science & Technology and Journal of Functional Foods, respectively. Paper III has been submitted to Food Research International. Paper IV has been submitted to Meat Science. Besides the experimental work, some results have been presented at four international conferences in poster and oral forms, along with three awards, including PhD Student of the Year award in EFFoST 2014, Special Award Oral Presentation in ICoMST 2015 and Second Place Oral Presentation Award in FFNHP 2016.

Acknowledgements

I would like to take this opportunity to convey my gratitude to everyone who helped me throughout my PhD studies. First of all, I would like to express my sincere appreciation towards my two supervisors, Dr. Margrethe Therkildsen and Dr. Jette F. Young for their great supervision, continuous support and superior advice from the first beginning of this research. Thank you for providing me an opportunity to pursue a PhD degree at Aarhus University. It was a nice experience to work with you and I really learned a lot from you both professionally and personally.

I would also like to thank Prof. Rotimi Aluko, at Department of Human Nutritional Sciences, University of Manitoba, who provided me extraordinary supervision and constructive advice for my research work during 3-month research stay. Appreciation also goes to Dr. Monisola Alashi for her help in the lab.

I would further like to thank Dr. René Lametsch and Dr. Cristian De Gobba at Department of Food Sciences, University of Copenhagen, for their help and guidance regarding mass spectrometric analysis.

I am very grateful to Dr. Trine K. Dalsgaard for her valuable advice and indispensable assistance in HPLC analysis. Gratitude also goes to Mette Marie Løkke for her inputs in the QSAR models.

In addition, I appreciate Dr. Niels Oksbjerg and Dr. Martin K. Rasmussen for their help and insightful advice concerning culture.

Special thanks to Hanne S. Møller, Caroline Nebel, Jens Askov Jensen and Marianne Danielsen. This work could not be accomplished without their technical assistance.

Thanks to Jesper, Bjørn, Bashar, Søren and all the colleagues at Department of Food Science, Aarhus University as well as Department of Human Nutritional Sciences, University of Manitoba for the help provided at various occasions.

Finally, words fail me to express my deepest appreciation to my family for their love understanding and support.

Yu Fu

July 2016

Dansk sammendrag

Mørheden af oksekød er en af de vigtigste spisekvalitetsegenskaber, hvilket bekræftes ved at forbrugerne er villige til at betale en mer-pris for denne egenskab. Samtidig varierer mørheden af oksekød rigtig meget hvilket kan tilskrives dels bidraget fra myofibrillære proteiner og dels bindevæv, som er rig på collagen. Collagen findes i kød men især biprodukter fra slagterier og processering er rig på collagen.

Postmortem modning af kød anvendes for at øge mørheden, og dette kan også fremme nedbrydningen af bindevæv. Modning af kød bidrager ikke kun til øget mørhed, men kan også øge indholdet af bioaktive stoffer i oksekød, da der genereres peptid-fraktioner med fysiologisk betydning. Formålet med dette PhD projekt var netop at undersøge hvilken rolle nedbrydning af bindevæv spiller i forhold til dannelse af bioaktive peptider i oksekød og biprodukter.

I dette projekt er collagen peptider med ACE-inhiberende aktivitet fra bovint bindevæv screenet og identificeret ved hjælp af in silico og in vitro hydrolyse. In silico proteolyse af bovint collagen frigjorde et meget stort antal ACE-inhiberende peptidsekvenser, hvilket blev prædikteret med QSAR modeller. To in silico peptider (YW og LRY) blev eksperimentelt verificeret som nye ACE-inhibitorer. In vitro hydrolyse med og Alcalase af ekstraheret bovint collagen frigjorde også ACE-inhiberende peptider. En tre-trins oprensningsproces (ionbytningskromatografi, gelfiltrering og RP-HPLC kromatografi) blev anvendt til aktivitets screening, og to peptider (VGPV og GPRGF) med højest aktivitet blev identificeret med LC-MS. In silico og in vitro gastrointestinal fordøjelse indikerede at VGPV var resistent overfor fordøjelsesenzymer, hvorimod GPRGF blev nedbrudt til mindre ACE-inhiberende peptider (GPR og GF) med dokumenteret lavere IC50 værdier sammenlignet med GPRGF. Mekanismen bag den inhiberende effekt af VGPV og GPRGF blev eksperimentelt bestemt som non-kompetitiv, hvilket blev bekræftet med molekylære modelleringsdata (molecular docking). VGPV og GPRGF kan transporteres over monolag af humane intestinale Caco-2 celler gennem paracellulære transport og bevare deres ACE-inhiberende effekt.

Betydningen af post mortem modning for mørheden og bioaktiviteten af ekstraherede peptider fra oksekød blev undersøgt. Konsistensen (Warner Bratzler shear force) af musklerne longissimus thoracis (LT) og semitendinosus (ST) faldt med stigende modningstid. De ekstraherede lav-molekylære peptider (< 3 kDa) fra modnet oksekød

udviste 2,2-diphenyl-1-picrylhydrazyl (DPPH) radikal scavenging kapacitet samt ACE- og renin-inhiberende aktivitet. De specifikke peptider blev identificeret med LC-MS, og deres bioaktive potentialer blev endvidere prædikteret med in silico analyse (PeptideRanker og BIOPEP). Resultaterne viste at peptider med prædikterede værdier > 0.8 og peptider fra collagen med værdier mellem 0.6-0.8 kan være årsag til de målte bioaktiviteter.

Dette projekt indikerer at peptider fra kollagen med bioaktivitet har potentialet til at indgå som bio-funktionelle ingredienser i fødevarer og ernæringstilskud. Endvidere har projektet vist at post mortem modning af oksekød bidrager til at kombinere bioaktivitet med mørhed, hvilket kan være det teoretiske grundlag for kød industrien til at udvikle oksekød med en mere sund profil.

Summary

Tenderness appears to be the most important attribute of beef as consumers are willing to pay premium for this attribute. However, it seems to be the most variable one of all beef palatability and has been proposed to be dependent on the myofibrillar and to a large extent on the connective tissue. Connective tissue as a major constituent of meat slaughter and processing byproduct is abundant of collagen.

Postmortem aging has been employed to improve beef tenderness, which is beneficial to degrade connective tissue. During this process, not only it contributes to meat tenderness but also it is likely to enhance the bioactivities of beef as it may lead to the generation of fractions with physiological significance. In this regard, the aim of this PhD project was to examine how the role of connective tissue would be observed not only in the light of its contribution to tenderness but also its contribution to the biological activities of the proteins.

In this work, collagen displaying ACE-inhibitory activity derived from bovine connective tissue were screened and identified based on in silico and in vitro protein digestions. In silico of bovine collagen released tremendous ACE-inhibitory peptide sequences and their ACE-inhibitory activities were predicted by QSAR models. Two in silico peptides (YW and LRY) were experimentally verified as novel ACE inhibitors. In vitro digestion of extracted bovine collagen using papain and Alcalase also released ACE-inhibitory peptides. A three-step purification process (anionic exchange, gel filtration and RP-HPLC chromatography) was employed for activity screening and two most active collagen peptides (VGPV and GPRGF) were identified by LC-MS. Moreover, in silico and in vitro gastrointestinal digestion indicated that VGPV remained resistant to digestive , while GPRGF was degraded into smaller ACE-inhibitory peptides (GPR and GF) with documented IC50 values lower than GPRGF. The inhibitory mechanism of VGPV and GPRGF was experimentally determined to be non-competitive inhibitors and supported by molecular docking data. VGPV and GPRGF could be transported across the monolayer of human intestinal Caco-2 cells through paracellular pathway and retained their ACE- inhibitory effects.

The role of postmortem aging in tenderness and bioactivity of the extracted peptides from aged beef was investigated. The gradually decreased Warner-Bratzler shear force (WBSF) values of longissimus thoracis (LT) and semitendinosus (ST) muscles were observed

during aging periods. The extracted low-molecular weight peptides (< 3 kDa) from aged beef samples exhibited 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging, ACE- and renin-inhibitory activities. The corresponding peptide sequences were identified by LC-MS. The bioactive potentials of identified peptides were further predicted through in silico analysis (PeptideRanker and BIOPEP). The results demonstrated the peptides with the predicted scores (> 0.8) as well as collagen peptides (0.6-0.8) may contribute to the measured bioactivities.

The present study indicates that collagen peptides with bioactivities have potential to serve as high value-added and bio-functional ingredients in the food and nutrition industry. Moreover, postmortem aging of beef contributes to the incorporation of bioactivity into tenderness, which provides a theoretical basis for meat industry to development of healthy beef.

Lists of disseminations

A: Papers

Paper I

Fu, Y., Young, J. F., Dalsgaard, T. K., & Therkildsen, M. (2015). Separation of angiotensin I-converting inhibitory peptides from bovine connective tissue and their stability towards temperature, pH and digestive enzymes. International Journal of Food Science & Technology, 50(5), 1234-1243.

Paper II

Fu, Y., Young, J. F., Løkke, M. M., Lametsch, R., Aluko, R. E., & Therkildsen, M. (2016). Revalorisation of bovine collagen as a potential precursor of angiotensin I-converting enzyme (ACE) inhibitory peptides based on in silico and in vitro protein digestions. Journal of Functional Foods, 24, 196-206.

Paper III

Fu, Y., Young, J. F., Dalsgaard, T. K., Lametsch, R., Aluko, R. E., & Therkildsen, M. Angiotensin I–converting enzyme–inhibitory peptides from bovine collagen: insights into inhibitory mechanism and transepithelial transport. Food Research International. Submitted.

Paper IV

Fu, Y., Young, J. F., & Therkildsen, M. Bioactive peptides in beef: endogenous generation through postmortem aging. Meat Science. Submitted.

B: Conference abstracts

Inhibition of angiotensin I-converting enzyme by collagen peptides derived from bovine connective tissue

Fu, Yu; Young, Jette F.; Therkildsen, Margrethe.

Poster presented at 28th European Federation of Food Science & Technology (EFFoST) International Conference, Uppsala, Sweden, 25-28 November, 2014

Towards bovine connective tissue utilization: in silico insight on the potential of collagen in production of ace inhibitory peptides

Fu, Yu; Young, Jette F.; Therkildsen, Margrethe.

Oral presentation at 61st International Congress of Meat Science & Technology (ICoMST), Clermont-Ferrand, France, 23-28 August, 2015

Novel angiotensin I-converting enzyme (ACE) inhibitory peptides from bovine connective tissue: purification and characterization.

Fu, Yu; Young, Jette F.; Therkildsen, Margrethe.

Oral presentation at 29th EFFoST International Conference, Athens, Greece, 10 - 12 November, 2015

Towards novel angiotensin I-converting enzyme-inhibitory peptides from bovine collagen: insights into inhibitory mechanism and transepithelial transport.

Fu, Yu; Young, Jette F.; Lametsch, René; E. Aluko, Rotimi; Therkildsen, Margrethe.

Oral presentation at Functional Foods and Natural Health Products Graduate Research Symposium (FFNHP), Winnipeg, Canada, 20 April, 2016

List of abbreviations

RAS: Renin-angiotensin system

ACE: Angiotensin-I converting enzyme

DH: Degree of hydrolysis

SDS-PAGE : Sodium dodecyl sulfate polyacrylamide gel electrophoresis

IC50: Concentration needed to reach 50% inhibition

FPLC: Fast protein liquid chromatography

HPLC: High performance liquid chromatography

LC-MS: Liquid chromatography - mass spectrometry

MS/MS: Tandem mass spectrometry

WBSF: Warner-Bratzler shear force

QSAR: Quantitative structure-activity relationship

PLS: Partial least square

DMEM: Dulbecco’s modified eagle’s medium

MTT: 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide

TEER: Transepithelial electrical resistance

DPPH: 2,2-diphenyl-1-picrylhydrazyl

RFU: Relative fluorescence units

ROS: Reactive oxygen species

MMP: Matrix metalloproteinases

ANOVA: Analysis of variance

Table of contents

1 Introduction...... 1

1.1 Backgrounds ...... 1

1.2 Objectives ...... 3

1.3 Hypotheses ...... 3

1.4 Outline of the thesis ...... 3

1.5 Meat tenderness ...... 5 1.5.1 The impacts of collagen on tenderness development ...... 5 1.5.2 Changes of collagen during postmortem aging and cooking ...... 6

1.6 Bioactive peptides and metabolic syndrome ...... 7 1.6.1 Antihypertensive/ACE-inhibitory peptides ...... 8 1.6.2 Antioxidant peptides ...... 8 1.6.3 Approaches for discovery of bioactive peptides ...... 10 1.6.3.1 Discovery of bioactive peptides by a classic approach ...... 10 1.6.3.2 Bioinformatics driven approaches for discovery of bioactive peptides...... 11 1.6.3.3 Integrated approaches ...... 12 1.6.4 Digestive stability and bioavailability of bioactive peptides ...... 12

1.7 The possible roles of bioactive peptides in reducing the risk of cardiovascular disease ...... 13

2 Methods ...... 14

2.1 In silico analysis of collagen peptides ...... 14 2.1.1 Collagen sequence alignment by BLAST analysis ...... 14 2.1.2 In silico digestion of bovine collagen ...... 14 2.1.3 Prediction of ACE-inhibitory activity of collagen peptide by QSAR model ..... 15 2.1.4 The probability of collagen peptides to be bioactive predicted by PeptideRanker ...... 15 2.1.5 Molecular docking ...... 15

2.2 Experimental approaches for isolation of peptides from bovine collagen ...... 15 2.2.1 Extraction of bovine collagen ...... 15 2.2.2 Characterization of collagen by SDS-PAGE and analysis ...... 16 2.2.3 In vitro digestion of bovine collagen ...... 16

2.2.4 Fractionation and purification of collagen peptides ...... 16 2.2.5 Identification of the most active collagen peptides by LC-MS ...... 16

2.3 Bioactivity evaluation ...... 17 2.3.1 Measurement of ACE- and renin-inhibitory activity ...... 17 2.3.2 Measurement of antioxidant activity ...... 17 2.3.3 Digestive stability of collagen peptides ...... 18 2.3.4 Cell studies ...... 18 2.3.4.1 Cytotoxicity evaluation of collagen peptides ...... 18 2.3.4.2 Transepithelial transport of collagen peptides and the mechanism ...... 19

2.4 Meat studies ...... 19 2.4.1 Sample preparation ...... 19 2.4.2 Measurement of shear force ...... 19 2.4.3 Peptide extraction ...... 20

2.5 Statistical analysis ...... 20

3 Test of hypothesis ...... 20

4 Discussion ...... 21

4.1 In vitro and in silico digestion of bovine collagen ...... 21

4.2 ACE-inhibitory activity of collagen peptide ...... 22

4.3 Bioavailability of collagen peptides ...... 23

4.4 The role of postmortem aging in tenderness and bioactivity of beef ...... 24

5 Conclusions ...... 26

6 Future perspectives ...... 27

7 References ...... 29

8 Papers ...... 39

1 Introduction

1.1 Backgrounds

Consumer demand related to beef is shifting to products that are safe, nutritious and high quality (Grunert, 2006). An optimized output of carcasses in the meat industry through increased tenderness of beef, development of bioactive compounds against chronic diseases and use of these bioactive compounds from beef as well as byproducts has a very great potential (Verbeke et al., 2010).

Tenderness, color, juiciness, flavor and aroma are all major attributes of beef palatability regarding consumers’ satisfaction, with tenderness being the most important factor (Huffman et al., 1996; Miller et al., 2001). As important as this attribute is, it seems to be the most variable of each meat palatability trait and has been attributed to several factors, including myofibrillar proteins and to a large extent the connective tissue. The connective tissue mainly consists of collagen and elastin, each of which contributes to the background toughness of beef to different extents (Ashie et al., 2002; Belew et al., 2003).

The meat processing industry annually produces tons of byproducts that are normally discarded as waste or revalorized for low-value purposes (Jayathilakan et al., 2012; Toldrá et al., 2014). Bovine connective tissue, abundant of collagen, is one of major constituents of meat processing byproducts (Mokrejs et al., 2009). Thus, conversion of bovine collagen into high value-added ingredients is the top trend in the meat industry, leading to a high benefit-to-cost rate, as these peptides possess tremendous potential for future commercial use as bio-functional ingredients in food systems to combat oxidative stress, hypertension, or as nutraceuticals (Schaafsma, 2009).

Hypertension is a global risk factor for cardiovascular diseases (Ahhmed & Muguruma, 2010). It is estimated that approximately 17.5 million people died from cardiovascular diseases in 2012 (WHO, 2015). The renin-angiotensin system (RAS) is a main pathway responsible for regulating pressure and ensuring fluid . Within RAS, renin can catalyze the conversion of angiotensinogen into angiotensin I that is further converted to angiotensin II by angiotensin I-converting enzyme (ACE), giving rise to the elevated blood pressure and hypertension (Ahhmed & Muguruma, 2010; Crowley & Coffman, 2012). However, synthesized anti-hypertensive drugs, such as Captopril, Lisinopril, Enalapril and Alacepril, have been reported to have some undesirable side effects (e.g. dry cough and edema) or other health complications for long-term

1 administration (Alderman, 1996). Therefore, utilization of natural source-derived antihypertensive or ACE inhibitors with fewer side effects for the management of hypertension has become increasingly attractive.

Oxidative stress, an imbalance between oxidants and antioxidants, might further induce and exacerbate hypertension (Kizhakekuttu & Widlansky, 2010). Reactive oxygen species (ROS) excessively generated during oxidative stress can lead to cell death (Pelicano et al., 2004). Therefore, there is an upsurge for researchers to explore novel natural antioxidants that may protect us against oxidative injury (Devasagayam et al., 2004; Valko et al., 2006; Samaranayaka & Li-Chan, 2011).

Postmortem aging of beef is a very effective method to improve tenderness (Huff-Lonergan et al. 1996). It is defined as the storing of fresh beef at refrigerated temperatures to allow the natural enzymatic and biochemical processes to take place resulting in increased tenderness (Nishimura et al., 1995) due to the weakening of the and the intramuscular connective tissues (Dransfield, 1994). During this process, it not only contributes to meat tenderness but it may also generate peptides with bioactivity, such as antioxidant or blood pressure lowering effects. In order to improve meat tenderness, several approaches have been employed, some of which have been observed to degrade myofibrillar protein or connective tissue, e.g. postmortem aging (Kemp et al., 2010), with proteolytic enzymes (Chen et al., 2006; Sullivan & Calkins, 2010), organic acids (Akta & Kaya, 2001) or phosphate (Murphy & Zerby, 2004).

In recent years, in vitro digestion of meat proteins has been documented to release several potential bioactive peptides exhibiting in vitro antihypertensive/ACE-inhibitory activities (Ryan et al., 2011). In silico approaches have been also used to predict the release of bioactive peptides from known protein sequences and assess the unstudied proteins as the precursor of bioactive peptides (Chanput et al., 2010; Lacroix & Li-Chan, 2012). Bioinformatic tools, such as BLAST (alignment search tool for similarity analysis) (Altschul et al., 2005), the BIOPEP database (Minkiewicz et al., 2008) and QSAR (quantitative structure-activity relationship) (Pripp et al., 2005; Wu et al., 2006) have been successfully utilized to predict and design bioactive peptides from food-derived proteins. To date, bovine connective tissue as a byproduct in meat industry is scantly investigated as a raw material to release of ACE-inhibitory peptides based on the in vitro and in silico approaches. Moreover, the digestive stability, ACE-inhibitory mechanism and transepithelial transport of bovine collagen peptides have not been reported. In addition,

2 few studies are involved in the effects of postmortem aging on the tenderness and bioactivity of beef for exploitation of healthy beef as a natural source of antihypertensive and antioxidant peptides to maintain blood pressure and health.

1.2 Objectives

The overall aim of this study is to examine the role of collagen not only for its contribution to tenderness but also to the bioactivity.

The specific objectives of this work include:

 To predict and evaluate the most active ACE-inhibitory peptides from bovine collagen via in silico approaches;  To isolate, purify and characterize the most active ACE-inhibitory peptides from bovine hydrolysates in vitro digestion;  To examine the digestive stability, inhibitory mechanism and transepithelial transport of the collagen-derived ACE-inhibitory peptides;  To examine the role of postmortem aging in tenderness and bioactivity of in beef.

1.3 Hypotheses

(1) Collagen peptides originating from bovine connective tissue possess ACE-inhibitory activities; (2) Collagen peptides exhibiting ACE-inhibitory activity can be identified based on in silico and in vitro approaches; (3) Collagen peptides have good digestive stability and bioavailability; (4) Postmortem aging of beef contributes to tenderness and release of bioactive peptides.

1.4 Outline of the thesis

This PhD thesis was structured based on the research results of four papers, coupled with introduction, methods, test of hypothesis and discussion sections. Paper I-III focus on the revalorisation of bovine collagen as a potential precursor of ACE-inhibitory peptides based on in silico and in vitro protein digestions as well as digestive stability, inhibitory mechanism and transepithelial transport of collagen peptides. Paper IV explains role of postmortem aging in tenderness and bioactivity of aged beef. An overview of the approaches used in this PhD thesis is shown in Fig. 1A & B.

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Fig. 1A Schematic overview of the approaches used in paper I-IV presented in this thesis

4

Fig. 1B Schematic overview of the approach used in paper IV to explore the simultaneous tenderization and release of bioactive peptides presented in this thesis

1.5 Meat tenderness

Skeletal muscle is comprised of long and parallel cells arranged into muscle fiber surrounded by connective tissue. Each muscle fiber consists of tremendous single strands (myofibrils) (Devine & Dikeman, 2014). Myofibrillar proteins are predominately composed of myosin, actin and titin, accounting for 65–70% (Devine & Dikeman, 2014). Meat tenderness is a key factor influencing the consumers’ evaluation of meat quality (Weston et al., 2002; Wu et al., 2015). The tenderness of meat is believed to be the result of the weakening of the myofibrils and the intramuscular connective tissue (IMCT) (Dransfield, 1994).

1.5.1 The impacts of collagen on tenderness development

IMCT generally consists of collagen and elastin, surrounded by a proteoglycan matrix (Purslow, 2005). The role of connective tissue in meat tenderness is dependent on the amount, type, and extent of intermolecular cross-linking in collagen (Light et al., 1985; Liu et al., 1996). However, the relationship between connective tissue characteristics and

5 tenderness development of meat has not been fully clarified. Collagen acts as an abundant connective tissue constituent (Kennedy & Wess, 2003) and is a crucial factor of variation in meat tenderness. It is reported that the higher content of collagen in Hanwoo beef exhibited the higher values of shear force (Moon, 2006). The solubility of collagen in beef exerts an influence on tenderness. There is a positive correlation between tenderness and collagen solubility (Lepetit, 2008). In addition, the cross-links of collagen contributed to the variations in collagen properties with increased age and heat-stability of the fiber. It is hypothesized that the quality of the collagen other than quantity, was critical for meat tenderness (Bailey & Light, 1989).

A number of studies have revealed the degradation of the collagen network along with time postmortem. A series of studies by Nishimura and colleagues (Nishimura et al., 1996, 1998 & 2010) suggested the changes in connective tissue during postmortem aging. They found that structural endomysium and perimysium were weakened, the endomysium was resolved into individual collagen fibrils and the thick sheets of perimysium were further separated into collagen fibers. In addition, the mechanical strength of the IMCT was decreased gradually after 14 days postmortem.

1.5.2 Changes of collagen during postmortem aging and cooking

Several major enzyme systems have been established in skeletal muscles (Sentandreu et al., 2002), responsible for the degradation of proteins into small peptides during postmortem aging, among which are and the proteasome complex which lead to more general proteolysis, while the and systems result in a more limited and specific proteolysis but play a pivotal role in the early postmortem period (Costelli et al. 2005; Xiong et al., 2012). Even though systems are primarily considered in the investigations concentrating on the mechanisms of meat tenderization, receive more attention mainly due to their ability to weaken during postmortem (Goll et al., 2003). The growing evidence suggests the potential effects of the proteasome complex on contributing to tenderization of stored meat (Lamare et al., 2002; Sentandreu et al., 2002; Herrera-Mendez et al., 2004). Besides, the systems may also contribute to postmortem proteolysis and meat tenderization (Kemp et al., 2010; Lana & Zolla, 2016).

Matrix metalloproteinases (MMPs) are a family of calcium and zinc metallo- , involved in extracellular matrix (ECM) degradation. The turnover of connective tissue is thought to be under the control of MMPs and their inhibitors (tissue inhibitors of metalloproteinase, TIMPs). The MMP family includes at least 24 members

6 that share common functional domains and activation mechanisms (Nagase et al., 2006). There are several distinct subgroups dependent on preferential substrates or similar structural domains. The are active in degradation of native fibrillar collagen, while the gelatinases possess high activity in the denatured collagens. The stromelysins and the matrilysins can cleave noncollagenous components of the ECM (Visse & Nagase, 2003). It is well accepted that IMCT is biochemically degraded during aging of meat (Purslow, 2005, 2014). Stanton & Light (1990) have showed that perimysial collagen was damaged and partially solubilized during postmortem aging. Sylvestre et al. (2002) found that presence of an active MMP-2 in lamb muscles even at 21 day of postmortem aging, at similar levels as had been detected at slaughter, suggesting that MMPs may retain activity for a long period after death of animals. This fact may help elucidate that collagen will become gradually more soluble during the postmortem aging of the meat. Similarly, Lewis et al. (1991) found that the shear force of the perimysial connective tissue in raw beef was decreased during postmortem aging.

In addition, cooking can change the IMCT contribution to meat toughness, including solubilization of collagen and heat denaturation of the insoluble collagen. Moreover, shrinkage of meat during cooking can lead to the increase number of endomysial and perimysial strands and shrinkage of the IMCT network that drives water out of the myofibrillar components, changing their effects on toughness (Purslow, 2014). Therefore, Purslow et al. (2014) came up with a refined hypothesis based on two populations of heat- insoluble collagen molecules (weak and strong pools). The weak collagen molecule is most easily degraded due to proteolysis during aging and cooking. After cooking, the remaining structures in both the aged and unaged meat decide on the remaining strength of the IMCT.

1.6 Bioactive peptides and metabolic syndrome

Bioactive peptides are defined as specific protein fragments that exert a physiological effect in the body (Kitts & Weiler, 2003). Dietary proteins are good sources for generation of bioactive peptides, which are inactive in the native protein sequence and become active when released during gastrointestinal digestion, food processing or food fermentation (Korhonen & Pihlanto, 2003). Typically, these bioactive peptides are composed of 2-20 amino acid residues (Elias et al., 2008). It is well documented that the specific bioactivity of peptides are related to their amino acid composition, hydrophobicity and molecular mass (Korhonen & Pihlanto, 2003; Elias et al., 2008).

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1.6.1 Antihypertensive/ACE-inhibitory peptides

Hypertension, as a global risk factor for cardiovascular diseases, is deemed the world’s largest killer and the estimated one billion people around the world will be diagnosed by 2025 (Ahhmed & Muguruma, 2010; Papadogiannis et al., 2011). Angiotensin I-converting enzyme (ACE, EC 3.4.15.1) plays a pivotal role in the renin-angiotensin system as it catalyses the transition of decapeptide (angiotensin I) to vasoconstrictive octapeptide (angiotensin II), which is a key regulatory peptide in vasodilation (Fig. 2, modified from Ahhmed & Muguruma, 2010). ACE also inactivates the vasodilatory bradykinin in the -kinin system, a peptide that helps reduce the blood pressure (Skeggs et al., 1956). Therefore, effective inhibition of the ACE has been considered a therapeutic approach of hypertension (Ahhmed & Muguruma, 2010).

As synthesized anti-hypertensive drugs have been reported to exhibit several undesirable side effects or other health complications following long-term administration, ACE- inhibitory peptides from food-derived proteins are considered to be safer in combating hypertension (Kim et al., 2001). Therefore, many researchers have concentrated on searching natural sources of ACE inhibitors in the past decade. The widely-studied food protein sources of the ACE-inhibitory peptides include proteins (Miguel et al., 2004; Contreras et al., 2009), proteins (Raghavan & Kristinsson, 2009), animal muscle proteins (Jang & Lee, 2005), egg proteins (Miguel et al., 2007) and soybean proteins (Wu & Ding, 2001). Powerful ACE-inhibitory peptides have been obtained not only from land- based sources, such as porcine skin collagen (Ichimura et al., 2009), bovine skin gelatin (Kim et al., 2001), chicken collagen from leg and bone (Nakade et al., 2008; Saiga et al., 2008), but also from marine sources such as fish skins (Byun & Kim, 2001), fish cartilage (Nagai et al., 2006), scales (Fahmi et al., 2004) and squid tunic (Alemán et al., 2011). Bovine collagen has also been reported to be good sources of ACE-inhibitory peptides by enzymatic digestion (Fu et al., 2015). In addition, in vivo studies using spontaneously hypertensive rats or preclinical experiment have indicated that these peptides are capable of reducing blood pressure significantly, either through intravenous or oral administration (Erdmann et al., 2008).

1.6.2 Antioxidant peptides

Oxidative stress is implicated in a wide range of disease processes, including hypertension, neurodegenerative, , gastric ulcers, diabetes mellitus, and aging (Martindale & Holbrook, 2002; Valko et al., 2007). Reactive oxygen species (ROS)

8 excessively generated during oxidative stress can lead to cell death (Pelicano et al., 2004). Hence, utilization of natural antioxidants that can scavenge or quench ROS or free radicals, thus protect the human body against oxidative injury by ROS is increasingly attractive (Valko et al., 2006; Samaranayaka & Li-Chan, 2011).

Fig. 2 The renin-angiotensin system

Bioactive peptides from various food proteins such as casein, soybean, gelatin and wheat gluten have been reported to have antioxidant capacity (Elias et al., 2008). In the past few years, antioxidant peptides from collagen are gaining increasing attention (Harnedy & FitzGerald, 2012; Piyadhammaviboon et al., 2012). Collagen peptides derived from various fish species, such as Nile tilapia (Ngo et al., 2010), Alaska pollack (Kim et al., 2001), hoki fish (Mendis et al., 2005), Pacific cod (Himaya et al., 2012) and cobia (Yang et al., 2008) are reported to show antioxidant capacity. However, the exact mechanism underlying the antioxidant activity of peptides is not completely clarified. It is reported that collagen peptides could inhibit lipid peroxidation more efficiently than antioxidant peptides derived from many other protein sources (Kim et al., 2001). Furthermore, antioxidant peptides derived from collagen and gelatin may protect living cells against free radical mediated oxidative damage (Himaya et al., 2012). Moreover, the scavenging capacity of free radical

9 species is a key mechanism by which antioxidant peptides elevate cell viability against oxidation-induced cell death.

1.6.3 Approaches for discovery of bioactive peptides

In the last years, the identification and characterization of bioactive peptides have been dedicated by the food researchers. The major approaches used in bioactive peptide research can be summarized in Fig. 3.

Fig. 3 The approaches for the discovery and production of bioactive peptides

1.6.3.1 Discovery of bioactive peptides by a classic approach

Exploration of bioactive peptides using the classic method involves selection of protein sources of particular interest, followed by enzymatic hydrolysis of the protein using food- grade enzymes to generate the hydrolysates. Subsequent fractionation and purification of the resulting hydrolysates based on their particular bioactivity (Udenigwe & Aluko, 2012). The most active bioactive peptide sequences are subjected to identification by mass spectrometry, and their bioactivity is validated by chemically synthesized peptides.

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However, there are several drawbacks of this approach, including time consumption, low yields of isolated peptides and the likelihood that some potent peptides are ignored during purification as the peptides may exhibit synergistic effects (Udenigwe, 2014).

1.6.3.2 Bioinformatics driven approaches for discovery of bioactive peptides

If the classic approach is a major protocol for release of bioactive peptides, the computer- simulated method (e.g. in silico proteolysis) can be also utilized to generate bioactive peptides (Lacroix & Li-Chan, 2012; Gu et al., 2012; Fu et al., 2016). Several popular tools used for in silico proteolysis of protein sequence include BIOPEP “enzyme action” (http://www.uwm.edu.pl/biochemia/index.php/en/biopep), ExPASy PeptideCutter (http://web.expasy.org/peptide_cutter) and PoPS (http://pops.csse.monash.edu.au). Compared with traditional experimental works, it is time-saving and more economical to investigate bioactive peptides derived from food-source proteins such as milk proteins (Iwaniak, Minkiewicz & Darewicz, 2015), porcine proteins (Minkiewicz et al., 2011;), egg proteins (Majumder & Wu, 2010), oat proteins (Cheung et al., 2009) and chickpea proteins (Chang & Alli, 2012).

QSAR, as an essential area of chemometrics, can help search information which relates chemical structure to biological activities by aid of use of computer analysis and has become increasingly crucial prior to in vitro and in vivo protocol in the field of bioactive peptide studies (Pripp et al., 2004; Gu et al., 2011; Iwaniak et al., 2015). The basic assumption in QSAR is that the bioactivity of compounds is closely related to variations in their structures (Hellberg et al., 1987). The QSAR models can be established according to the various molecular descriptors. These molecular descriptors are numerical values, which can characterize the properties of a certain molecule (Hellberg et al., 1987). A comparative QSAR study was implemented regarding the ACE-inhibitory peptides from milk proteins (Pripp et al., 2004). A correlation between ACE-inhibitory activity and structural properties (hydrophobicity, molecular weight of the amino acids and positive charge) at the C-terminal region was ascertained. Similarly, several QSAR models have been established to predict ACE-inhibitory activity for peptides with 2-10 amino acid residues (Wu et al., 2006).

Normally, the in silico approach involves computational mining of protein sequence information in the database, followed by in silico hydrolysis of the protein source based on known enzyme cleavage sites. Moreover, QSAR models can be employed for prediction of activity of peptides for comparison between the in silico and classic approaches. In order to

11 identify novel peptides, the synthesized peptides are used for bioactivity testing. However, in silico peptides may be not experimentally released given that the complex interaction between and proteins as well as the post-translational modifications of protein (Rajendran et al., 2016). Besides, several existing databases are not regularly updated.

1.6.3.3 Integrated approaches

Considering the drawbacks of the aforementioned methods, the integrated approached is proposed. The integrated approach involves the selection of optimal according to in silico prediction, followed by in vitro digestion to generate hydrolysates. The hydrolysates can be characterized by peptidomics (analysis by tandem mass spectrometry) (Sasaki et al., 2010). The identified peptide profile is subjected to activity prediction by in silico methods. PeptideRanker server (http://bioware.ucd.ie/~compass/biowareweb/Server_pages/peptideranker.php) can rank peptide sets and assign scores from 0 to 1 according to structure-function patterns. The closer the predicted score is to 1, the higher possibility is that the peptide is bioactive (Mooney et al., 2012). However, it should be noted that strong knowledge of structure- function patterns needs to be previously established to predict meaningful information, given that the software does not predict the particular type or extent of bioactivity of the peptides (Udenigwe, 2014). In addition, experimental analysis is indispensable in order to better employ the integrated approach for discovery of bioactive peptides.

1.6.4 Digestive stability and bioavailability of bioactive peptides

The stability of bioactive peptides to resist degradation by gastrointestinal digestion is a major concern when they are used as functional ingredients, as further breakdown may cause the loss of the structure and further impaired activity in vivo (Tavares et al., 2011). Digestion is normally started in the stomach where pepsin prevailing under the strongly acidic conditions. Afterwards, the digest is further degraded by pancreatic enzymes, including , and membrane peptidases (Madureira et al., 2010). Pepsin is an aspartic protease, preferentially cleaving the C-terminal of Phe and Leu, while trypsin preferentially cleaves at the C-terminal end of Lys and Arg (Burrell, 1993). Chymotrypsin has a broader specificity, preferentially cleaving the C-terminal, including Phe, Tyr, Trp, and Leu (Burrell, 1993). Thus, the capability of peptides to withstand enzymatic degradation partially depends on their amino acid composition (Ao & Li, 2013). It is documented that peptides containing Pro residues can resist -specific peptidases (Fitzgerald & Meisel, 2000). Collagen peptides derived from bovine collagen

12 retained ACE-inhibitory activity after in vitro digestion (Fu et al., 2015). A possible reason could be due to post-translational hydroxylation of proline in collagen, conferring higher resistance towards gastrointestinal proteases (Liu et al., 2009). However, peptides can still be further digested by brush border peptidases and cytoplasmics, releasing amino acids that can be further absorbed by the intestinal mucous (Picariello et al., 2010).

The peptides that resist the digestive process can be transported into the intestine or may travel cross the epithelium, enter the blood stream and exert a physiological effect (Daniel, 2004; Segura-Campos et al., 2011). The intestinal barrier is of great importance for effective absorption of peptides (Kompella & Lee, 2011). Normally, there are three potential mechanisms responsible for peptide transport across the intestinal epithelium, such as transcytosis, peptide transporter 1 (PepT1)-assisted transport and paracellular pathway through tight junctions (Shimizu et al., 1997; Shimizu, 2004). PepT1 (proton- dependent transporter) is capable of transportation of smaller peptides, such as di- and tri- peptides (Adibi, 1997). On the contrary, transcytosis through internalized vesicle is predominantly in charge of the transport of large molecular weight peptides (Regazzo et al., 2010). The paracellular pathway is mainly regulated by tight junctions (Quirós et al., 2008). A number of different sources of bioactive peptides can be transported across intestines based on several established model systems (e.g. Caco-2 cell monolayer) due to its similarity to mature human enterocytes (Segura-Campos et al., 2011; Li-Chan, 2015). The absorption of peptides can be assessed by whether they are transported from the apical membrane to the basolateral membrane (Hidalgo et al., 1989). Nevertheless, there is still a dearth of report regarding structural requirements for cellular uptake and intestinal peptide intestinal absorption and molecular mechanism thereof.

1.7 The possible roles of bioactive peptides in reducing the risk of cardiovascular disease

A considerable amount of investigations in relation to the antihypertensive effects of bioactive peptides based on spontaneously hypertensive rats and clinical trials of hypertensive human volunteers have revealed that several ACE-inhibitory peptides significantly reduce blood pressure through either intravenous injection or oral administration (Korhonen & Pihlanto, 2003). Moreover, these peptides exert little or no impacts on blood pressure of normotensive subjects. Thus, ACE-inhibitory peptides have a great potential as a supplemental or initial treatment in mildly hypertensive patients (Erdmann et al., 2008). Supplements of antioxidant peptides may contribute to the endogenous antioxidant defense system against oxidative stress (Fang et al., 2002). 13

However, the exact mechanisms have not been fully clarified. The antioxidant capacities of the peptides have reported to involve in scavenging of free radical, singulet oxygen quenching or metal ion chelation (Kitts & Weiler, 2003). Chen et al. (1998) reported that none of these capacities can be attributed solely to the certain antioxidant property of the studied peptides. Therefore, the total antioxidant effect is probably related to the synergistic actions of the above mechanisms. In addition, another antioxidant mechanism could be the expressions of the relevant antioxidant by these peptides based on a human endothelial cell model, which further protect cells against oxidative damage by ROS (Erdmann et al., 2006). Further research remains to be implemented to explore the underlying mechanism and the structure–activity relationship of bioactive peptides.

2 Methods

In order to address 4 hypotheses raised above, some analytical methods have been used in the present study. This section is a summary of the approaches employed in the present study and the detailed description is presented in the original papers (I - V).

2.1 In silico analysis of collagen peptides

2.1.1 Collagen sequence alignment by BLAST analysis

Collagen is not a uniform substance, but is a family of proteins over 28 members ranging from type I to XXVIII (Ricard-Blum, 2011). Type I collagen exists primarily in bovine connective tissue, such as skin, bone and tendons, which consists of two representative protein sequences of alpha-1 chain and alpha-2 chain (Schrieber & Gareis, 2007). Therefore, bovine collagen alpha-1(I) and alpha-2(I) sequences were selected for BLAST analysis to clarify their potential to be ACE inhibitors with similar protein sequences. The detailed information is presented in paper II.

2.1.2 In silico digestion of bovine collagen

In silico digestion of bovine collagen sequences were subjected to the “enzymes action” tool in BIOPEP database (http://www.uwm.edu.pl/biochemia/index.php/pl/biopep) (Iwaniak, Dziuba, & Niklewicz, 2005). All the proteases available in the database were employed for in silico hydrolysis, including chymotrypsin A and C, papain, pepsin, proteinase K, , , thermolysin, , metridin, pancreatic I and II, glycyl endopeptidases, leukocyte elastase, proteinase P1 and ) as well as combination of mixed enzymes (pepsin and trypsin, and pepsin, trypsin and chymotrypsin) to mimic in

14 vitro gastrointestinal enzymatic hydrolysis. The detailed information was referred to paper II.

2.1.3 Prediction of ACE-inhibitory activity of collagen peptide by QSAR model

Predictive models were established by QSAR analysis of physicochemical characteristics (molecular weight, charge, hydrophobicity, secondary structure or sequence) for the specific activities of peptides. In this study, two ACE-inhibitory peptide databases were established, consisting of 166 dipeptides and 141 tripeptides collected from previously published works (Wu, Aluko, & Nakai, 2006). The parameters and procedures of the QSAR models used were referred to paper II.

2.1.4 The probability of collagen peptides to be bioactive predicted by PeptideRanker

The bioactive potential of peptides were analyzed with aid of PeptideRanker software (http://bioware.ucd.ie/~testing/biowareweb/), using the N-to-1 neural network probability (Mooney et al. 2012). Briefly, this model was established by a sequence of variable length N into a single property or fixed-width array of properties. This helps identify among a set of peptides that are more probably to be bioactive (Mooney et al. 2012).

2.1.5 Molecular docking

Molecular docking involves the docking between small molecules ligands (e.g. ACE- inhibitory peptides) and the structures of macromolecular (e.g. ACE) and scoring their potential binging (Pripp, 2007), leading to deep understanding of the interaction from a molecular level. Molecular docking between collagen peptides and ACE was carried out with aid of Accelrys Discovery Studio software 2.5. The molecular docking procedures and parameters were referred to paper IV.

2.2 Experimental approaches for isolation of peptides from bovine collagen

2.2.1 Extraction of bovine collagen

In general, there are three main approaches of collagen extraction, resulting in salt- solubilized, acid solubilized and pepsin-solubilized collagens (Schrieber & Gareis, 2007), among which pepsin treatment possesses a higher yield and exerts a mild impact on the structure of collagen (Schrieber & Gareis, 2007). In this study, the extraction procedures were described in paper I.

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2.2.2 Characterization of collagen by SDS-PAGE and amino acid analysis

SDS-PAGE was used to characterize the forms of the extracted collagen according to the method of Laemmli (1970).

The extracted collagen was subjected to hydrolysis using 6 mol/L HCl at 110 °C for 24 h. The amino acid composition was determined and quantified by a Biochrom amino acid analyzer (Biochrom, Gründau, Germany).

2.2.3 In vitro digestion of bovine collagen

In this work, papain and Alcalase were used in the in vitro digestion. Papain was selected for digestion of collagen based on the in silico analysis that papain can release the highest number of ACE-inhibitory peptides. Alcalase was selected as it has been widely used for hydrolysis of collagen/gelatin to generate bioactive peptides due to its broad specificity and capacity to reach high degree of hydrolysis in a relatively short time (Gómez-Guillén et al., 2011). The procedures for hydrolysis of collagen were referred to paper I.

2.2.4 Fractionation and purification of collagen peptides

Bioactive peptides can be separated and purified from the protein hydrolysate mixture through a series of approaches, including various chromatography and membrane-based separation techniques (Shahidi & Zhong, 2008). A three-step purification process (anionic exchange, gel filtration and RP-HPLC chromatography) was used in this work, as illustrated in Fig. 1A. The fractionation and purification of collagen peptides are referred to paper I, II & III.

2.2.5 Identification of the most active collagen peptides by LC-MS

The most potent collagen peptides purified from Alcalase- and papain-catalyzed groups with the highest ACE-inhibitory activities and the extracted peptides (< 3 kDa) from each aged beef were subjected to identification by LC-MS. The parameters were described in Paper II-IV. Tandem MS spectra were further analyzed by PEAKS Studio 7.5 (Waterloo, ON, Canada) and searched against the customized bovine family (Bos taurus) from UniProt database. The search was carried out using no specific enzyme cleavage sites and an MS/MS mass tolerance of 0.5 Da. The peptides with average local confidence (ALC) over 75% were used for further analysis. The analysis was performed using two individual samples and only peptides positively identified in both samples were acceptable.

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2.3 Bioactivity evaluation

2.3.1 Measurement of ACE- and renin-inhibitory activity

In this study, a rapid and sensitive assay was used for measurement of ACE activity. The fluorescent tripeptide, o-aminobenzoylglycyl-p-nitrophenylalanylproline (Abz-Gly-

Phe(NO2)-Pro) was selected as the substrate. This method is different from the previous method used in other publications where the synthetic peptide hippuril-His-Leu was employed as the substrate (Cushman & Cheung, 1971). However, the latter method has some limitations, such as the use of organic solvent for extraction of the product from the reaction mixture, the limited number of samples that can be analyzed per hour, thus the fluorescent tripeptide method was used. The procedures for determination of ACE- inhibitory activity could be found in paper I. In addition, the IC50 values were estimated by nonlinear regression-global curve fitting (Sigmaplot 11, Systat Software Inc.). IC50 represents the concentration at which ACE activity is inhibited by 50%, which could be used to compare the potencies between different ACE inhibitors. Besides, the ACE inhibition kinetics studies were performed using 0.025, 0.05, 0.1, 0.2 and 1.0 mol/L of substrate in the absence or presence of collagen peptides. The Km and Vmax values for the reaction at different concentrations of collagen peptides were calculated according to Lineweaver-Burk plots.

The renin-inhibitory activity of the extracted peptides from aged beef samples was evaluated using the renin inhibitor screening assay kit from Cayman Chemical Company, Inc. (Ann Arbor, MI, USA). This assay utilizes a renin-based substrate containing the fluorophore EDANS at one terminal and an EDAN-quenching molecule (Dabcyl) at the other terminal (Wang et al., 1993). The peptide-EDANS product can be released after cleavage by renin, producing fluorescence that can be detected using excitation wavelengths of 335-345 nm and emission wavelengths of 485-510 nm. The procedures for measurement of renin-inhibitory activity were described in paper I.

2.3.2 Measurement of antioxidant activity

Antioxidant activity can be assessed by various assays with different mechanisms, such as single electron transfer, hydrogen atom transfer, reducing power, and metal chelation, etc (Shahidi & Zhong, 2015). DPPH radical scavenging assay is the most widely used approach for evaluating antioxidant activity, which is an electron transfer-based method with hydrogen atom transfer mechanism (Prior et al., 2005). This assay is based on electron donation of antioxidants to quenching DPPH radical, companied by absorbance change in 17 the reaction and the discoloration serve as a predictor of the antioxidant capacity (Prior et al., 2005). The procedures for DPPH radical scavenging assay were referred to paper IV.

2.3.3 Digestive stability of collagen peptides

The stability is one of major concerns for development of bioactive peptides as bio- functional ingredients, as further breakdown of the bioactive peptides may lead to reduced activity in vivo (Tavares et al., 2011). In silico digestion can be used for screening stability of bioactive peptides (Lafarga et al., 2014). In this study, the theoretical cleavage site and the resulting digested peptides were evaluated using the program Expasy Peptide-Cutter tool. The theoretical cleavage sites of collagen peptides by pepsin (pH 2.0 and pH > 1.3), trypsin and chymotrypsin (high and low specificity) were evaluated. In order to experimentally verify this, empirical digestion by trypsin was performed according to Shanmugam et al. (2015). Briefly, trypsin was added to the pre-incubated peptide solution (5 mg/mL, pH 8.0 containing 0.03 mol/L NaCl) at the ratio of 1:100 and digested for 3 h at 37 °C. The hydrolysis was monitored by assessing free amino concentrations using N- terminal with fluorescamine (Petrat-Melin et al. 2015). The specific procedures for digestive stability assay for VGPV and GPRGF were referred to paper III.

2.3.4 Cell studies

The Caco-2 cell line is derived from human colon adenocarcinoma (Hidalgo et al., 1989). The cells are cultivated on a microporous membrane in the Millicell® cell culture insert (Merck Millipore, Ireland). After 21 days’ culture, cells differentiated into a monolayer, separating the apical (AP) and basolateral (BL) compartments of the chamber (Fig. 4). The integrity of the cell monolayer can by assessed by measuring the transepithelial electrical resistance (TEER) across the monolayer (Hidalgo et al., 1989; Hubatsch et al., 2007). The detailed information was referred to paper III.

2.3.4.1 Cytotoxicity evaluation of collagen peptides

The MTT assay is a well-known method for cytotoxicity evaluation by determining activity of mitochondrial dehydrogenase in the living cells. MTT is a water soluble tetrazolium salt, which can be converted to insoluble purple formazan by dehydrogenase in the mitochondria (Mossmann, 1983). The detailed information is referred to paper III.

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Fig. 4 Schematic representation of the Caco-2 cell monolayer

2.3.4.2 Transepithelial transport of collagen peptides and the mechanism

In order to check integrity of the Caco-2 monolayer, Lucifer yellow permeability was determined according to the equation below. The transport mechanism of the two identified peptides from collagen (VGPV and GPRGF) across Caco-2 cell monolayers was studied by employment of transport modulators, including Gly-Sar (a peptide transport PepT 1 substrate, 10 mM), wortmannin (a transcytosis inhibitor, 500 nM) , cytochalasin D (a tight junction disruptor, 0.5 μg/mL) . The detailed information is referred to paper III.

퐹푙푢표푟푒푠푐푒푛푐푒푡푒푠푡 − 퐹푙푢표푟푒푠푐푒푛푐푒푏푙푎푛푘 퐿푌푝푒푟푚푒푎푡푎푡푖표푛% = ( ) ∗ 100 퐹푙푢표푟푒푠푐푒푛푐푒푖푛푖푡푖푎푙 − 퐹푙푢표푟푒푠푐푒푛푐푒푏푙푎푛푘

2.4 Meat studies

2.4.1 Sample preparation

Six bulls (3 Danish Holstein + 3 Danish Holstein cross breeds; age: 16 ± 2 months; live weight: 245 ± 35 kg) were obtained from a Danish Crown slaughter house (Aalborg, Denmark). At one day postmortem, longissimus thoracis (LT) and semitendinosus (ST) muscles were removed from each carcass and split in to three parts, vacuum-packed, and aged for additional 0, 9 and 19 days postmortem at 4°C. The day 1, 10 and 20 beef samples were analyzed for texture of the cooked and bioactivity in raw and cooked samples.

2.4.2 Measurement of shear force

The WBSF of aged beef samples was determined as per the method of Honikel (1998). The beef samples were cooked in a water bath at 63 °C for 50 min (internal temperature 62 °C), chilled in running tap-water, stored at 4°C until next day and analyzed. The detailed information was referred to paper IV.

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2.4.3 Peptide extraction

The peptides were extracted from meat samples using 3% perchloric acid to precipitate non-hydrolyzed proteins according to procedures described by Bauchart et al. (2006). The resultant supernatant was subjected to ultra-filtration using 3 kDa cut-off centrifugal filters. The detailed information was presented in paper IV.

2.5 Statistical analysis

The data of shear force values and bioactivity for LT and ST muscles (n=6) measured after 1, 10 and 20 days of aging were presented as means ± standard error (SD). The other measured data were reported as means ± SD from three independent measurements, each performed in triplicate. Differences between groups were analyzed using one-way analysis of variance (ANOVA). Statistical significance was considered at P < 0.05 with Duncan’s procedure of the SPSS version 20.0 program (SPSS Inc., Chicago, IL, USA).

3 Test of hypothesis

In order to test 4 hypotheses in this study, several experiments were performed and described in the 4 papers. The relevant results were summarized below.

(1) Bovine collagen was isolated from connective tissue. Alcalase and papain were employed to generate collagen hydrolysates with different DHs. In vitro ACE-inhibitory activities were evaluated and the two most potent hydrolysates from each enzyme were separated by 3-step purification, including anionic exchange chromatography, gel filtration chromatography and RP-HPLC. Two most active collagen peptides were identified as

VGPV and GPRGF with the estimated IC50 values of 405.12 and 200.91 μM, respectively. The present results confirmed the hypothesis 1 that collagen peptides originating from bovine connective tissue possess ACE-inhibitory activities.

(2) In silico proteolysis by 27 proteases theoretically released numerous ACE-inhibitory peptides from collagen alpha-1(I) and alpha-2(I) sequences. Based on in silico analysis, papain was the most effective protease to release ACE-inhibitory peptides. Two QSAR models for ACE-inhibitory peptides were established and employed to predict the activities of in silico-derived collagen peptides. Two promising in silico peptides (YW and LRY) derived from papain and bromelain digestions were experimentally confirmed as novel ACE inhibitors. In vitro digestion of collagen by papain generated ACE-inhibitory peptides and the most active one was identified as GPRGF. However, GPRGF remained unidentified as the ACE-inhibitory peptide during the in silico digestion by papain mainly due to 20 complete hydrolysis, which was not the case during in vitro digestion affected by external factors. The present results confirmed the hypothesis 2 that collagen peptides exhibiting ACE-inhibitory activities can be identified based on in silico and in vitro approaches, despite that discordance exists between in silico and in vitro approaches.

(3) In silico and in vitro simulated gastrointestinal digestion of VGPV and GPRGF indicated that VGPV remained resistant to digestive enzymes (pepsin, trypsin and chymotrypsin), while GPRGF was degraded into smaller ACE-inhibitory peptides (GPR and GF) with documented IC50 values lower than GPRGF. Based on results of LC-MS analysis and transport mechanism, VGPV and GPRGF could be transported across the monolayer of human intestinal Caco-2 cells through paracellular pathway and retained their ACE-inhibitory effects. The present findings confirmed the hypothesis 3 that collagen peptides have good digestive stability and bioavailability.

(4) The gradually decreased Warner-Bratzler shear force (WBSF) values of LT and ST muscles were observed during aging periods. The low-molecular weight (< 3 kDa) peptides were extracted from aged LT and ST samples and assayed to exhibit DPPH radical scavenging capacity, ACE- and renin-inhibitory activities. The peptide sequences were identified by LC-ESI-MS and their bioactivity potentials were further predicted through in silico analysis (PeptideRanker and BIOPEP), revealing that peptides with the predicted scores (> 0.8) as well as collagen peptides (0.6-0.8) may contribute to the bioactivities. The present findings confirmed the hypothesis 4 that postmortem aging of beef contributes to tenderness and release of bioactive peptides.

4 Discussion

Below the main findings and new insights originating from this PhD thesis were reviewed and discussed. The details of discussion of specific results can be found in paper I-IV.

4.1 In vitro and in silico digestion of bovine collagen

In the present study, in vitro and in silico digestion of bovine collagen were employed to release ACE-inhibitory peptides. Prior to in vitro digestion of bovine collagen, in silico analysis revealed that papain could release the highest number of ACE-inhibitory peptides (paper II). Alcalase was selected for hydrolysis of bovine collagen mainly due to its broad specificity, efficiency to achieve high DH in a relatively short time and capacity to release a considerable amount of bioactive peptides from collagen (Gómez-Guillén et al., 2011; Zhang et al., 2013). However, Alcalase is not available in the database for in silico analysis.

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Thus, it has only been possible to compare the difference between in vitro and in silico digestion of bovine collagen by papain.

In vitro digestion of bovine collagen by papain generated a mixture of peptides with ACE- inhibitory activity. After a 3-step separation procedure and ACE-inhibitory activity screening (described in paper I & II), the most potent peptide was identified as a pentapeptide (GPRGF). The pentapeptide differs from the peptides derived from in silico hydrolysis due to several reasons. Firstly, the parent protein of pentapeptide is derived from bovine collagen alpha-1(II), which exhibits slight difference from collagen alpha-1(I) used in in silico hydrolysis. However, in silico digestion of bovine collagen alpha-1(II) failed to release the pentapeptide. Moreover, in silico proteolysis has several limitations and it is not guaranteed that in silico peptides can be experimentally reproduced with regard to complete protease digestion of proteins (paper II). In addition, the current in silico digestion ignored some peptides in the “in silico hydrolysates” that are not deposited in databases (Udenigwe, 2014). In contrast, in vitro digestion failed to release the smaller peptides probably due to external factors, including substrate condition, protease efficiency, temperature and pH (Gu & Wu, 2013). A number of the other active peptides may be excluded during the purification procedures as a result of co-elution with some weaker fractions with ACE-inhibitory activities. Therefore, an improved approach can be considered to perform complete peptide profiling (peptidomics) of the papain-catalyzed hydrolysate, which helps clearly demonstrate if the same or similar peptides were observed in the two approaches. In addition, the integrated approach based on peptidomics analysis of the protein hydrolysates released from in vitro experimental hydrolysis combined with in silico prediction (e.g. PeptideRanker and structure-function relationship analyses) could be used for discovery of novel bioactive peptides.

4.2 ACE-inhibitory activity of collagen peptide

In the present study, the ACE assay was based on a sensitive and simple method and it differed from previous assays due to the different fluorescent substrate (ABz-Gly-

Phe(NO2)-Pro). The IC50 values estimated by nonlinear regression-global curve fitting were 405.12 and 200.91 μM for VGPV and GPRGF, respectively. In addition, kinetics data revealed that VGPV and GPRGF were non-completive inhibitors, which was supported by molecular docking results that they did not occupy the S1’ and S2’ subsites of ACE and cannot compete with substrate for the of ACE (paper III).

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It is reported that some animal protein-derived ACE-inhibitory peptides with IC50 values above 200 μM still possessed pronounced antihypertensive effects (Toldrá et al., 2012). A collagen-derived ACE-inhibitory peptide (Gly-Pro) with IC50 value of 360 μM could significantly decrease blood pressure of spontaneously hypertensive rats (Nakashima et al., 2002). Furthermore, transepithelial transport experiments of VGPV and GPRGF further revealed their good bioavailability (paper III). Hence, collagen peptides (VGPV and GPRGF) identified in this study were promising to be effective ACE inhibitors and may exert in vivo antihypertensive effects.

4.3 Bioavailability of collagen peptides

The resistance towards intestinal peptidases and transport through the brush border are two key issues of bioavailability of bioactive peptides. In this work, Caco-2 cell monolayer was employed as a model for evaluation of transepithelial transport. The present results indicated that VGPV and GPRGF have stability against epithelial cell peptidases. This fact was mainly due to their structures. In the first place, it is documented that peptides with Pro residue located in the third position are resistant to hydrolysis by dipeptidyl peptidase- IV (DPP-IV), a key peptidase detected in the brush border (Fairweather et al., 2012). Furthermore, peptides with aromatic amino acids (e.g. Phe) at the C-terminal are less susceptible to intestinal peptidases (Bejjani & Wu, 2013). In addition, Phe-based peptides are reported to be DPP-IV inhibitors (Xu et al., 2005). The above information provided a theoretic basis for the fact that VGPV and GPRGF remained resistant towards peptidase and maintained intact in the apical side of the Caco-2 monolayer, which enhanced peptide transportation across intestinal epithelia cells.

Collagen peptides (VGPV and GPRGF) could be transported across the monolayer of human intestinal Caco-2 cells through paracellular pathway according to the results of mechanism study using the modulator of peptide transport. Pretreatment by cytochalasin D (a tight junction disruptor) prior to peptide exposure gave rise to a significant rise of ACE inhibition rate in basolateral side (paper III). In addition, there was no significant effect on the transport with use of Gly-Sar (a peptide transport PepT 1 substrate) and wortamanin (a transcytosis inhibitor). This suggests that the paracellular pathway, as the major route for passive permeation across the mammalian intestinal epithelium, was responsible for the transport of VGPV and GPRGF across Caco-2 monolayers. This result was in line with a number of other food-derived bioactive peptides (Satake et al., 2002; Lei et al., 2008; Ding et al., 2015). It was reported that compounds transported through a paracellular pathway in vitro might have relatively higher bioavailability in vivo 23

(Artursson et al., 2012). Therefore, VGPV and GPRGF with good bioavailability are promising to exert antihypertensive effects in vivo. In addition, the above information on structural requirements for stability and transepithelial transport of bioactive peptides will enhance the strategic design of bioactive peptides that exert bioactivity in vivo.

4.4 The role of postmortem aging in tenderness and bioactivity of beef

Postmortem aging is an effective approach to increase meat tenderness. In this present study, beef samples followed the expected pattern that WBSF values were decreased with postmortem aging. ST muscle is usually reported to possess higher shear force values compared to LT muscle probably due to higher amount of intramuscular connective tissue (collagen) than ST muscle (Rowe, 1986; Jeremiah & Gibson, 2003). In addition, LT and ST muscles were expected to differ in aging profiles due to different patterns of metabolism in two muscles based on the difference in muscle fiber types (Maltin et al., 2003), also reflected by their differences in ultimate pH in this study, but this did not provoke significant variations in WBSF values.

The extracted low-molecular weight peptides (< 3 kDa) from all aged beef of 1, 10 and 20 days exhibited DPPH radical scavenging, ACE- and renin-inhibitory activities. In this study, the 3 kDa cut-off centrifugal filter was employed to obtain low-molecular weight peptides, which may contribute to the measured bioactivity. It is well documented that the bioactivity of peptides typically containing 2-20 amino acid residues per molecule mainly depends on their molecular weight, amino acids composition and sequences (Elias et al., 2008; Samaranayaka & Li-Chan, 2011). Overall, there was a tendency that the extracted peptides from samples from 10 and 20 days possessed higher bioactivity than day 1 samples no matter if they were raw or cooked. Different breakdown patterns of the proteins in the two muscles might also lead to different peptide profiles with different bioactivities. However, no systematic difference between LT and ST was observed in the peptide profiles. The highest ACE-inhibitory activity was observed in the cooked sample of day 10, suggesting that some certain potent peptides were released during both the aging as well as the cooking process (paper IV). Alternatively, the decreased ACE-inhibitory activity at day 20 may be due to degradation of these potent peptides by in meat during aging (Gallego et al., 2014).

Aging time can exert impacts on the generation of peptides and their bioactivity of the aged beef. In the current study, a remarkable increase of the extracted peptide concentration was observed with aging periods, which was in line with a fact that a relative increased

24 concentration of the peptides with postmortem time can be observed in beef muscles (Claeys et al., 2004), indirectly reflected by the increased tenderness of beef partially due to action of proteolysis of structural proteins (Kemp et al., 2010). However, the bioactivity of the extracted peptides derived from the raw and cooked samples were evaluated based on the normalized concentration of peptides (30 μM). Therefore, a possible reason responsible for the changes in bioactivity of different samples is due to some certain active peptides generated in the different phases of aged or cooked beef. Postmortem proteolysis is a dynamic and variable procedure where endopeptidases exert their cleavages randomly to produce a complex mixture of peptides (Gallego et al., 2014). In this study, more peptides (over 50) relative to day 1 and 10 were identified after 20 days postmortem mainly due to extensive degradation of meat proteins during extended aging periods. Furthermore, many of the peptides released during the postmortem aging period may be further hydrolyzed through the action of endopeptidases to generate new ones, which partially elucidated some peptides unidentified at certain time of aging but identification again in later phases.

Cooking temperature can affect the peptides generated in beef (Fogle et al., 2008; Christensen et al., 2013). In the present study, 63 °C was selected as the cooking temperature. It is documented that calpains become rapidly inactivated at 55°C (Christensen et al., 2013), whereas the remaining endogenous peptidases (e.g. cathepsins, or metalloproteinase, etc.) in the cooked samples could still catalyze the proteolysis of proteins during cooking, resulting in richer composition of small peptides (Escudero et al., 2013). To some extent, this fact elucidates the increased DPPH radical scavenging and renin-inhibitory activities in some cooked samples, comparing to raw samples. However, it does not necessary mean that a larger amount of the smaller peptides provoke more potent bioactivity as there may be antagonistic effects within a mixture of bioactive peptides (Hartmann & Meisel, 2007; Hernández-Ledesma et al., 2008).

Structural proteins and proteolytic enzymes are two major factors responsible for the meat tenderness and release of peptides during postmortem aging (Lana & Zolla, 2016). In the present study, a number of peptide fragments derived from several key myofibrillar proteins were identified, including actin, myosin, actinin, troponin and tubulin, etc (paper IV). Moreover, several peptide fragments derived from collagen and elastin, as the major components of the intramuscular connective tissue were found as well, which suggested weakening of the bovine connective tissue, accompanied with the increased tenderness (Weston et al., 2002; Nishimura, 2010). Furthermore, several proteolytic enzyme systems

25 are involved in tenderization process. In this study, the peptides fragments of calpain small subunit 1, and and a disintegrin and metalloproteinase with thrombospondin motifs 2 were identified, which suggested the calpains, cathepsin and MMPs exerted their weakening effects on muscle proteins (including connective tissue) during postmortem aging and cooked periods (paper IV). However, further researches are needed to shed light on whether cathepsin, MMP or collagenase is responsible for degradation of myofibrillar and connective tissue, releasing peptides with bioactivity in the aged and cooked beef samples.

5 Conclusions

In summary, based on the results obtained in this PhD thesis, the following conclusions can be obtained:

 In silico digestion of bovine collagen sequences released a number of ACE- inhibitory peptide sequences and their ACE-inhibitory activities were predicted by QSAR models. The two most active in silico peptides (YW and LRY) derived from papain and bromelain digestion were identified and experimentally confirmed as novel ACE inhibitors.  In vitro digestion of the extracted bovine collagen using Alcalase and papain released ACE-inhibitory peptides. The two most potent collagen peptides from each enzyme group were identified as VGPV and GPRGF after a three-step purification process (anionic exchange, gel filtration and RP-HPLC chromatography).  In silico and in vitro gastrointestinal digestion revealed that VGPV remained resistant to digestive enzymes, while GPRGF was degraded into smaller ACE- inhibitory peptides (GPR and GF).  The ACE-inhibitory mechanism of VGPV and GPRGF was experimentally determined to be non-competitive inhibitors based on Lineweaver-Burk plots and further supported by molecular docking data.  VGPV and GPRGF could be transported across the monolayer of human intestinal Caco-2 cells through a paracellular pathway and retained their ACE-inhibitory effects.  Postmortem aging led to the decreased WBSF values in LT and ST muscles after aging for 1, 10 and 20 days. However, no difference of WBSF was observed between muscles at the same aging day.

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 The extracted low-molecular weight peptides (< 3 kDa) from aged beef samples possessed DPPH radical scavenging, ACE- and renin-inhibitory activities.  The extracted peptide sequences were derived from several structural proteins and enzymes, while in silico analysis (PeptideRanker and BIOPEP) revealed their bioactive potentials, suggesting that peptides with the predicted scores over 0.8 as well as collagen peptides (0.6-0.8) may contribute to the measured bioactivities.  Collagen peptides from bovine connective tissue displaying ACE-inhibitory and have potential to serve as high value-added and bio-functional ingredients in the food and nutrition industry. In addition, postmortem aging of beef contributes to the incorporation of bioactivity into tenderness, which provides a theoretical basis for meat industry to development of healthy beef.

6 Future perspectives

The present PhD thesis provides insights into role of bovine collagen in the contribution to tenderness and bioactivity. Bovine collagen purified from bovine collagen can be used a high value-added ingredient in the bio-functional food products. Moreover, intake of a certain amount of aged beef containing ACE-, renin-inhibitory and antioxidant peptides may play a vital role in maintaining normal level of blood pressure, given that the daily consumption of red meat in Denmark is around 100-150 g/day. However, further studies are needed to elucidate in vivo antihypertensive effects of bovine collagen peptides or aged beef samples. This could be achieved by animal models (e.g. spontaneously hypertensive rats) or human clinical trials to confirm their efficacy and offer prospective health claims on products.

Moreover, the antioxidant activities of the extracted peptides could be further verified by chemical and biological (e.g. in vitro cell model) model systems. For example, the myoblast cell line could be applied for assessment of the protective effects of collagen peptides against H2O2. Similarly, muscle food model systems could be used for antioxidant assessment of collagen peptides against lipid oxidation. Collagen peptides may be added into meat product during processing steps and the efficacy could be evaluated using several common oxidative markers.

In addition, research on the taste and sensory quality attributes of collagen peptides could be performed for further development of functional food ingredients that can benefit human health.

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Finally, the integrated approach (peptidomics combined with in silico analysis) could be was developed for exploration of bioactive peptides (e.g. antioxidant, renin- or DPP-IV- inhibitory peptides) from other protein sources or food processing byproducts.

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7 References

Adibi, S. A. (1997). The oligopeptide transporter (Pept-1) in human intestine: biology and function. Gastroenterology, 113(1), 332-340. Ahhmed, A. M., & Muguruma, M. (2010). A review of meat protein hydrolysates and hypertension. Meat Science, 86(1), 110-118. Aktas, N. and Kaya, M. (2001). The influence of marinating with weak organic acids and salts on the intramuscular connective tissue and sensory properties of beef. European Food Research and Technology, 213(2), 88-94. Alderman, C. P. (1996). Adverse effects of the angiotensin-converting enzyme inhibitors. Annals of Pharmacotherapy, 30(1), 55-61. Alemán, A., Giménez, B., Montero, P., & Gómez-Guillén, M. C. (2011). Antioxidant activity of several marine skin gelatins. LWT-Food Science and Technology, 44(2), 407-413. Altschul, S. F., Wootton, J. C., Gertz, E. M., Agarwala, R., Morgulis, A., Schäffer, A. A., & Yu, Y. K. (2005). Protein database searches using compositionally adjusted substitution matrices. FEBS Journal, 272(20), 5101-5109. Ao, J., & Li, B. (2013). Stability and antioxidative activities of casein peptide fractions during simulated gastrointestinal digestion in vitro: Charge properties of peptides affect digestive stability. Food Research International, 52(1), 334-341. Artursson, P., Palm, K., & Luthman, K. (2001). Caco-2 monolayers in experimental and theoretical predictions of drug transport. Advanced Drug Delivery Reviews, 46(1), 27-43. Ashie, I. N. A., Sorensen, T. L., & Nielsen, P. M. (2002). Effects of papain and a microbial enzyme on meat proteins and beef tenderness. Journal of Food Science, 67(6), 2138-2142. Bailey, A. J., & Light, N. D. (1989). Connective tissue in meat and meat products. London: Elsevier Applied Science. Bauchart, C., Rémond, D., Chambon, C., Mirand, P. P., Savary-Auzeloux, I., Reynes, C., & Morzel, M. (2006). Small peptides (< 5kDa) found in ready-to-eat beef meat. Meat Science, 74(4), 658-666. Bejjani, S., & Wu, J. (2013). Transport of IRW, an ovotransferrin-derived antihypertensive peptide, in human intestinal epithelial caco-2 cells. Journal of Agricultural and Food Chemistry, 61(7), 1487-1492. Belew, J. B., Brooks, J. C., McKenna, D. R., & Savell, J. W. (2003). Warner–Bratzler shear evaluations of 40 bovine muscles. Meat Science, 64(4), 507-512. Burrell, M. M. (1993). Enzymes of molecular biology. Humana Press. Byun, H. G., & Kim, S. K. (2001). Purification and characterization of angiotensin I converting enzyme (ACE) inhibitory peptides from Alaska pollack (Theragra chalcogramma) skin. Process Biochemistry, 36(12), 1155-1162. Chang, Y. W., & Alli, I. (2012). In silico assessment: Suggested homology of chickpea (Cicer arietinum L.) legumin and prediction of ACE-inhibitory peptides from chickpea proteins using BLAST and BIOPEP analyses. Food Research International, 49(1), 477-486. Chanput, W., Theerakulkait, C., & Nakai, S. (2009). Antioxidative properties of partially purified barley hordein, rice bran protein fractions and their hydrolysates. Journal of Cereal Science, 49(3), 422-428. Chen, H. M., Muramoto, K., Yamauchi, F., Fujimoto, K., & Nokihara, K. (1998). Antioxidative properties of histidine-containing peptides designed from peptide fragments 29 found in the digests of a soybean protein. Journal of Agricultural and Food Chemistry, 46(1), 49-53. Chen, Q. H., He, G. Q. Jiao, Y. C., & Ni, H. (2006). Effects of elastase from a Bacillus strain on the tenderization of beef meat. Food Chemistry. 98(4), 624-629. Cheung, H. S., Wang, F. L., Ondetti, M. A., Sabo, E. F., & Cushman, D. W. (1980). Binding of peptide substrates and inhibitors of angiotensin-converting enzyme. Importance of the COOH-terminal dipeptide sequence. Journal of Biological Chemistry, 255(2), 401–407. Cheung, I. W., Nakayama, S., Hsu, M. N., Samaranayaka, A. G., & Li-Chan, E. C. (2009). Angiotensin-I converting enzyme inhibitory activity of hydrolysates from oat (Avena sativa) proteins by in silico and in vitro analyses. Journal of Agricultural and Food Chemistry, 57(19), 9234-9242. Christensen, L., Ertbjerg, P., Loje, H., Risbo, J., van den Berg, F. W. J., & Christensen, M. (2013). Relationship between meat toughness and properties of connective tissue from cows and young bulls heat treated at low temperatures for prolonged times. Meat Science, 93(4), 787-795. Cushman, D. W., & Cheung, H. S. (1971). Spectrophotometric assay and properties of the angiotensin-converting enzyme of rabbit lung. Biochemical Pharmacology, 20(7), 1637- 1648. Rajendran, C.K. S. R., Mason, B., & Udenigwe, C. C. (2016). Peptidomics of Peptic Digest of Selected Potato Tuber Proteins: Post-Translational Modifications and Limited Cleavage Specificity. Journal of Agricultural and Food Chemistry, 64(11), 2432-2437. Costelli, P., Reffo, P., Penna, F., Autelli, R., Bonelli, G., Baccino, F. M. (2005). Ca2+- dependent proteolysis in muscle wasting. The International Journal of Biochemistry & Cell Biology, 37(10), 2134-2146. Crowley, S. D., & Coffman, T. M. (2012). Recent advances involving the renin–angiotensin system. Experimental Cell Research, 318(9), 1049-1056. Daniel, H. (2004). Molecular and integrative physiology of intestinal peptide transport. Annual Review of Physiology, 66, 361-384. de Avellar, I. G., Magalhães, M. M., Silva, A. B., Souza, L. L., Leitão, A. C., & Hermes-Lima, M. (2004). Reevaluating the role of 1, 10-phenanthroline in oxidative reactions involving ferrous ions and DNA damage. Biochimica et Biophysica Acta (BBA)-General Subjects, 1675(1), 46-53. del Mar Contreras, M., Carrón, R., Montero, M. J., Ramos, M., & Recio, I. (2009). Novel casein-derived peptides with antihypertensive activity. International Dairy Journal, 19(10), 566-573. Devasagayam, T. P. A., Tilak, J. C., Boloor, K. K., Sane, K. S., Ghaskadbi, S. S., & Lele, R. D. (2004). Free radicals and antioxidants in human health: current status and future prospects. Japi, 52(794804), 4. Devine, C., & Dikeman, M. (2014). Encyclopedia of meat sciences. Elsevier. Ding, L., Wang, L., Zhang, Y., & Liu, J. (2015). Transport of antihypertensive peptide RVPSL, ovotransferrin 328–332, in human intestinal Caco-2 cell monolayers. Journal of Agricultural and Food Chemistry, 63(37), 8143-8150. Dransfield, E. (1994). Optimization of tenderization, aging and tenderness. Meat Science, 36(1-2), 105-121.

30

Elias, R. J., Kellerby, S. S., and Decker, E. A. (2008). Antioxidant activity of proteins and peptides. Critical Review Food Science Nutrition, 48(5), 430–441. Erdmann, K., Cheung, B. W., & Schröder, H. (2008). The possible roles of food-derived bioactive peptides in reducing the risk of cardiovascular disease. The Journal of Nutritional Biochemistry, 19(10), 643-654. Erdmann, K., Grosser, N., Schipporeit, K., & Schröder, H. (2006). The ACE inhibitory dipeptide Met-Tyr diminishes free radical formation in human endothelial cells via induction of heme oxygenase-1 and ferritin. The Journal of Nutrition, 136(8), 2148-2152. Escudero, E., Mora, L., Fraser, P. D., Aristoy, M.-C., Arihara, K., & Toldra, F. (2013). Purification and Identification of antihypertensive peptides in Spanish dry-cured ham. Journal of Proteomics, 78, 499-507. Escudero, E., Toldrá, F., Sentandreu, M. A., Nishimura, H., & Arihara, K. (2012). Antihypertensive activity of peptides identified in the in vitro gastrointestinal digest of pork meat. Meat Science, 91(3), 382-384. Fahmi, A., Morimura, S., Guo, H. C., Shigematsu, T., Kida, K., & Uemura, Y. (2004). Production of angiotensin I converting enzyme inhibitory peptides from sea bream scales. Process Biochemistry, 39(10), 1195-1200. Fairweather, S. J., Bröer, A., O'Mara, M. L., & Bröer, S. (2012). Intestinal peptidases form functional complexes with the neutral amino acid transporter B0AT1. Biochemical Journal, 446(1), 135-148. Fang, Y. Z., Yang, S., & Wu, G. (2002). Free radicals, antioxidants, and nutrition. Nutrition, 18(10), 872-879. FitzGerald, R. J., & Meisel, H. (2000). Milk protein-derived peptide inhibitors of angiotensin-I-converting enzyme. British Journal of Nutrition, 84(S1), 33-37. Fu, Y., Young, J. F., Dalsgaard, T. K., & Therkildsen, M. (2015). Separation of angiotensin I-converting enzyme inhibitory peptides from bovine connective tissue and their stability towards temperature, pH and digestive enzymes. International Journal of Food Science & Technology, 50(5), 1234-1243. Fu, Y., Young, J. F., Løkke, M. M., Lametsch, R., Aluko, R. E., & Therkildsen, M. (2016). Revalorisation of bovine collagen as a potential precursor of angiotensin I-converting enzyme (ACE) inhibitory peptides based on in silico and in vitro protein digestions. Journal of Functional Foods, 24, 196-206. Gallego, M., Mora, L., Fraser, P. D., Aristoy, M. C., & Toldrá, F. (2014). Degradation of LIM domain-binding protein three during processing of Spanish dry-cured ham. Food Chemistry, 149, 121-128. Goll, D. E., Thompson, V. F., Li, H. Q., Wei, W., & Cong, J. Y. (2003). The calpain system. Physiological Reviews, 83(3),731-801. Gómez-Guillén, M. C., Giménez, B., López-Caballero, M. E., & Montero, M. P. (2011). Functional and bioactive properties of collagen and gelatin from alternative sources: A review. Food Hydrocolloids, 25(8), 1813-1827. Grunert, K. G. (2006). Future trends and consumer lifestyles with regard to meat consumption. Meat science, 74(1), 149-160. Gu, Y., & Wu, J. (2013). LC–MS/MS coupled with QSAR modeling in characterising of angiotensin I-converting enzyme inhibitory peptides from soybean proteins. Food Chemistry, 141(3), 2682-2690.

31

Hartmann, R., & Meisel, H. (2007). Food-derived peptides with biological activity: from research to food applications. Current Opinion in Biotechnology, 18(2), 163-169. Hernández-Ledesma, B., Recio, I., & Amigo, L. (2008). β-Lactoglobulin as source of bioactive peptides. Amino Acids, 35(2), 257-265. Harnedy, P.A. and FitzGerald, R.J. (2012). Bioactive peptides from marine processing waste and shellfish: A review. Journal Functional Foods, 4(1), 6-24. Hellberg, S., Sjoestroem, M., Skagerberg, B., & Wold, S. (1987). Peptide quantitative structure-activity relationships, a multivariate approach. Journal of Medicinal Chemistry, 30(7), 1126-1135. Herrera-Mendez, C. H., Becila, S., Boudjellal, A., & Ouali, A. (2006). Meat ageing: Reconsideration of the current concept. Trends in Food Science & Technology, 17(8), 394- 405. Hidalgo, I. J., Raub, T. J., & Borchardt, R. T. (1989). characterization of the human-colon carcinoma cell-line (Caco-2) as a model system for intestinal epithelial permeability. Gastroenterology, 96(3), 736-749. Himaya, S. W. A., Ryu, B., Ngo, D. H., & Kim, S. K. (2012). Peptide isolated from Japanese Flounder skin gelatin protects against cellular oxidative damage. Journal of Agricultural and Food Chemistry, 60(36), 9112-9119. Honikel, K. O. (1998). Reference methods for the assessment of physical characteristics of meat. Meat Science, 49(4), 447-457. Huang, F., Huang, M., Zhou, G., Xu, X., & Xue, M. (2011). In vitro proteolysis of myofibrillar proteins from beef skeletal muscle by caspase-3 and caspase-6. Journal of Agricultural and Food Chemistry, 59(17), 9658-9663. Hubatsch, I., E. G. E. Ragnarsson, and P. Artursson. 2007. Determination of drug permeability and prediction of drug absorption in Caco-2 monolayers. Nature Protoctol, 2(9), 2111-2119. Huff-Lonergan, E., Mitsuhashi, T., Beekman, D. D., Parrish, F. C., Olson, D. G., & Robson, R M. (1996). Proteolysis of specific muscle structural proteins by mu-calpain at low pH and temperature is similar to degradation in postmortem bovine muscle. Journal of Animal Science, 74(5), 993-1008. Huffman, K. L., Miller, M. F., Hoover, L. C., Wu, C. K., Brittin, H. C., & Ramsey, C. B. (1996). Effect of beef tenderness on consumer satisfaction with steaks consumed in the home and restaurant. Journal of Animal Science, 74(1), 91-97. Ichimura, T., Yamanaka, A., Otsuka, T., Yamashita, E., & Maruyama, S. (2009). Antihypertensive effect of enzymatic hydrolysate of collagen and Gly-Pro in spontaneously hypertensive rats. Bioscience, Biotechnology and Biochemistry, 73(10), 2317-2319. Iwaniak, A., Dziuba, J., & Niklewicz, M. (2005). The BIOPEP database-a tool for the in silico method of classification of food proteins as the source of peptides with antihypertensive activity. Acta Alimentaria, 34(4), 417-425. Iwaniak, A., Minkiewicz, P., Darewicz, M., Protasiewicz, M., & Mogut, D. (2015). Chemometrics and cheminformatics in the analysis of biologically active peptides from food sources. Journal of Functional Foods, 16, 334-351. Jang, A., & Lee, M. (2005). Purification and identification of angiotensin converting enzyme inhibitory peptides from beef hydrolysates. Meat Science, 69 (4), 653-661.

32

Jayathilakan, K., Sultana, K., Radhakrishna, K., & Bawa, A. S. (2012). Utilization of byproducts and waste materials from meat, poultry and fish processing industries: a review. Journal of Food Science and Technology, 49(3), 278-293. Jeremiah, L., & Gibson, L. (2003). The effects of postmortem product handling and aging time on beef palatability. Food Research International, 36(9), 929-941. Kemp, C. M., Sensky, P. L., Bardsley, R. G., Buttery, P. J., & Parr, T. (2010). Tenderness – An enzymatic view. Meat Science, 84(2), 248-256. Kennedy, C. J. and Wess, T. J. (2003). The structure of collagen within parchment - A review. Restaurator-International Journal for the Preservation of Library and Archival Material, 24(2), 61-80. Kim, S. K., Byun, H. G., Park, P. J., & Shahidi, F. (2001). Angiotensin I converting enzyme inhibitory peptides purified from bovine skin gelatin hydrolysate. Journal of Agricultural and Food Chemistry, 49(6), 2992-2997. Kitts, D. D., & Weiler, K. (2003). Bioactive proteins and peptides from food sources. Applications of bioprocesses used in isolation and recovery. Current Pharmaceutical Design, 9(16), 1309-1323. Kizhakekuttu, T. J., & Widlansky, M. E. (2010). Natural antioxidants and hypertension: promise and challenges. Cardiovascular Therapeutics, 28(4), e20-e32. Kompella, U. B., & Lee, V. H. (2001). Delivery systems for penetration enhancement of peptide and protein drugs: design considerations. Advanced Drug Delivery Reviews, 46(1), 211-245. Korhonen, H., & Pihlanto, A. (2003). Food-derived bioactive peptides - Opportunities for designing future foods. Current Pharmaceutical Design, 9(16), 1297-1308. Lacroix, I. M., & Li-Chan, E. C. (2012). Evaluation of the potential of dietary proteins as precursors of dipeptidyl peptidase (DPP)-IV inhibitors by an in silico approach. Journal of Functional Foods, 4(2), 403-422. Lafarga, T., O’Connor, P., & Hayes, M. (2014). Identification of novel dipeptidyl peptidase- IV and angiotensin-I-converting enzyme inhibitory peptides from meat proteins using in silico analysis. Peptides, 59, 53-62. Lamare, M., Taylor, R. G., Farout, L., Briand, Y., & Briand, M. (2002). Changes in proteasome activity during postmortem aging of bovine muscle. Meat Science, 61(2), 199- 204. Lana, A., & Zolla, L (2016). Proteolysis in meat tenderization from the point of view of each single protein: A proteomic perspective. Journal of Proteomics, doi:10.1016/j.jprot.2016.02.011. Lei, L., Sun, H., Liu, D., Liu, L., & Li, S. (2008). Transport of Val-Leu-Pro-Val-Pro in human intestinal epithelial (Caco-2) cell monolayers. Journal of Agricultural and Food Chemistry, 56(10), 3582-3586. Lepetit, J. (2008). Collagen contribution to meat toughness: Theoretical aspects. Meat Science, 80(4), 960-967. Lewis, G. J., Purslow, P. P., & Rice, A. E. (1991). The effect of conditioning on the strength of perimysial connective tissue dissected from cooked meat. Meat Science, 30(1), 1-12. Li-Chan, E. C. (2015). Bioactive peptides and protein hydrolysates: research trends and challenges for application as nutraceuticals and functional food ingredients. Current Opinion in Food Science, 1, 28-37.

33

Light, N., Champion, A. E., Voyle, C., & Bailey, A. J. (1985). The role of epimysial, perimysial and endomysial collagen in determining texture in six bovine muscles. Meat Science, 13(3), 137-149. Liu, A., Nishimura, T., & Takahashi, K. (1996). Relationship between structural properties of intramuscular connective tissue and toughness of various chicken skeletal muscles. Meat Science, 43(1), 43-49. Liu, C., Sugita, K., Nihei, K.I., Yoneyama, K. & Tanaka, H. (2009). Absorption of hydroxyproline-containing peptides in vascularly perfused rat small intestine in situ. Bioscience, Biotechnology, and Biochemistry, 73, 1741–1747. Madureira, A. R., Tavares, T., Gomes, A. M. P., Pintado, M. E., & Malcata, F. X. (2010). Invited review: physiological properties of bioactive peptides obtained from whey proteins. Journal of Dairy Science, 93(2), 437-455. Majumder, K., & Wu, J. (2010). A new approach for identification of novel antihypertensive peptides from egg proteins by QSAR and bioinformatics. Food Research International, 43(5), 1371-1378. Maltin, C., Balcerzak, D., Tilley, R., & Delday, M. (2003). Determinants of meat quality: tenderness. Proceedings of the Nutrition Society, 62(2), 337-347. Martindale, J. L. and Holbrook, N. J. (2002). Cellular response to oxidative stress: Signaling for suicide and survival. Journal of Cellular Physiology, 192(1), 1-15. Mendis, E., Rajapakse, N., Byun, H. G., & Kim, S. K. (2005). Investigation of jumbo squid (Dosidicus gigas) skin gelatin peptides for their in vitro antioxidant effects. Life Sciences, 77(17), 2166-2178. Miguel, M., Recio, I., Gómez-Ruiz, J. A., Ramos, M., & López-Fandiño, R. (2004). Angiotensin I-converting enzyme inhibitory activity of peptides derived from egg white proteins by enzymatic hydrolysis. Journal of Food Protection, 67(9), 1914-1920. Miguel, M., Alonso, M. J., Salaices, M., Aleixandre, A., & López-Fandiño, R. (2007). Antihypertensive, ACE-inhibitory and vasodilator properties of an egg white hydrolysate: effect of a simulated intestinal digestion. Food Chemistry, 104(1), 163-168. Miller, M. F., Carr, M. A., Ramsey, C. B., Crockett, K. L., & Hoover, L. C. (2001). Consumer thresholds for establishing the value of beef tenderness. Journal of Animal Science, 79(12), 3062-3068. Minkiewicz, P., Dziuba, J., & Michalska, J. (2011). Bovine meat proteins as potential precursors of biologically active peptides-a computational study based on the BIOPEP database. Food Science and Technology International, 17(1), 39-45. Minkiewicz, P., Dziuba, J., Iwaniak, A., Dziuba, M., & Darewicz, M. (2008). BIOPEP database and other programs for processing bioactive peptide sequences. Journal of AOAC International, 91, 965-980. Mokrejs, P., Langmaier, F., Mladek, M., Janacova, D., Kolomaznik, K., & Vasek, V. (2009). Extraction of collagen and gelatine from meat industry by-products for food and non food uses. Waste Management & Research, 27(1), 31-37. Moon, S. S. (2006). The effect of quality grade and muscle on collagen contents and tenderness of intramuscular connective tissue and myofibrillar protein for Hanwoo beef. Asian-Australasian Journal of Animal Sciences, 19(7), 1059-1064. Mooney, C., Haslam, N. J., Pollastri, G., & Shields, D. C. (2012). Towards the improved discovery and design of functional peptides: common features of diverse classes permit generalized prediction of bioactivity. PloS one, 7(10), e45012. 34

Mosmann, T. (1983). Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. Journal of Immunological Methods, 65(1-2), 55-63. Murphy, M. A. and Zerby, H. N. (2004). Prerigor infusion of lamb with sodium chloride, phosphate, and dextrose solutions to improve tenderness. Meat Science, 66(2): 343-349. Nagai, T., Nagashima, T., Abe, A., & Suzuki, N. (2006). Antioxidative activities and angiotensin I-converting enzyme inhibition of extracts prepared from chum salmon (Oncorhynchus keta) cartilage and skin. International Journal of Food Properties, 9(4), 813-822. Nagase, H., Visse, R., & Murphy, G. (2006). Structure and function of matrix metalloproteinases and TIMPs. Cardiovascular Research, 69(3), 562-573. Nakade, K., Kamishima, R., Inoue, Y., Ahhmed, A., Kawahara, S., Nakayama, T., Maruyama, M., Numata, M., Ohta, K., Aoki, T., Muguruma, M. (2008). Identification of an antihypertensive peptide derived from chicken bone extract. Animal Science Journal, 79(6), 710-715. Nakashima, Y., Arihara, K., Sasaki, A., Mio, H., Ishikawa, S., & Itoh, M. (2002). Antihypertensive activities of peptides derived from porcine skeletal muscle myosin in spontaneously hypertensive rats. Journal of Food Science, 67(1), 434-437. Ngo, D. H., Qian, Z. J., Ryu, B., Park, J. W., & Kim, S. K. (2010). In vitro antioxidant activity of a peptide isolated from Nile tilapia (Oreochromis niloticus) scale gelatin in free radical-mediated oxidative systems. Journal of Functional Foods, 2(2), 107-117. Nishimura, T. (2010). The role of intramuscular connective tissue in meat texture. Animal Science Journal, 81(1), 21-27. Nishimura, T., Hattori, A., & Takahashi, K. (1995). Structural weakening of intramuscular connective tissue during conditioning of beef. Meat Science, 39(1), 127-133. Nishimura, T., Ojima, K., Liu, A., Hattori, A., & Takahashi, K. (1996). Structural changes in the intramuscular connective tissue during development of bovine semitendinosus muscle. Tissue and Cell, 28(5), 527-536. Nishimura, T., Liu, A., Hattori, A., & Takahashi, A. (1998). Changes in mechanical strength of intramuscular connective tissue during postmortem aging of beef. Journal of Animal Science, 76(2), 528-532. Papadogiannis, D. E., & Protogerou, A. D. (2011). Blood pressure variability: a confounder and a cardiovascular risk factor. Hypertenstion Research, 34(2), 162-163. Pelicano, H., Carney, D., & Huang, P. (2004). ROS stress in cancer cells and therapeutic implications. Drug Resist Update, 7 (2), 97–110. Picariello, G., Miralles, B., Mamone, G., Sánchez‐Rivera, L., Recio, I., Addeo, F., & Ferranti, P. (2015). Role of intestinal brush border peptidases in the simulated digestion of milk proteins. Molecular Nutrition & Food Research, 59(5), 948-956. Piyadhammaviboon, P., Wongngam, W., Benjakul, S., & Yongsawatdigul, J. (2012). Antioxidant and angiotensin-converting enzyme inhibitory activities of protein hydrolysates prepared from threadfin bream (Nemipterus spp.) surimi by-products. Journal of Aquatic Food Product Technology, 21(3), 265-278. Prior, R. L., Wu, X., & Schaich, K. (2005). Standardized methods for the determination of antioxidant capacity and phenolics in foods and dietary supplements. Journal of Agricultural and Food Chemistry, 53(10), 4290-4302.

35

Pripp, A. H. (2007). Docking and virtual screening of ACE inhibitory dipeptides. European Food Research and Technology, 225(3-4), 589-592. Pripp, A. H., Isaksson, T., Stepaniak, L., & Sørhaug, T. (2004). Quantitative structure- activity relationship modelling of ACE-inhibitory peptides derived from milk proteins. European Food Research and Technology, 219(6), 579-583. Pripp, A. H., Isaksson, T., Stepaniak, L., Sørhaug, T., & Ardö, Y. (2005). Quantitative structure activity relationship modelling of peptides and proteins as a tool in food science. Trends in Food Science & Technology, 16(11), 484-494. Purslow, P. P. (2005). Intramuscular connective tissue and its role in meat quality. Meat Science, 70(3), 435-447. Purslow, P. P. (2014). New developments on the role of intramuscular connective tissue in meat toughness. Annual Review of Food Science and Technology, 5, 133-153. Quirós, A., Dávalos, A., Lasunción, M. A., Ramos, M., & Recio, I. (2008). Bioavailability of the antihypertensive peptide LHLPLP: Transepithelial flux of HLPLP. International Dairy Journal, 18(3), 279-286. Raghavan, S. and Kristinsson, H. G. (2009). ACE-inhibitory activity of tilapia protein hydrolysates. Food Chemistry, 117(4), 582-588. Rajendran, C. K. S. R., Mason, B., & Udenigwe, C. C. (2016). Peptidomics of peptic digest of selected potato tuber proteins: post-translational modifications and limited cleavage specificity. Journal of Agricultural and Food Chemistry, 64(11), 2432-2437. Regazzo, D., Molle, D., Gabai, G., Tome, D., Dupont, D., Leonil, J., & Boutrou, R. (2010). The (193–209) 17‐residues peptide of bovine β‐casein is transported through Caco‐2 monolayer. Molecular Nutrition & Food Research, 54(10), 1428-1435. Ricard-Blum, S. (2011). The collagen family. Cold Spring Harbor Perspectives in Biology, 3(1), a004978. Rowe, R. W. D. (1986). Elastin in bovine Semitendinosus and Longissimus dorsi muscles. Meat Science, 17(4), 293-312. Ryan, J. T., Ross, R. P., Bolton, D., Fitzgerald, G. F., & Stanton, C. (2011). Bioactive peptides from muscle sources: meat and fish. Nutrients, 3(9), 765-791. Saiga, A., Iwai, K., Hayakawa, T., Takahata, Y., Kitamura, S., Nishimura, T., & Morimatsu, F. (2008). Angiotensin I-converting enzyme-inhibitory peptides obtained from chicken collagen hydrolysate. Journal of Agricultural and Food Chemistry, 56(20), 9586-9591. Samaranayaka, A.G.P. and Li-Chan, E.C.Y. (2011). Food-derived peptidic antioxidants: A review of their production, assessment, and potential applications. Journal of Functional Foods, 3, 229–254. Sasaki, K., Takahashi, N., Satoh, M., Yamasaki, M., & Minamino, N. (2010). A peptidomics strategy for discovering endogenous bioactive peptides. Journal of Proteome Research, 9(10), 5047-5052. Satake, M., Enjoh, M., Nakamura, Y., Takano, T., Kawamura, Y., Arai, S., & Shimizu, M. (2002). Transepithelial transport of the bioactive tripeptide, Val-Pro-Pro, in human intestinal Caco-2 cell monolayers. Bioscience, Biotechnology, and Biochemistry, 66(2), 378-384. Schaafsma, G. (2009). Safety of protein hydrolysates, fractions thereof and bioactive peptides in human nutrition. European Journal of Clinical Nutrition, 63(10), 1161-1168.

36

Schrieber, R., & Gareis, H. (2007). Gelatine handbook: theory and industrial practice. John Wiley & Sons. Segura-Campos, M., Chel-Guerrero, L., Betancur-Ancona, D., & Hernandez-Escalante, V. M. (2011). Bioavailability of bioactive peptides. Food Reviews International, 27(3), 213-226. Sentandreu, M. A., Coulis, G., & Ouali, A. (2002). Role of muscle endopeptidases and their inhibitors in meat tenderness. Trends in Food Science & Technology, 13(12), 400-421. Shahidi, F., & Zhong, Y. (2008). Bioactive peptides. Journal of AOAC International, 91(4), 914-931. Shahidi, F., & Zhong, Y. (2015). Measurement of antioxidant activity. Journal of Functional Foods, 18, 757-781. Shanmugam, V. P., Kapila, S., Sonfack, T. K., & Kapila, R. (2015). Antioxidative peptide derived from enzymatic digestion of buffalo casein. International Dairy Journal, 42, 1-5. Shimizu, M. (2004). Food‐derived peptides and intestinal functions. Biofactors, 21(1‐4), 43-47. Shimizu, M., Tsunogai, M., & Arai, S. (1997). Transepithelial transport of oligopeptides in the human intestinal cell, Caco-2. Peptides, 18(5), 681-687. Skeggs, L. T., Kahn, J. K. & Shumway, N. P. (1956) The preparation and function of the angiotensin-converting enzyme. The Journal of Experimental Medicine, 103, 295-299. Stanton, C. and Light, N. (1990). The effects of conditioning on meat collagen: Part 3— Evidence for proteolytic damage to endomysial collagen after conditioning. Meat Science, 27(1), 41-54. Sullivan, G. A. and Calkins, C. R. (2010). Application of exogenous enzymes to beef muscle of high and low-connective tissue. Meat Science, 85(4), 730-734. Sylvestre, M. N., Balcerzak, D., Feidt, C., Baracos, V. E., & Bellut, J. B. (2002). Elevated rate of collagen solubilization and postmortem degradation in muscles of lambs with high growth rates: Possible relationship with activity of matrix metalloproteinases. Journal of Animal Science, 80(7), 1871-1878. Tang, X., He, Z., Dai, Y., Xiong, Y. L., Xie, M., & Chen, J. (2009). Peptide fractionation and free radical scavenging activity of zein hydrolysate. Journal of Agricultural and Food Chemistry, 58(1), 587-593. Tavares, T., del Mar Contreras, M., Amorim, M., Pintado, M., Recio, I., & Malcata, F. X. (2011). Novel whey-derived peptides with inhibitory effect against angiotensin-converting enzyme: in vitro effect and stability to gastrointestinal enzymes. Peptides, 32(5), 1013-1019. Toldrá, F., Aristoy, M. C., Mora, L., & Reig, M. (2012). Innovations in value-addition of edible meat by-products. Meat Science, 92(3), 290-296. Udenigwe, C. C. (2014). Bioinformatics approaches, prospects and challenges of food bioactive peptide research. Trends in Food Science & Technology, 36(2), 137-143. Udenigwe, C. C., & Aluko, R. E. (2012). Food protein‐derived bioactive peptides: production, processing, and potential health benefits. Journal of Food Science, 77(1), R11- R24. Valko, M., Leibfritz, D., Moncol, J., Cronin, M. T. D., Mazur, M., & Telser, J. (2007). Free radicals and antioxidants in normal physiological functions and human disease. International Journal of Biochemistry & Cell Biology, 39(1): 44-84.

37

Valko, M., Rhodes, C. J., Moncol, J., Izakovic, M., and Mazur, M. (2006). Free radicals, metals and antioxidants in oxidative stress-induced cancer. Chemico-Biological Interactions, 160(1), 1–40. Verbeke, W., Pérez-Cueto, F. J., de Barcellos, M. D., Krystallis, A., & Grunert, K. G. (2010). European citizen and consumer attitudes and preferences regarding beef and pork. Meat Science, 84(2), 284-292. Visse, R. and Nagase, H. (2003). Matrix metalloproteinases and tissue inhibitors of metalloproteinases - Structure, function, and biochemistry. Circulation Research, 92(8), 827-839. Wang, G. T., Chung, C. C., Holzman, T. F., & Krafft, G. A. (1993). A continuous fluorescence assay of renin activity. Analytical Biochemistry, 210(2), 351-359. Weston, A. R., Rogers, R. W., & Althen, T. G. (2002). Review: The role of collagen in meat tenderness. The Professional Animal Scientist, 18(2), 107-111. World Health Organization (2015). http://www.who.int/mediacentre/factsheets/fs317/en/ Wu, J., Aluko, R. E., & Nakai, S. (2006). Structural requirements of angiotensin I- converting enzyme inhibitory peptides: quantitative structure-activity relationship study of di-and tripeptides. Journal of Agricultural and Food Chemistry, 54(3), 732-738. Wu, J., & Ding, X. (2001). Hypotensive and physiological effect of angiotensin converting enzyme inhibitory peptides derived from soy protein on spontaneously hypertensive rats. Journal of Agriculture and Food Chemistry, 49(1), 501-506. Wu, W., Fu, Y., Therkildsen, M., Li, X. M., & Dai, R. T. (2015). Molecular understanding of meat quality through application of proteomics. Food Reviews International, 31(1), 13-28. Xiong, G. Y., Zhang, L. L., Zhang, W., & Wu, J. (2012). Influence of ultrasound and proteolytic enzyme inhibitors on muscle degradation, tenderness, and cooking loss of hens during aging. Czech Journal of Food Sciences, 30(3), 195-205. Xu, J., Wei, L., Mathvink, R., He, J., Park, Y.-J., He, H., Leiting, B., Lyons, K. A., Marsilio, F., Patel, R. A., Wu, J. K., Thornberry, N. A., & Weber, A. E. (2005). Discovery of potent and selective based dipeptidyl peptidase IV inhibitors. Bioorganic & Medicinal Chemistry Letters, 15(10), 2533-2536. Yang, J., Ho, H., Chu, Y., & Chow, C. (2008). Characteristic and antioxidant activity of retorted gelatin hydrolysates from cobia (Rachycentron canadum) skin. Food Chemistry, 110(1), 128-136. Zhang, Y., Olsen, K., Grossi, A., & Otte, J. (2013). Effect of pretreatment on enzymatic hydrolysis of bovine collagen and formation of ACE-inhibitory peptides. Food Chemistry, 141(3), 2343-2354.

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8 Papers

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Paper I

Fu, Y., Young, J. F., Dalsgaard, T. K., & Therkildsen, M. (2015). Separation of angiotensin I- converting enzyme inhibitory peptides from bovine connective tissue and their stability towards temperature, pH and digestive enzymes. International Journal of Food Science & Technology, 50(5), 1234-1243.

1234 International Journal of Food Science and Technology 2015, 50, 1234–1243

Original article Separation of angiotensin I-converting enzyme inhibitory peptides from bovine connective tissue and their stability towards temperature, pH and digestive enzymes

Yu Fu, Jette F. Young, Trine K. Dalsgaard & Margrethe Therkildsen*

Department of Food Science, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark (Received 8 October 2014; Accepted in revised form 19 January 2015)

Summary Bovine collagen was isolated from connective tissue, a by-product in the meat processing industry and characterised by SDS-PAGE. Alcalase and papain were employed to generate collagen hydrolysates with different degree of hydrolysis (DH). In vitro angiotensin I-converting enzyme (ACE) inhibitory activities were evaluated and the two most potent hydrolysates from each enzyme were separated by two-step puri- fication. Both alcalase-catalysed and papain-catalysed hydrolysates exhibited strong ACE inhibitory À1 capacities with IC50 values of 0.17 and 0.35 mg mL , respectively. Purification by ion-exchange chroma- tography and gel filtration chromatography revealed higher ACE inhibitory activities in one fraction from À1 each enzyme with IC50 values of 3.95 and 7.29 lgmL . These peptide fractions were characterised as 6-12 amino acid residues by MALDI-TOF/MS. The peptides retained their activity (>90%) after exposure to processing temperature and pH and in vitro simulated gastrointestinal digestion. The present results demonstrated that collagen peptides can be utilised for developing high value-added ingredients, for exam- ple ACE inhibitory peptides.

Keywords ACE inhibitory peptides, by-product, collagen hydrolysates, purification, stability.

Some in vivo studies on spontaneously hypertensive Introduction rats or preclinical experiments have suggested that Hypertension, as a global risk factor in cardiovascular these biopeptides can reduce blood pressure remark- diseases, is deemed the world’s largest killer and an ably, by either oral or endovenous administration estimated one billion people around the world will be without exerting side effects on normotensive subjects diagnosed by 2025 (Ahhmed & Muguruma, 2010). (Fernandez-Musoles et al., 2013). Moreover, the clini- Angiotensin I-converting enzyme (ACE, EC 3.4.15.1) cal data demonstrated that oral administration of tri- plays a crucial role in the renin–angiotensin system as peptides (e.g. IPP and VPP) can significantly reduce it catalyses the transition of decapeptide (angiotensin systolic blood pressure (SBP) and diastolic blood pres- I) to vasoconstrictive octapeptide (angiotensin II) sure (DBP) (Martınez-Maqueda et al., 2012). In recent which is a key regulatory peptide in vasodilation. ACE years, collagen or gelatine has been reported as good also inactivates the vasodilatory bradykinin in the kal- sources of ACE inhibitory peptides after enzymatic likrein–kinin system, a peptide that helps reduce the digestions. These potent ACE inhibitory peptides have blood pressure (Skeggs et al., 1956). Therefore, effec- been obtained not only from land-based sources such tive inhibition of the ACE enzyme has been regarded as porcine skin collagen (Ichimura et al., 2009), bovine as a therapeutic approach of hypertension (Ahhmed & skin gelatine (Kim et al., 2001) and chicken collagen Muguruma, 2010). Compared with some synthesised (Onuh et al., 2013), but also from marine sources such drugs, such as captopril, enalapril, lisinopril and ala- as fish skin (Byun & Kim, 2001), fish cartilage (Nagai cepril for counteracting hypertension, the ACE inhibi- et al., 2006) and squid tunic (Aleman et al., 2011). tory peptides from food-derived proteins are The meat processing industry produces considerable considered to be safer and milder (Webb et al., 2010). quantities of slaughter by-products every year. They are highly underutilised and regarded as huge wastes *Correspondent: Fax: +45-87154891; and potential pollution to the environment (Mokrejs e-mail: [email protected] et al., 2009). Connective tissue, accounting for 9–12%

doi:10.1111/ijfs.12771 © 2015 Institute of Food Science and Technology ACE inhibitory peptides and stability Y. Fu et al. 1235 of meat by weight, is a major constituent of meat adjusted to pH 2.5 by 2 mol LÀ1 HCl. The extraction slaughter and processing by-products (Jayathilakan process was initiated by addition of 300 mg pepsin et al., 2012). Hence, connective tissue, which is abun- (enzyme: collagen ratio 1:400) at 20 °C for 3 days with dant of collagen, is a potential starting material for continuous stirring. Viscous solubilised collagen was development of high value-added ingredients with bio- filtered through cheesecloth; the pH of the filtrate was functional and health promoting effect, for example adjusted to 10.0 using 2 mol LÀ1 NaOH and allowed collagen peptides displaying various bioactivities, such to stand for 24 h at 40 °C to inactivate the pepsin. as antioxidant capacity (Zhuang et al., 2012; Fu & Thereafter, the pH of the filtrate was adjusted to 7.0, Zhao, 2014), bone health promoting effect (Fu & and the collagen precipitate was obtained by centrifu- Zhao, 2013) or opioid capacity (Toldra et al., 2012). gation at 5000 g at 4 °C for 30 min and washed with Although ACE inhibitory peptides have been prepared distilled water. Finally, the extracted pepsin-soluble from different meat sources by utilisation of various collagen was lyophilised for further use. proteases (Di Bernardini et al., 2012; Escudero et al., 2012), to date there have been few studies carried out Sodium dodecyl sulphate polyacrylamide gel on the collagen peptides separated from bovine con- electrophoresis nective tissue. Furthermore, the resistance of ACE inhibitory peptides against food processing conditions Sodium dodecyl sulphate polyacrylamide gel electro- (temperature and pH) and gastrointestinal digestion is phoresis (SDS-PAGE) was performed according to the crucial before they are exploited and exert potential method of Laemmli (1970). Collagen sample (20 lL) antihypertensive function in the form of active was mixed with Laemmli buffer at the ratio of 1:1 (v/ sequence (Escudero et al., 2014). Therefore, the objec- v) in the presence of 10% dithioerythritol, followed by tive of this study is to explore in vitro ACE inhibitory boiling for 5 min. After cooling, the solution was cen- activity of collagen peptides from bovine connective trifuged at 8500 g for 5 min. Afterwards, the samples tissue. In addition, the thermal, pH and in vitro diges- were loaded onto a 12% Criterion XT Bis-Tris Gel tive stability of collagen peptides will be evaluated. (Bio-Rad, Richmond, CA, USA) and subjected to elec- trophoresis at a constant electrical potential of 200 V. After electrophoresis, the gel was stained with 0.4% Material and methods Coomassie Brilliant Blue G-250 solution. As marker proteins, the Range Molecular Weight Protein Stan- Materials dard (10–260 kDa) (Bio-Rad) was used. The raw connective tissue was sampled from the nuchal ligament of bovine carcasses at a Danish Preparation of collagen hydrolysates Crown slaughter house (Aalborg, Denmark). Abz-Gly- p-nitro-Phe-Pro-OH (ACE substrate) was purchased The extracted collagen was dissolved in deionised fil- from Bachem (Bubendorf, Switzerland). Angiotensin tered water to a final concentration of 5% (w/w) and I-converting enzyme from rabbit lung, fluorescamine, adjusted to pH of 8.0 and 6.0 for alcalase (E/S ratio of papain (8.3 U gÀ1) and pepsin (0.25 U gÀ1) were all 100 U gÀ1) and papain (E/S ratio of 50 U gÀ1) diges- from Sigma Chemical Co. (St. Louis, MO, USA); tion at 55 and 60 °C, respectively. The protease activ- Alcalaseâ (13.7 U mLÀ1) from Millipore (Billerica, ity was determined by the method of Sigma’s MA, USA), and all other chemicals and reagents used nonspecific protease activity assay, using casein as a for the experiments were of analytical grade and com- substrate (Cupp-Enyard, 2008). Hydrolysis was initi- mercially available. ated by adding the proteases with continuous stirring. Collagen hydrolysates of 1 mL were withdrawn after 0.5, 1, 2, 3, 4, 5, 6 and 7 h, respectively. The hydroly- Extraction of collagen from connective tissue sis was terminated by heating the resulting solution to Collagen was extracted from bovine connective tissue 95 °C for 15 min. After cooling to room temperature, in accordance with the procedures described by Radhi- the degree of hydrolysis (DH) of collagen hydrolysates ka & Sehgal (1997) with slight modifications. The raw was assayed. Subsequently, the collagen hydrolysates bovine connective tissue (200 g) was submerged in were condensed, lyophilised and stored at À20 °C for 0.1 mol LÀ1 NaOH solution for 6 h, followed by 10% later analysis. butanol treatment at a solid/solvent ratio of 1:10 (w/v) for 18 h aiming to remove extraneous protein and fat, Degree of hydrolysis respectively. Afterwards, the connective tissue was cleaned in deionised (18.2 MO) filtered water The DH of collagen hydrolysates was quantified by (0.22 lm), dried and grounded by a meat grinder, then N-terminal amines with fluorescamine (Udenfriend dissolved in 1 L of deionised filtered water and et al., 1972). The method was performed according

© 2015 Institute of Food Science and Technology International Journal of Food Science and Technology 2015 1236 ACE inhibitory peptides and stability Y. Fu et al.

Petrat-Melin et al. (2015). The factor of residues Each fraction was collected, and its ACE inhibitory in the collagen is 0.028, and the average molecular capacity was investigated. weight of amino acids in collagen is 99 g molÀ1. Characterisation of ACE inhibitory peptides by Mass Measurement of ACE inhibitory activity Spectrometry Angiotensin I-converting enzyme inhibitory activity The Ultraflex matrix-assisted laser desorption and was determined according to the approach of Sentand- ionisation time-of-flight mass spectrometer (MALDI- reu & Toldra (2006). ACE working solution was TOF/MS) (Bruker Daltonics, Bremen, Germany) was diluted with 0.05 mol LÀ1 borate buffer (pH 8.3, employed for characterising the most potent peptide 1molLÀ1 NaCl) containing 7.1 U mLÀ1 of enzyme in fractions (A2-C and P2-B). The collagen peptides were the final reaction solution. ACE working solution desalted using ZipTip C18 (Millipore) by 50 lL, 50% (50 lL) was added to each well of a 96-well microplate acetonitrile containing 0.1% TFA and the resulting and adjusted to 100 lL by adding deionised water to peptides were co-crystallised with the a-cyano-4- controls or samples in the inhibitory assay. The hydroxycinnamic acid (matrix). The m/z ratio of the enzyme reaction was started by the addition of mass spectrometer was calibrated by use of a standard À1 200 lL, 0.45 mmol L Abz-Gly-Phe(NO2)-Pro dis- peptide mixture. solved in 150 mmol LÀ1 Tris-base buffer (pH 8.3), containing 1 mol LÀ1 NaCl, immediately mixed and Evaluation of collagen peptides’ stability incubated at 37 °C. The generated fluorescence was measured every minute for 30 min using a multiscan To investigate the influence of different temperatures BioTek Gen 5 microplate reader. Excitation and emis- on the stability of collagen-derived ACE inhibitory sion wavelengths were fixed at 355 and 405 nm, peptides, the collagen peptide solutions (A2-C and respectively. In addition, the IC50 values of collagen P2-B) were incubated for 2 h at 20, 40, 60, 80 and hydrolysates were estimated by nonlinear regression- 100 °C, respectively. The impacts of pH on the stabil- global curve fitting. ity of collagen-derived ACE inhibitory peptides were also explored. The collagen peptide solutions were incubated at pH 2.0, 4.0, 6.0, 8.0 and 10.0, respectively Fractionation of ACE inhibitory peptides (40 °C, 2 h). The resulting ACE inhibitory activity was The alcalase-treated and papain-treated hydrolysates measured as described above. showing the highest ACE inhibitory activity were sub- The in vitro digestive stability of collagen peptides jected to a 2-step purification process by the AKTA€ was determined as per the method of Hwang (2010). Fast Protein Liquid Chromatography System (FPLC) Collagen peptide solution in KCl–HCl buffer (Uppsala, Sweden). The hydrolysates were fractionated (0.1 mol LÀ1, pH 2.0) was digested with pepsin using an anionic exchange column (HiScreen Capto Q; (20 lgmLÀ1) for 2 h at 37 °C, and the reaction was GE Healthcare, Sweden) at the flow rate of terminated in a 100 °C water bath for 15 min. After 1.0 mL minÀ1, previously equilibrated by deionised fil- neutralisation to pH 7.0 with the addition of a tered water. Peptides were eluted with a linear gradient 2 mol LÀ1 NaOH solution, 1 mL of the neutralised of NaCl (0–0.75 mol LÀ1) in Tris-HCl buffer suspension was centrifuged (12 000 g, 30 min), and (50 mmol LÀ1, pH 8.2) at a flow rate of 1 mL minÀ1 the supernatant analysed for ACE inhibitory activity. and elution fractions (1.5 mL) were detected at The remaining neutralised solution was further 215 nm. The pooled fractions corresponding to each digested by 2% (w/w) pancreatin at 37 °C for 4 h. peak were then desalted by HiTrap Desalting Column The enzyme was inactivated by boiling for 15 min fol- (GE Healthcare), concentrated and lyophilised. The lowed by centrifugation at 12 000 g for 30 min. There- ACE inhibitory activity of each fraction was mea- after, ACE inhibitory activity of the supernatant was sured, and the most potent fractions were further sepa- measured. rated onto a gel filtration column (Superdex Peptide 10/300 GL; GE Healthcare). The column was equili- Statistical analysis brated with deionised filtered water and eluted with phosphate-buffered saline (pH 7.0) at a flow rate of Data were reported as means Æ standard deviations 0.5 mL minÀ1. The absorbance of eluent was fixed at from at least three independent measurements. Differ- 215 nm. Vitamin B12 (Mr = 1355 Da) and gly–gly ences between the mean values of triplicate groups (Mr = 132 Da) were used as standard protein to indi- were analysed by one-way analysis of variance (ANO- cate the approximate molecular weight of peptides. VA). Statistical significance was considered at P < 0.05

International Journal of Food Science and Technology 2015 © 2015 Institute of Food Science and Technology ACE inhibitory peptides and stability Y. Fu et al. 1237

with Duncan’s procedure of the SPSS version 20.0 (a) program (SPSS Inc., Chicago, IL, USA).

Results and discussion

Characterisation of collagen A representative electrophoretic pattern of pepsin-solu- ble collagen under reducing conditions is displayed in Figure S1. Overall, the pepsin-soluble collagen extracted from bovine connective tissue partially consti- tuted two a-chains (a1, a2) at a molecular weight of approximately 140 kDa. b-chain and c-chain were observed at a higher molecular weight, suggesting highly cross-linked components. The collagen profile constituted a typical profile of bovine type I collagen. (b) Meanwhile, amino acid analysis also demonstrated that the extracted collagen displayed similar amino acid composition to the type I collagen (data not shown). Interestingly, an additional band was discerned near the molecular weight of 40 kDa, possibly due to partial degradation of high molecular weight components by pepsin during the extraction process. Our extracted col- lagen was in agreement with those from beef tendon as a heterodimer of two identical a-chains (Ha et al., 2012). Similarly, type I collagen derived from fish spe- cies also revealed analogous patterns, most of which were comprised of two different a chains, a1 and a2 (Kittiphattanabawon et al., 2005).

Figure 1 Angiotensin I-converting enzyme (ACE) inhibitory activity Collagen hydrolysates and their ACE inhibitory activity and degree of hydrolysis (DH) of collagen hydrolysates obtained at Alcalase and papain were designated to hydrolyse col- different hydrolysis times. (a) Alcalase-catalysed hydrolysates. (b) lagen, and the hydrolysis was performed at the optimal Papain-catalysed hydrolysates. Bars: ACE inhibition rate, points: DH, n = 3. Different lowercase letters show significantly different conditions of each enzyme. The relationship between means (P < 0.05) for ACE inhibition rate, and capital letters indicate the DH and ACE inhibitory capacity of alcalase-trea- difference in DH tested by one-way ANOVA analysis. ted and papain-treated collagen hydrolysates is shown in Fig. 1a,b, respectively. In the alcalase-catalysed group, the DH of collagen hydrolysates remarkably due to its broad cleavage preference (the carboxyl ter- increased from 0 to 50.0% (P < 0.05) in the first 4 h, mini of Glu, Met, Leu, Tyr and Lys in the peptide followed by a slight increase to 52.3% after 7-h hydro- linkages), leading to a higher degree of hydrolysis in a lysis. In papain-catalysed group, there was a significant shorter hydrolysis time (Gomez-Guill en et al., 2011). rise of DH in the first 3 h from 0 to 34.6% (P < 0.05). On the other hand, papain, known as the pro- Subsequently, the DH was elevated to approximately teinase, is a -derived protease, which 36.6% at 7 h. A rapid hydrolysis in the initial period preferably cleaves the carboxyl end of or implies a large amount of peptide bonds available for lysine. It can hydrolyse meat proteins, and indiscrimi- hydrolysis by enzymes while the declining rate of nately break down connective tissue and myofibrillar hydrolysis may be due to a drop in the available proteins to obtain the highest rate of tenderisation hydrolysis sites or enzyme autodigestion (Khantaphant (Ashie et al., 2002). Therefore, the difference in the et al., 2011; Senphan & Benjakul, 2014). DH between samples digested with alcalase and Degree of hydrolysis, as an index for the breakup of papain, respectively, could be attributed to different peptide bonds, could be applied for evaluation of the specificity of the enzyme (Bougatef et al., 2009). extent of hydrolysis in protein hydrolysates (Zhao The ACE inhibitory activity of alcalase-treated et al., 2014). Alcalase, as a commercial enzyme of hydrolysates ranged from 49.1 to 59.8% at the concen- microbial origin, has been widely used in a number of tration of 0.2 mg mLÀ1 with a maximal inhibitory rate investigations with regard to hydrolysis of collagen after 4-h hydrolysis, followed by an insignificant decline

© 2015 Institute of Food Science and Technology International Journal of Food Science and Technology 2015 1238 ACE inhibitory peptides and stability Y. Fu et al.

(P > 0.05) (Fig. 1b). The impaired activity at longer (a) hydrolysis time could be due to further hydrolysis of potent ACE inhibitory peptides obtained at 4-h hydrolysis. Papain treatment rendered ACE inhibitory activities from 24.2 to 33.9% at a similar concentration to alcalase hydrolysis, and the maximal inhibitory rate was reached after 3-h hydrolysis. No ACE inhibitory capacity was ascertained in the nonhydrolysed collagen (0 h, data not shown), namely collagen without addi- tion of enzymes, suggesting the necessity of utilising exogenous enzymes is an approach to produce bioac- tive peptides. Furthermore, collagen hydrolysates or peptides produced by alcalase tend to exhibit strong ACE inhibitory capacity (Aleman et al., 2011; Zhuang et al., 2012) supported by our current result where alcalase-treated hydrolysates display higher activities than their counterparts of papain. It is thus reasonable (b) to assume that the different activities of ACE inhibition between the collagen hydrolysates could be attributed to distinct peptide fractions generated by selective cleavages of the two proteases.

Fractionation and characterisation of collagen peptides A combination of ion-exchange and gel filtration chro- matography was used to separate the potent ACE inhibitory peptides, based on the charge (affinity) and molecular weight of collagen peptides. The most pow- erful collagen hydrolysates (alcalase hydrolysate after 4 h and papain-hydrolysate after 3 h digestion) were separated into three fractions by FPLC. The fractions with different elution times were pooled to obtain the A1, A2 and A3 of the alkaline hydrrolysate and P1, P2 and P3 from the papain-hydrolysate (Fig. 2a,b). Figure 2 Anionic exchange chromatogram of alcalase-catalysed Each fraction was collected, lyophilised and its ACE hydrolysates (a) and papain-catalysed hydrolysate (b), and their cor- responding ACE inhibitory activity of pooled fractions inhibitory activity was determined. In general, all À1 fractions of both collagen hydrolysates displayed (0.2 mg mL protein). Different letters indicate significantly differ- ent values (P < 0.05) by one-way ANOVA analysis. A1, A2 and A3 ACE inhibitory capacity at the concentration of À1 Æ represent the different fractions of a 4 h alcalase-hydrolysed sample, 0.02 mg mL protein, while fraction P2 (27.7 while P1, P2 and P3 represent the different fractions of a 3 h 1.7%) and A2 (39.7 Æ 1.6%) exhibited the highest papain-hydrolysed sample. inhibitory rate in each hydrolysate. The IC50 (the con- centration of peptide fraction required to reduce the ACE activity by 50%) for the most potent collagen According to the results in Fig. 3a,b, the approximate hydrolysates and peptide fractions are shown in molecular weight distribution of A2-C was between Table 1. Compared with unseparated collagen hydro- 132–1355 Da while the portion of P2-B was above lysates, a pronounced enhancement in ACE inhibitory 1355 Da. Among different fractions, A2-C fraction capacity was obtained. The IC50 values of A2 and P2 and P2-B fraction from each enzyme displayed the À1 fractions were 16.42 and 34.39 lgmL , respectively. most potent ACE inhibitory capacity with the IC50 Consequently, A2 and P2 were selected for further value of 3.85 and 7.29 lgmLÀ1, respectively (Table 1). purification by gel filtration chromatography by mean It is apparent that the ACE inhibitory activity of colla- of Superdex peptide 10/300 GL (separation range, gen peptide fractions was remarkably elevated through 100–7000 Da). The eluted fractions of A2 and P2 are consecutive purification steps. shown in Fig. 3a,b. The A2 fraction was purified With the objective of characterising the most into four subfractions, namely A2-A, A2-B, A2-C and active collagen peptides, the A2-C and P2-B frac- A2-D, while three subfractions isolated from P2 were tions were analysed by MALDI-TOF/MS (Figure labelled as P2-A, P2-B and P2-C, respectively. S2A,B). It is obvious that the peptide distribution

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Table 1 IC50 of the ACE inhibitory activity of collagen hydrolysates and peptide fractions

Alcalase Papain

Sample ACH4* A2 A2-C PCH3† P2 P2-B

À1 IC50 (lgmL ) 170.10 Æ 32.50 16.42 Æ 0.17 3.95 Æ 0.14 352.40 Æ 64.87 34.39 Æ 0.12 7.29 Æ 0.09

*ACH4 denotes alcalase-treated collagen hydrolysates after 4-h hydrolysis. †PCH3 denotes papain-treated collagen hydrolysates after 3-h hydrolysis.

(a) (b)

Figure 3 Elution chromatograms of the A2 fraction (a) and P2 fraction (b) (obtained by anion exchange separation) gel filtration chromatog- raphy using Superdex peptide 10/300 GL column and their corresponding ACE inhibitory activity of pooled fractions (0.02 mg mLÀ1 protein). Different letters indicate significantly different values (P < 0.05) by one-way ANOVA analysis. A2-A, A2-B, A2-C and A2-D represent different fractions of the A2 fraction, while P2-A, P2-B and P2-C represent different fractions of P2. of P2-B was remarkably wider than that of A2-C, In recent years, a considerable number of studies containing several major peptides with higher molec- have indicated ACE inhibitory peptides derived ular mass compared to that of the A2-C fraction from muscle proteins. Arihara et al. (2001) where no significant peaks were observed over firstly reported ACE inhibitory peptides from porcine 1200 Da. Moreover, there are much smaller peptides skeletal muscle proteins, where the two most potent in the A2-C fraction than the counterpart in P2-B. inhibitory peptides were purified, named as myopenta- As the average amino acid molecular weight for peptides (Met-Asn-Pro-Pro-Lys and Ile-Thr-Thr-Asn- À1 bovine collagen is 99 g mol (Schrieber & Gareis, Pro) with ACE-inhibiting IC50 of 945.5 and 549.0 lM, 2007), the potent collagen peptide fractions were respectively. A follow-up investigation revealed that characterised to contain 6-12 amino acid residues potent ACE inhibitory peptides derived from beef hy- per molecule. drolysates (Val-Leu-Ala-Gln-Tyr-Lys) with IC50 value

© 2015 Institute of Food Science and Technology International Journal of Food Science and Technology 2015 1240 ACE inhibitory peptides and stability Y. Fu et al.

l À1 of 23.1 gmL (Jang & Lee, 2005). Similarly, several (a) peptides with ACE inhibitory capacity were found in chicken leg meat (Terashima et al., 2010) and collagen hydrolysates (Saiga et al., 2008). More recently, Escudero et al. (2012) demonstrated ACE inhibitory and antihypertensive activity of peptide fractions extracted from Spanish dry-cured ham, while Di Ber- nardini et al. (2012) proved the ACE inhibitory activ- ity of bovine brisket sarcoplasmic peptide fractions generated by papain. Although collagens derived from other sources are reported to exhibit ACE inhibitory activity, bovine connective tissue, as a by-product in the meat processing industry, is rarely utilised as a starting material for production of ACE inhibitory peptides. IC50 for ACE inhibitory activity of collagen peptides was 1.165 mg mLÀ1 from Atlantic salmon skin (Gu et al., 2011), 0.0142 mg mLÀ1 from sea (b) cucumber collagen (Zhao et al., 2007), 42 lM from chicken collagen (Saiga et al., 2008), 47.78 lM from squid skin (Aleman et al., 2013) and 43 lM from jelly- fish (Zhuang et al., 2012). Nevertheless, some of the reported IC50 values of collagen peptides derived from different sources are lower than that of the fractions in the present study (3.95 lgmLÀ1). The molecular weight of the peptide plays a pivotal role in production of protein hydrolysates with expected biofunctional activity. Low molecular weight peptides tended to possess higher ACE inhibitory capacity (Qian et al., 2008). Furthermore, Gomez-Gu- illen et al. (2011) revealed that the penta, hexa- and deca-peptides derived from gelatine hydrolysates exhi- bit ACE inhibitory activity. Our collagen peptides catalysed by alcalase and papain confer different pep- tide profiles with different molecular weights. There Figure 4 Stability of ACE inhibitory collagen fractions against (a) are more peptides in P2-B fraction with molecular temperature and (b) pH. weight over m/z 1200, while the peptides in A2-C are all below m/z 1200. Alcalase has broader enzyme cleavage sites than papain, cleaving at the carboxyl temperatures (20–100 °C) on the ACE inhibitory activ- side of Glu, Met, Leu, Tyr and Lys in the peptide link- ity of collagen are displayed in Fig. 4a. The two pep- ages (Fu & Zhao, 2013). This was confirmed by the tide fractions retained ACE inhibitory capacity at the higher number of smaller peptides in the A2-C fraction relatively lower temperatures (20–60 °C) with a slight compared to that of the P2-B fraction. In our current reduction in activity after heating for 2 h at 100 °Cin study, collagen peptides with potent ACE inhibitory both A2-C and P2-B peptide fractions. However, there activities were fractionated through a 2-step process was only approximately 5% loss for ACE inhibitory and characterised by MALDI-TOF/MS to predomi- activities from 20 to 100 °C, suggesting that collagen- nantly constitute 6-12 amino acid residues. derived ACE inhibitory peptides possessed satisfactory stability against heat treatment. The impacts of pH values from 2.0 to 10.0 on the ACE inhibitory activity The impacts of temperature, pH and in vitro digestion on of collagen hydrolysates are shown in Fig. 4b. At the collagen peptides strong alkaline conditions (pH = 10.0), there was a sig- The ACE inhibitory peptides can be conveyed as func- nificant reduction of ACE inhibitory activities in both tional ingredients. Therefore, the stability of the ACE A2-C and P2-B fractions (P < 0.05). In the pH range inhibitory peptides towards food processing steps is from 2.0 to 6.0, the slight fluctuations of ACE inhibi- crucial. The temperature and pH are two typical indi- tory activities were observed. The ACE inhibition rates cators commonly used during final processing (Hwang, of A2-C and P2-B maintained 98.5%, 91.8%, and 2010; Escudero et al., 2014). The effects of different 97.0% or 98.5%, 92.6% and 91.7% activities at pH

International Journal of Food Science and Technology 2015 © 2015 Institute of Food Science and Technology ACE inhibitory peptides and stability Y. Fu et al. 1241

Table 2 The impacts of in vitro digestion on collagen peptide peptides retaining activity. Collagen peptides display potent ACE inhibitory activity after in vitro digestion Hydrolysis ACE inhibitory possibly due to post-translational hydroxylation of Sample Protease time (h) activity (%)* proline in collagen which has been reported to confer Control ––85.52 Æ 0.09 c higher resistance of imitated hydrolysis by gastrointest- A2-C Pepsin 2 79.13 Æ 0.09 b inal proteases (Liu et al., 2009). On the contrary, Pancreatin 4 77.37 Æ 0.53 a Quiros et al. (2009) reported that one b-casein peptide Control ––71.43 Æ 1.01 C with antihypertensive properties was totally hydrolysed P2-B Pepsin 2 68.01 Æ 0.51 B and its activity was substantially decreased after incu- Pancreatin 4 66.80 Æ 0.99 A bation with pepsin and a pancreatic extract. Therefore, *The average values with different lowercase and capital letters in the these results indicate that collagen peptides with a same column demonstrate that one-way ANOVA of the average values is satisfactory stability will be potential for further appli- significantly different (P < 0.05). cation in the food industry.

Conclusion values of 2.0, 4.0 and 6.0, respectively. In this study, collagen peptides exhibited good resistance against the Collagen hydrolysates prepared by alcalase and papain acidic and weak alkaline conditions and heat treat- exhibited capacity to inhibit ACE. Moreover, the ment. Heat treatment, an important method in food 2-step purification process increased ACE inhibitory processing, can bring about denaturation and aggrega- activities of collagen peptides in each group by tion of protein during temperature changes from 60 to approximately 50-fold. These potent ACE inhibitory 90 °C (Korhonen et al., 1998). ACE inhibitory pep- peptides were characterised to contain 6-12 amino acid tides derived from soy protein were reported to exhibit residues per molecule. The alcalase- and papain-cataly- resistance to temperature (Wu & Ding, 2002). Simi- sed collagen peptides maintained an ACE inhibitory larly, ACE inhibitory peptides derived from tuna activity (>90%) at 100 °C, pH ranging from 2 to 10 cooking juice retained high activity against different and after simulated in vitro digestion by gastrointesti- temperature treatments (from 20 to 100 °C for 2 h) nal proteases. This study elucidated that the collagen (Hwang, 2010). The present results are consistent with extracted from bovine connective tissue has potential the previous results, indicating that the collagen pep- as a high value-added food ingredient, for example tides possess excellent heat stability. Recently, Wu ACE inhibitory peptides. However, more detailed et al. (2014) reported that ACE inhibitory activities of work is needed to explore the peptide sequences bovine casein-derived peptides noticeably decreased responsible for ACE inhibitory activity and in vivo under extremely alkaline conditions. A possible reason antihypertensive potency. could be that some active peptides were further degraded into inactive fragments with strong alkaline Acknowledgments heating. Similarly in this study, ACE inhibitory activi- ties of collagen peptides significantly declined at pH The authors greatly appreciate the technical assistance 8.0 and 10.0, compared with neutral pH (P < 0.05). of Inger Østergaard, Department of Animal Science The resistance of ACE inhibitory peptides against and Hanne Søndergaard Møller and acknowledge gastrointestinal proteases is a prerequisite before the financial support by Future Food Innovation, regional exploitation for potential antihypertensive function in consortium of Central Denmark and Graduate their active sequence in vivo (Escudero et al., 2014). School of Science & Technology (GSST) at Aarhus Accordingly, ACE inhibitory peptides need to resist University. the hydrolysis by gastrointestinal enzymes and pass through the intestinal walls while remaining active. In Reference general, pepsin and pancreatin gave rise to proteolysis of A2-C and P2-B, reflected by an elevated content of Ahhmed, A.M. & Muguruma, M. (2010). A review of meat protein free amino (data not shown). There was 6.5% and hydrolysates and hypertension. Meat Science, 86, 110–118. Aleman, A., Gimenez, B., Perez-Santin, E., Gomez-Guill en, M. & 9.5% loss of the ultimate ACE inhibitory activities in Montero, P. (2011). Contribution of Leu and Hyp residues to anti- A2-C and P2-B peptide fractions, respectively. Despite oxidant and ACE-inhibitory activities of peptide sequences isolated the statistically significant decrease in activity, the from squid gelatin hydrolysate. Food Chemistry, 125, 334–341. ACE inhibitory activity of collagen peptides remained Aleman, A., Gomez-Guill en, M.C. & Montero, P. (2013). Identifica- very stable after in vitro digestion by gastric proteases tion of ace-inhibitory peptides from squid skin collagen after in vitro gastrointestinal digestion. Food Research International, 54, (shown in Table 2), suggesting that collagen peptides 790–795. can largely resist digestion through the gastrointestinal Arihara, K., Nakashima, Y., Mukai, T., Ishikawa, S. & Itoh, M. tract or they may be partially degraded into smaller (2001). Peptide inhibitors for angiotensin I-converting enzyme from

© 2015 Institute of Food Science and Technology International Journal of Food Science and Technology 2015 1242 ACE inhibitory peptides and stability Y. Fu et al.

enzymatic hydrolysates of porcine skeletal muscle proteins. Meat poultry and fish processing industries: a review. Journal of Food Science, 57, 319–324. Science and Technology-Mysore, 49, 278–293. Ashie, I., Sorensen, T. & Nielsen, P. (2002). Effects of papain and a Khantaphant, S., Benjakul, S. & Kishimura, H. (2011). Antioxida- microbial enzyme on meat proteins and beef tenderness. Journal of tive and ACE inhibitory activities of protein hydrolysates from the Food Science, 67, 2138–2142. muscle of brownstripe red snapper prepared using pyloric caeca Bougatef, A., Hajji, M., Balti, R., Lassoued, I., Triki-Ellouz, Y. & and commercial proteases. Process Biochemistry, 46, 318–327. Nasri, M. (2009). Antioxidant and free radical-scavenging activi- Kim, S.K., Byun, H.G., Park, P.J. & Shahidi, F. (2001). Angiotensin ties of smooth hound (Mustelus mustelus) muscle protein hydroly- I converting enzyme inhibitory peptides purified from bovine skin sates obtained by gastrointestinal proteases. Food Chemistry, 114, gelatin hydrolysate. Journal of Agricultural and Food Chemistry, 49, 1198–1205. 2992–2997. Byun, H.G. & Kim, S.K. (2001). Purification and characterization of Kittiphattanabawon, P., Benjakul, S., Visessanguan, W., Nagai, T. angiotensin I converting enzyme (ACE) inhibitory peptides from & Tanaka, M. (2005). Characterisation of acid-soluble collagen Alaska pollack (Theragra chalcogramma) skin. Process Biochemis- from skin and bone of bigeye snapper (Priacanthus tayenus). Food try, 36, 1155–1162. Chemistry, 89, 363–372. Cupp-Enyard, C. (2008). Sigma’s non-specific protease activity Korhonen, H., Pihlanto-Leppala,€ A., Rantamaki,€ P. & Tupasela, T. assay-casein as a substrate. Journal of Visualized Experiments, 19, (1998). Impact of processing on bioactive proteins and peptides. e899, doi: 10.3791/899. Trends in Food Science & Technology, 9, 307–319. Di Bernardini, R., Mullen, A.M., Bolton, D., Kerry, J., O’Neill, E. Laemmli, U.K. (1970). Cleavage of structural proteins during the & Hayes, M. (2012). Assessment of the angiotensin-I-converting assembly of the head of bacteriophage T4. Nature, 227, 680–685. enzyme (ACE-I) inhibitory and antioxidant activities of hydroly- Liu, C., Sugita, K., Nihei, K.I., Yoneyama, K. & Tanaka, H. (2009). sates of bovine brisket sarcoplasmic proteins produced by papain Absorption of hydroxyproline-containing peptides in vascularly and characterisation of associated bioactive peptidic fractions. perfused rat small intestine in situ. Bioscience, Biotechnology, and Meat Science, 90, 226–235. Biochemistry, 73, 1741–1747. Escudero, E., Aristoy, M.C., Nishimura, H., Arihara, K. & Toldra, Martınez-Maqueda, D., Miralles, B., Recio, I. & Hernandez-Led- F. (2012). Antihypertensive effect and antioxidant activity of pep- esma, B. (2012). Antihypertensive peptides from food proteins: a tide fractions extracted from Spanish dry-cured ham. Meat Science, review. Food & Function, 3, 350–361. 91, 306–311. Mokrejs, P., Langmaier, F., Mladek, M., Janacova, D., Kolomaznik, Escudero, E., Mora, L. & Toldra, F. (2014). Stability of ACE inhibi- K. & Vasek, V. (2009). Extraction of collagen and gelatine from tory ham peptides against heat treatment and in vitro digestion. meat industry by-products for food and non food uses. Waste Food Chemistry, 161, 305–311. Management & Research, 27,31–37. Ferna´ ndez-Musoles, R., Manzanares, P., Burguete, M.C., Alborch, Nagai, T., Nagashima, T., Abe, A. & Suzuki, N. (2006). E. & Salom, J.B. (2013). In vivo angiotensin I-converting enzyme Antioxidative activities and angiotensin I-converting enzyme inhibition by long-term intake of antihypertensive lactoferrin inhibition of extracts prepared from chum salmon (Oncorhynchus hydrolysate in spontaneously hypertensive rats. Food Research keta) cartilage and skin. International Journal of Food Properties, 9, International, 54, 627–632. 813–822. Fu, Y. & Zhao, X.H. (2013). In vitro responses of hFOB1.19 cells Onuh, J.O., Girgih, A.T., Aluko, R.E. & Aliani, M. (2013). Inhibi- towards chum salmon (Oncorhynchus keta) skin gelatin hydroly- tions of renin and angiotensin converting enzyme activities by sates in cell proliferation, cycle progression and apoptosis. Journal enzymatic chicken skin protein hydrolysates. Food Research Inter- of Functional Foods, 5, 279–288. national, 53, 260–267. Fu, Y. & Zhao, X.H. (2014). Utilization of chum salmon (Oncorhyn- Petrat-Melin, B., Andersen, P., Rasmussen, J.T., Poulsen, N.A., Lar- chus keta) skin gelatin hydrolysates to attenuate hydrogen perox- sen, L.B. & Young, J.F. (2015). In vitro digestion of purified b- ide-induced oxidative injury in rat hepatocyte BRL cell model. casein variants A1, A2, B, and I: effects on antioxidant and Journal of Aquatic Food Product Technology. doi:10.1080/ angiotensin-converting enzyme inhibitory capacity. Journal of 10498850.2013.804141. Dairy Science, 98,15–26. Gomez-Guill en, M., Gimenez, B., Lopez-Caballero, M.A. & Mon- Qian, Z.J., Jung, W.K. & Kim, S.-K. (2008). Free radical scavenging tero, M. (2011). Functional and bioactive properties of collagen activity of a novel antioxidative peptide purified from hydrolysate and gelatin from alternative sources: a review. Food Hydrocolloids, of bullfrog skin, Rana catesbeiana Shaw. Bioresource Technology, 25, 1813–1827. 99, 1690–1698. Gu, R.Z., Li, C.Y., Liu, W.Y., Yi, W.X. & Cai, M.Y. (2011). Angio- Quiros, A., Contreras, M.D.M., Ramos, M., Amigo, L. & Recio, I. tensin I-converting enzyme inhibitory activity of low-molecular- (2009). Stability to gastrointestinal enzymes and structure–activity weight peptides from Atlantic salmon (Salmo salar L.) skin. Food relationship of b-casein-peptides with antihypertensive properties. Research International, 44, 1536–1540. Peptides, 30, 1848–1853. Ha, M., Bekhit, A.E.A., Carne, A. & Hopkins, D.L. (2012). Charac- Radhika, M. & Sehgal, P.K. (1997). Studies on the desamidation of terisation of commercial papain, bromelain, actinidin and bovine collagen. Journal of Biomedical Materials Research, 35, protease preparations and their activities toward meat proteins. 497–503. Food Chemistry, 134,95–105. Saiga, A., Iwai, K., Hayakawa, T. et al. (2008). Angiotensin I-con- Hwang, J.S. (2010). Impact of processing on stability of angiotensin verting enzyme-inhibitory peptides obtained from chicken colla- I-converting enzyme (ACE) inhibitory peptides obtained from tuna gen hydrolysate. Journal of Agricultural and Food Chemistry, 56, cooking juice. Food Research International, 43, 902–906. 9586–9591. Ichimura, T., Yamanaka, A., Otsuka, T., Yamashita, E. & Maruy- Schrieber, R. & Gareis, H. (2007). Gelatine Handbook: Theory and ama, S. (2009). Antihypertensive effect of enzymatic hydrolysate of Industrial Practice. Weinhem: Wiley-VCH GmbH & Co. collagen and Gly-Pro in spontaneously hypertensive rats. Biosci- Senphan, T. & Benjakul, S. (2014). Antioxidative activities of ence, Biotechnology, and Biochemistry, 73, 2317–2319. hydrolysates from seabass skin prepared using protease from hepato- Jang, A. & Lee, M. (2005). Purification and identification of angio- pancreas of Pacific white shrimp. Journal of Functional Foods, 6, 147– tensin converting enzyme inhibitory peptides from beef hydroly- 156. sates. Meat Science, 69, 653–661. Sentandreu, M.A. & Toldra, F. (2006). A rapid, simple and sensitive Jayathilakan, K., Sultana, K., Radhakrishna, K. & Bawa, A.S. fluorescence method for the assay of angiotensin-I converting (2012). Utilization of byproducts and waste materials from meat, enzyme. Food Chemistry, 97, 546–554.

International Journal of Food Science and Technology 2015 © 2015 Institute of Food Science and Technology ACE inhibitory peptides and stability Y. Fu et al. 1243

Skeggs, L.T., Kahn, J.R. & Shumway, N.P. (1956). The preparation tide from sea cucumber gelatin hydrolysate. Process Biochemistry, and function of the hypertensin-converting enzyme. The Journal of 42, 1586–1591. Experimental Medicine, 103, 295–299. Zhao, X.H., Fu, Y. & Yue, N. (2014). In vitro cytoprotection of Terashima, M., Baba, T., Ikemoto, N., Katayama, M., Morimoto, modified casein hydrolysates by plastein reaction on rat hepatocyte T. & Matsumura, S. (2010). Novel angiotensin-converting cells. CyTA-Journal of Food, 12,40–47. enzyme (ACE) inhibitory peptides derived from boneless Zhuang, Y., Sun, L. & Li, B. (2012). Production of the angiotensin- chicken leg meat. Journal of Agricultural and Food Chemistry, I-converting enzyme (ACE)- inhibitory peptide from hydrolysates 58, 7432–7436. of jellyfish (Rhopilema esculentum) collagen. Food and Bioprocess Toldra, F., Aristoy, M., Mora, L. & Reig, M. (2012). Innovations in Technology, 5, 1622–1629. value-addition of edible meat by-products. Meat Science, 92, 290– 296. Udenfriend, S., Stein, S., Boehlen, P., Dairman, W., Leimgruber, W. Supporting Information & Weigele, M. (1972). Fluorescamine: a reagent for assay of amino acids, peptides, proteins, and primary amines in the picomole Additional Supporting Information may be found in range. Science, 178, 871–872. the online version of this article: Webb, A.J., Fischer, U., Mehta, Z. & Rothwell, P.M. (2010). Effects Figure S1. Electrophoretic profiles of bovine colla- of antihypertensive-drug class on interindividual variation in blood gen extracted from connective tissue after pepsin treat- pressure and risk of stroke: a systematic review and meta-analysis. The Lancet, 375, 906–915. ment and analysed by 12% SDS-PAGE using Wu, J. & Ding, X. (2002). Characterization of inhibition and stabil- Coomassie brilliant blue staining. ity of soy-protein-derived angiotensin I-converting enzyme inhibi- Figure S2. MALDI-TOF/MS spectra of relative tory peptides. Food Research International, 35, 367–375. intensities of masses from fractions with the most Wu, W., Yu, P.P., Zhang, F.Y., Che, H.X. & Jiang, Z.M. (2014). Stability and cytotoxicity of angiotensin-I-converting enzyme inhib- potent ACE inhibitory peptides (A: A2-C fraction) itory peptides derived from bovine casein. Journal of Zhejiang Uni- and (B: P2-B). À versity Science B, 15, 143–152. Table S1. The compositions (g kg 1) of amino acids Zhao, Y., Li, B., Liu, Z., Dong, S., Zhao, X. & Zeng, M. (2007). of collagen extracted from connective tissue. Antihypertensive effect and purification of an ACE inhibitory pep-

© 2015 Institute of Food Science and Technology International Journal of Food Science and Technology 2015

Fig. S1

1.0

0.8

0.6

0.4

) 4 0.2

0.0

intensity (10 intensity

1.0 Relative Relative 0.8

0.6

0.4

0.2

0.0 600 800 1000 1200 1400 1600 1800 2000 m/z

Fig. S2

Table S1 The compositions (g/kg) of amino acids of collagen extracted from connective tissue

Extracted Type I Amino acid collagen collagen*

Ala 101.25 102.59

Arg 91.00 89.74

Asn - 21.35

Asp 62.62 39.00

Cys - -

Glu 110.95 71.34

Gln - 36.90

Gly 253.94 251.75

His 7.76 6.90

Hyp - 137.75

Hyl - 8.84

Ile 13.57 14.58

Leu 31.94 31.80

Lys 41.22 41.35

Met 9.32 8.59

Phe 23.60 21.69

Pro 155.75 133.74

Ser 39.02 37.15

Thr 19.96 20.45

Try - -

Tyr - 8.05

Val 28.83 26.03

*The amino acid composition of type I collagen was from Schrieber & Gareis (2007). “-” denotes not determined. Paper II

Fu, Y., Young, J. F., Løkke, M. M., Lametsch, R., Aluko, R. E., & Therkildsen, M. (2016). Revalorisation of bovine collagen as a potential precursor of angiotensin I-converting enzyme (ACE) inhibitory peptides based on in silico and in vitro protein digestions. Journal of Functional Foods, 24, 196-206.

journal of functional foods 24 (2016) 196–206

Available online at www.sciencedirect.com ScienceDirect

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Revalorisation of bovine collagen as a potential precursor of angiotensin I-converting enzyme (ACE) inhibitory peptides based on in silico and in vitro protein digestions

Yu Fu a, Jette Feveile Young a, Mette Marie Løkke a, René Lametsch b, Rotimi E. Aluko c, Margrethe Therkildsen a,* a Department of Food Science, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark b Department of Food Science, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg C, Denmark c Department of Human Nutritional Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada

ARTICLE INFO ABSTRACT

Article history: In silico proteolysis using 27 proteases theoretically released numerous ACE-inhibitory pep- Received 9 December 2015 tides from collagen alpha-1(I) and alpha-2(I) sequences. Papain was the most effective protease Received in revised form 15 March to release ACE-inhibitory peptides. Two quantitative structure–activity relationship (QSAR) 2016 models for ACE-inhibitory peptides were established and employed to predict the activi- Accepted 29 March 2016 ties of in silico-derived collagen peptides. Furthermore, two promising in silico peptides (Tyr- Available online 19 April 2016 Trp and Leu-Arg-Tyr) derived from papain and bromelain digestion were synthesised and experimentally confirmed as novel ACE inhibitors. In vitro digestion of collagen by papain Keywords: generated ACE-inhibitory peptides and the most active one was identified as a pentapep- In silico analysis tide (Gly-Pro-Arg-Gly-Phe). However, Gly-Pro-Arg-Gly-Phe remained unidentified as the ACE- Collagen inhibitory peptide during the in silico digestion by papain mainly due to complete hydrolysis, ACE-inhibitory peptides which was not the case during in vitro digestion affected by external factors. Overall, the BIOPEP present study highlights bovine collagen as a promising precursor of ACE-inhibitory pep- QSAR tides by in silico and in vitro protein digestions. © 2016 Elsevier Ltd. All rights reserved.

conversion of angiotensin I to angiotensin II, a vasoconstric- 1. Introduction tive peptide that elevates blood pressure. Furthermore, ACE also can inactivate bradykinin, a vasodilator in the blood pressure- Hypertension is one of the key risk factors that contribute to reducing kallikrein–kinin system (Skeggs, Kahn, & Shumway, the pathogenesis of cardiovascular diseases. It is estimated that 1956). Therefore, effective ACE inhibition has been consid- over 500 million people will be diagnosed with hypertension ered a practical approach in reducing blood pressure (Aluko, by 2025 (Ibrahim & Damasceno, 2012). Angiotensin-I convert- 2015a; Ondetti & Cushman, 1977). Although synthetic ACE in- ing enzyme (EC 3.4.15.1, ACE), a main regulator in the renin– hibitors are effective as antihypertensive drugs, they may angiotensin system, plays a pivotal role in catalysing the provoke adverse side effects for extended administration,

* Corresponding author. Department of Food Science, Aarhus University, Blichers Allé 20, Postbox 50, 8830 Tjele, Denmark. Tel.: +45 87158007; fax: +45 87154891. E-mail address: [email protected] (M. Therkildsen). http://dx.doi.org/10.1016/j.jff.2016.03.026 1756-4646/© 2016 Elsevier Ltd. All rights reserved. journal of functional foods 24 (2016) 196–206 197

including coughing, allergic reactions, skin rashes and taste dis- occurrence of bioactive motif (Iwaniak & Dziuba, 2011; Udenigwe turbances (Alderman, 1996). This creates a drive to investigate & Howard, 2013). Therefore, the aim of this study was to in- and develop ACE inhibitors from natural sources with fewer vestigate the potential ACE-inhibitory peptides encrypted in negative side effects for hypertension management. the bovine collagen sequences using in silico analysis and in In recent years, in silico approaches, such as BLAST (Altschul vitro protein digestion. et al., 2005), the BIOPEP database (Minkiewicz, Dziuba, & Michalska, 2011) and quantitative structure–activity relation- ship (QSAR) models (Iwaniak, Minkiewicz, Darewicz, 2. Materials and methods Protasiewicz, & Mogut, 2015; Pripp, Isaksson, Stepaniak, & Sørhaug, 2004), have been successfully applied to predict and 2.1. Materials design bioactive peptides from food-derived proteins, which help circumvent several challenges of classic approaches The raw connective tissue was sampled from the nuchal liga- (Udenigwe, 2014). However, it is an undeniable fact that the pep- ment of bovine carcasses at Danish Crown slaughter house tides released by in silico digestion may not be experimentally (Aalborg, Denmark). Abz-Gly-p-nitro-Phe-Pro-OH (ACE sub- reproduced in view of the complex nature of enzyme–proteins strate) was purchased from Bachem (Bubendorf, Switzerland). interactions (Udenigwe, 2014). Several interference factors during ACE enzyme from rabbit lung and papain (8.3 U/g) were ob- hydrolysis include complex protein structures, catalytic effi- tained from Sigma Chemical Co. (St. Louis, MO, USA). The ciency of protease, temperature and pH (Gu & Wu, 2013). QSAR, chemically synthesised (purity >95%) peptides were pur- as an essential area of chemometrics, can search for informa- chased from Schafer-N ApS (Copenhagen, Denmark). All other tion relating chemical structure to biological activities and has chemicals and reagents used were of analytical grade and com- become increasingly essential in understanding various aspects mercially available. of chemical–biological interactions, e.g. bioactive peptides re- search (Pripp et al., 2004). In other words, the bioactivity can 2.2. Protein sequences of collagen be modelled as a function of molecular structures (Wold, Sjostrom, & Eriksson, 2001). BIOPEP, a robust bioinformatics tool, Two representative protein sequences of collagen alpha-1(I) can predict the release of bioactive peptides from proteins by chain and collagen alpha-2(I) chain [Bos taurus] (NCBI Acces- certain proteases or their combinations (Minkiewicz, Dziuba, sion No.: gi|77404252 and gi|8039779, respectively) were selected Iwaniak, Dziuba, & Darewicz, 2008). Moreover, compared with from the protein database of the National Center for Biotech- traditional experimental works, in silico analysis is time- nology Information (NCBI) (http://www.ncbi.nlm.nih.gov/protein) saving and more economical, which has led to a number of and applied in our current study. investigations concerning food-derived ACE inhibitors from milk proteins (Iwaniak, Minkiewicz, & Darewicz, 2014), porcine skel- etal muscle proteins (Minkiewicz et al., 2011), egg proteins 2.3. Collagen sequence alignment by BLAST analysis (Majumder & Wu, 2010), oat proteins (Cheung, Nakayama, Hsu, Samaranayaka, & Li-Chan, 2009), chickpea proteins (Chang & The collagen type I triple helix is a heterotrimer that consists Alli, 2012), ribulose bisphosphate carboxylase (RuBisCO) (Je, Cho, of two identical alpha-1 chains and one alpha-2 chain (Gelse, Gong, & Udenigwe, 2015; Udenigwe, Gong, & Wu, 2013) and Poschl, & Aigner, 2003). Therefore, homology analysis was per- cereal proteins (Cavazos & Gonzalez de Mejia, 2013). However, formed using the representative protein sequences of collagen in silico prediction tends to ignore bioactive peptides release alpha-1(I) chain and collagen alpha-2(I) chain. Two collagen se- under practical hydrolytic conditions. In addition, some in silico quences in FASTA format were aligned with each other by studies did not validate activities of the predicted bioactive pep- means of BLAST analysis (http://blast.ncbi.nlm.nih.gov/Blast.cgi). tides or performed in vitro hydrolysis. A pairwise sequence alignment was generally displayed while Edible by-products in the meat industry serve as an ap- a series of instructive data could be generated, such as per- pealing protein source for production of high value-added centage of identities, positives, gaps and scores (Altschul et al., ingredients (Damgaard, Lametsch, & Otte, 2015; Lafarga & Hayes, 2005). 2014). Bovine connective tissue, as a major constituent of meat slaughter and processing by-products, is abundant in colla- 2.4. BIOPEP analysis gen (Toldrá, Aristoy, Mora, & Reig, 2012). Collagen that contains high amounts of Gly and Pro residues can serve as a poten- 2.4.1. Assessment of ACE-inhibitory peptides from tial precursor of bioactive peptides (Gómez-Guillén, Giménez, bovine collagen López-Caballero, & Montero, 2011). Although it has been re- The assessment of potential ACE-inhibitory peptides from ported that collagen peptides derived from different sources bovine collagen was carried out using BIOPEP (version 2015 April, can display ACE-inhibitory (Saiga et al., 2008), antioxidant (Kim http://www.uwm.edu.pl/biochemia/index.php/pl/biopep)analy- et al., 2013) and dipeptidylpeptidase IV-inhibitory (Lacroix & sis (Minkiewicz et al., 2008). The protein sequences of collagen Li-Chan, 2012) activities, the systematic evaluation of bovine alpha-1(I) chain and collagen alpha-2(I) chain obtained from collagen as a potential precursor of ACE-inhibitory peptides NCBI database were analysed by the “profiles of potential bio- based on in silico proteolysis, followed by in vitro protein di- logical activity” tool. During this process, a number of potential gestion, has not been reported. Recently, in silico analysis of meat collagen-derived ACE-inhibitory peptide sequences were proteome indicated that bovine collagen was the best precur- screened and compared with peptide sequences that exhib- sor of ACE inhibitors due to the higher probability of the ited either in vitro or in vivo ACE-inhibitory capacity as reported 198 journal of functional foods 24 (2016) 196–206

in the BIOPEP database. In addition, the frequency of bioactive more complicated to interpret, as they were related to some fragments in the protein chain (A) was applied to character- properties, such as electronegativity, heat of formation, elec- ise the potential activity of protein fragments according to the trophilicity and hardness (Sandberg et al., 1998). Moreover, this equation: approach was reported to be more stable than the 3-z scale approach due to better linear combination (Sandberg et al., 1998). = A aN The ACE-inhibitory activity was expressed as IC50 values (μM), the concentration that inhibits 50% activity of ACE. Partial least where a is the number of fragments with a given activity within squares (PLS) regression analysis between amino acid descrip- the protein sequence and N is the number of amino acid resi- tors (predictors, X) and log-transformed IC50 values (dependent, dues within this protein chain (Minkiewicz et al., 2008). In Y) was carried out using SIMCA 14 (Umetrics, Umeå, Sweden). addition, higher occurrence frequency of protein sequences does All variables were auto-scaled to unit variance prior to the analy- not necessarily indicate liberation of these protein sequences, ses. The two models were validated by cross-validation using but they may act as promising precursors for ACE-inhibitory several approaches, including Full Cross (leave-one out), Sys- peptides. tematic 123 (venetian blinds), Systematic 111 (contiguous blocks) and Random (random subsets) validations during modelling. 2.4.2. In silico proteolysis of bovine collagen The number of significant PLS components was chosen based Collagen sequences were subjected to in silico proteolysis for on the RMSECV value. RMSECV is the cross validation corre- prediction of the theoretical peptide sequences cleaved by lation coefficient calculated from predicted residual sum of twenty seven different enzymes (Minkiewicz et al., 2008). Ad- squares (PRESS). In an attempt to improve the predictive ability ditionally, a combination of mixed enzymes (pepsin+trypsin of the model, the U vs. T and Residual Variance vs. T2 plots were and pepsin+trypsin+chymotrypsin A and C) was applied in order used in PLS models to exclude outliers (data not shown). The to examine proteolysis of collagen sequences by digestive outliers, displaying much worse fit than others, were ex- enzymes. Afterwards, the theoretical peptides released by a cluded (2 out of 166 samples for the dipeptide group and none variety of proteases were searched for active fragments. Even- for the tripeptide group). The lowest value of RMSECV in the tually, a list of potential di- and tripeptides with ACE-inhibitory validation was selected for further prediction of unknown ACE- activity was generated for further analysis. inhibitory activity.The optimal model regarding balance between the models’ fit and the predictive ability was selected (Siebert, 2.5. Toxic prediction of collagen peptides 2003).The regression coefficients with confidence intervals were also analysed by SIMCA. Furthermore, jack-knifing during cross The toxicity of bioactive peptides is one of the major con- validation was applied for calculation of 95% confidence in- cerns for development of peptide-based nutraceuticals. In order tervals on the scaled regression coefficients (Efron & Gong, 1983). to investigate toxicity of the collagen peptides, ToxinPred Eventually, the ACE-inhibitory activities of collagen peptides (http://www.imtech.res.in/raghava/toxinpred/), an in silico tool produced by in silico hydrolysis were further predicted by the for the prediction of toxicity of collagen peptides, was applied. optimal predictive models. Briefly, the toxic and non-toxic peptides were collected from various databases (ATDB, Arachno-Server, Conoserver, DBETH, 2.7. Separation and identification of ACE-inhibitory BTXpred, NTXpred, and SwissProt) to generate datasets (Gupta peptides derived from bovine collagen et al., 2013). In silico models were established based on the machine-learning technique support vector machine (SVM) to Based on the predicted results from in silico hydrolysis, papain differentiate toxic peptides from non-toxic peptides. In addi- (the optimal protease) was selected for in vitro hydrolysis of tion, a number of certain motifs from toxic peptides were bovine collagen to generate ACE-inhibitory peptides. The in vitro discovered and used for toxicity prediction. The threshold value protein hydrolysis and peptide separation procedures were per- (0.0) was used to separate toxic from non-toxic peptides (Gupta formed according to Fu, Young, Dalsgaard, and Therkildsen et al., 2013). (2015). The papain-catalysed collagen hydrolysates were sub- jected to a 2-step separation process, followed by further 2.6. Prediction of ACE-inhibitory activity of collagen purification by high-performance liquid chromatography (HPLC) peptides by QSAR model with a reversed-phase column (Luna 3 μmC18, 150 × 10 mm). Elution was performed with a non-linear gradient system from Two ACE-inhibitory peptide databases consisting of 166 dipep- solvent A (0.1% trifluoroacetic acid in Milli-Q water) to solvent tides and 141 tripeptides collected from previously published B (0.1% trifluoroacetic acid in 90% acetonitrile) at 2.5 mL/min works (Wu, Aluko, & Nakai, 2006) were constructed with a slight flow rate, and absorbance was monitored at 215 nm. The pu- modification. Data sets for the di- and tripeptides are pre- rified collagen peptides were screened for their ACE-inhibitory sented in the supplementary materials (Table S1A and B). The activities. Eventually, the most potent peptides with high purity peptide sequences were transformed into X-matrix by means from reversed phase-HPLC were chosen for identification by of the z-5 scale. The chemical attributes of each amino acid a Dionex 3000 RSLC UHPLC system (Thermo Fisher Scientific, reported as 5-z scales (z1, z2, z3, z4 and z5) were previously Hvidovre, Denmark) coupled with a Q Exactive mass spec- obtained according to principal component analysis (PCA) trometer (Thermo Fisher Scientific, Hvidovre, Denmark). The (Sandberg, Eriksson, Jonsson, Sjöström, & Wold, 1998). Among most active collagen peptides were suspended in 200 μL 0.1% them, z1–z3 represented hydrophobicity, side chain bulk/ TFA and 5 μL was loaded on an Aeris PEPTIDE 1.7 μm XB-C18, molecular size and electronic property, while z4 and z5 were 150 × 2.1 mm column (Phenomenex, Værløse, Denmark). The journal of functional foods 24 (2016) 196–206 199

peptides were eluted with chromatographic gradient ranging alpha-2(I), 73% positives (matched amino acids), 1% gap and from 0 to 60% solvent B (80% acetonitrile, 0.1% Formic acid) 1024 bits (out of 1678) within the total length. The “identi- for 60 min at 250 μL/min flow rate. The mass spectrometer was ties” serves as the proportion of matched amino acids in the operated in a data-dependent mode automatically switching entire length of the aligned sequences, i.e. the larger the iden- between MS and MS/MS. A survey MS scan (200–2000 m/z) was tities, the more homology; the “positives” stands for the values acquired in the Orbitrap analyser with a resolution of 70,000 of matched amino acids and the substitutions “+” within the at 400 m/z. The top seven most intense ions were selected for entire length; the “gaps” accounts for the values of “−” within MS/MS. The peptides were subjected to higher-energy colli- the entire length (Altschul et al., 2005). The bit score acts as a sional dissociation (HCD) MS/MS, and the obtained data were number calculated from the value of gaps and substitutions further analysed using de novo sequencing (PEAKS Studio 7.0, concerning every aligned sequence, i.e. the larger the scores, BSI, Waterloo, ON, Canada). the more pronounced the alignment (Altschul et al., 2005; Chang & Alli, 2012). The present BLAST analysis indicated that the pep- 2.8. Determination of ACE-inhibitory activities tides generated from collagen alpha-1(I) and collagen alpha- 2(I) chains shared similar bioactivities. ACE-inhibitory activity was determined according to the ap- proach of Petrat-Melin et al. (2015). ACE working solution 3.2. Generation of potential ACE-inhibitory peptides (7.1 U/mL) of 50 μL in 0.05 mol/L borate buffer (pH 8.3, 1 mol/L derived from collagen NaCl) was added to each well of a 96-well microplate, fol- lowed by addition of 50 μL of either sample or control (Milli-Q The frequency of ACE-inhibitory peptides in collagen alpha- water). The enzyme reaction was started by addition (200 μL) 1(I) and alpha-2(I) sequences was nearly similar (0.554 and 0.562, respectively), which was probably a consequence of the high of 0.45 mmol/L Abz-Gly-Phe(NO2)-Pro dissolved in 150 mmol/L Tris-base buffer (pH 8.3), containing 1 mol/L NaCl; reagents were homology. Also, we analysed the A values of the beta-casein immediately mixed and incubated at 37 °C. The generated fluo- [Bos taurus] (gi|162805), ovalbumin [Gallus gallus] (gi|45384056) rescence was measured every minute for 30 min using a and myosin-1 [Bos taurus] (gi|41386691) protein sequences from multiscan BioTek Gen 5 microplate reader with 355 and 405 nm the NCBI database, which were reported as good precursors excitation and emission wavelengths, respectively. The most of potent ACE-inhibitory peptides (Miguel, Aleixandre, Ramos, potent ACE-inhibitory peptide (Gly-Pro-Arg-Gly-Phe) from the & López-Fandiño, 2006; Nakashima et al., 2002; Otte, Shalaby, in vitro hydrolysis experiment and the two most potent pep- Zakora, & Nielsen, 2007). The A values from in silico analysis tides (Tyr-Trp and Leu-Arg-Tyr) from in silico prediction were were 0.437 (beta-casein), 0.2811 (ovalbumin) and 0.3137 (myosin- chemically synthesised (purity >95%) by Schafer-N ApS 1), which were lower than that of bovine collagen. The present results are in agreement with an in silico report that bovine col- (Copenhagen, Denmark). The IC50 values of collagen-derived peptides were estimated by nonlinear regression–global curve lagen was one of the best precursors of ACE inhibitors (Iwaniak fitting (Sigmaplot 11, Systat Software Inc., San Jose, CA, USA). & Dziuba, 2011; Minkiewicz et al., 2011). The collagen alpha-1(I) and alpha-2(I) protein sequences were subjected to BIOPEP analysis, and peptides displaying ACE- 2.9. Statistical analysis inhibitory capacity were assembled for further analysis. The theoretical ACE-inhibitory peptides released by various pro- Statistical analyses were performed three times for each ex- teases are summarised and the identical or different peptide perimental item. Data were expressed as means ± standard error patterns derived from hydrolysis of collagen alpha-1(I) and from three independent measurements. Differences between alpha-2(I) sequences are displayed in Tables 1 and 2, respec- the mean values of triplicate groups were analysed by one- tively. In general, there were 784 and 781 potential ACE- way analysis of variance (ANOVA). Statistical significance was inhibitory peptides (di- and tripeptide) from collagen alpha- considered at P < 0.05 with Duncan’s procedure of the SPSS 1(I) and alpha-2(I), respectively. Papain theoretically released version 20.0 program (SPSS Inc., Chicago, IL, USA). the highest number of ACE-inhibitory peptide sequences (102) and the peptide sequences (Gly-Pro and Pro-Gly) were the most abundant, which was due to the high number of repeating units 3. Results and discussion of these amino acid residues in collagen. Thus, papain was further chosen as the optimal protease for in vitro digestion, 3.1. Suggested homology of bovine collagen as it not only released the highest number of peptides but also generated one of the most active ACE-inhibitory peptides, the BLAST analysis can be employed to compare different protein activities of which were further predicted and validated using sequences and to calculate significant regions of similarity of QSAR models and the synthetic peptides. However, the com- amino acid sequences within proteins (Lafarga, O’Connor, & bined digestion using the gastrointestinal enzymes pepsin, Hayes, 2014). Homologous proteins confer similar potential trypsin and chymotrypsin (A and C) only generated 20 pep- bioactive activity of protein fragments (Vercruysse et al., 2009). tides with ACE-inhibitory activity. This fact suggests that Therefore, homology assessment between collagen alpha-1(I) gastrointestinal enzymes cannot effectively hydrolyse colla- and alpha-2(I) sequences was performed using BLAST to clarify gen to release numerous ACE-inhibitory peptide sequences. their potential to be ACE inhibitors with similar protein se- Therefore, application of the exogenous proteases with broad quences. Based on BLAST results, there was a 65% similarity cleavage sites is indispensable to release ACE-inhibitory pep- in protein sequences of collagen alpha-1(I) and collagen tides from protein (Udenigwe & Aluko, 2012).Anumberof 200 journal of functional foods 24 (2016) 196–206

Table1–Theidentical ACE inhibitory peptides released from both collagen alpha-1(I) and collagen alpha-2(I) by various proteases. Enzymes Collagen alpha-1(I) and collagen alpha-2(I) No. of Peptide sequence peptides

Papain 102 IR(1), VWY(1), PR(10), LPG(9), IA(2), LA(1), VG(3), FG(1), DA(4), QG(4), SG(3), LG(1), TG(9), NG(4), PG(35), VR(1), QK(1), DG(10), IE(1), YW(1) Chymotrypsin C 91 RL(2), GRP(1), RY(1), GY(1), GP(52), VP(1), GM (2), GL(6), GQ(5), GE(14), SY(1), KP(1), IE(1), TQ(1), KE(1), AGSP(1) Ficin 89 RL(2), RY(2), PL(1), IA(2), RA(1), DA(5), MG(1), QG(6), SG(4), TG(4), EG(2), EA(5), PG(36), QK(1), DG(11), SY(1), EY(1), EV(1), EK(2), IEY(1) 81 RL(2), GRP(1), RY(1), GY(1), AY(2), GP(49), GEP(2), AP(1), GF(3), GI(1), GM(1), GL(1), AI(1), SY(1), SF(2), EY(2), KP(1), GEP(2), EI (1), EV(1), TF(1), AGSP (1) Proteinase K 81 RL(2), PL(1), RA(1), KG(9), DA(5), MG(1), QG(5), SG(2), TG(3), EG(1), EA(5), NG(3), PG (34), MKG(1), DG(5), KA(1), EI(1), EV(1) Bromelain 76 RY(1), LPG(9), IA(2), LA(1), RA(1), VG(2), DA(4), QG(2), SG(3), LG(4), EG(2), EA(5), NG(3), PG(24), LRY(1), QK(1), DG(8), EY(1), EK(2) Pepsin (pH > 2) 50 RL(2), GPA(6), RY(1), GY(1), IRA(1), PGL(3), PL(1), RA(1), GF(2), GA(10), GL(5), DA(1), GQ(4), GE(6), SY(1), SF(2), KA(1), IE(1), TF(1) Prolyloligopeptidase 48 GRP(1), GLP(1), GP(40), GEP(4), VP(1), AGSP(1) Leucocyte elastase 27 GPA(10), GA (14), GS(2), GV(1) Pepsin+Trysin+Chymotrypsin (A or C) 20 VF(1), GY(1), PGL(1), GF(4), GL(5), GR(1), GK(1), SY(1), SF(2), EY(1), EK(1), TF(1) Pepsin+Trypsin 16 VF(1), PGL(1), GF(4), GL(5), GR(1), SF(2), EK(1), TF(1) Glycylendopeptidase 13 LPG(9), LG(1), EG(1), PG(1), MKG(1) Metridin 11 RL(2), RY(1), GY(1), PGL(1), GF(1), SY(1), SF(2), EY(1), TF(1) Proteinase P1 (lactocepin) 8 GP(2), GA(2), GL(1), AG(1), PG(1), PP(1) Pancreatic elastase II 5 PGL(1), GF(1), SF(2), TF(1) 4 GY(1), GF(1), SF(1), EY(1) Trypsin 2 GR(1), EK(1) 2 GR(1), EK(1) Glutamylendopeptidase II 2 IE(1), ME(1) Oligopeptidase B 2 GR(1), EK(1) V-8 protease (Glutamylendopeptidase) 1 IE(1) Chymase 1 GR(1) Prolidase L. lactis s. cremoris H61 1 PGL(1)

enzymes, especially papain, chymotrypsin C and pancreatic present BIOPEP analysis indicated that bovine collagen was an elastase, generated peptides with hydrophobic amino acids (Pro, excellent precursor of ACE inhibitors due to the high occur- Leu, Ala, Tyr and Val) at the C-terminal positions. These amino rence of ACE-inhibitory pattern (Gly-X-Y), as this repeating residues have been reported to potentiate stronger ACE- pattern could act as a potent ACE inhibitor. Similarly, in silico inhibitory activity (Aluko, 2015a). approaches have been employed to search ACE-inhibitory pep- Di- and tripeptides containing Gly and Pro tend to be most tides from other protein precursors. Egg proteins (Majumder abundant in bovine collagen and are closely associated with & Wu, 2010), soybean proteins (Gu & Wu, 2013) and cereal pro- a high frequency of ACE-inhibitory peptides (Minkiewicz et al., teins (Cavazos & Gonzalez de Mejia, 2013; Udenigwe et al., 2013) 2011). The collagen sequence encompassing domains with rep- have been demonstrated as good precursors for ACE-inhibitory etitions of Gly-X-Y is involved in the formation of collagen capacity based on in silico analysis. Chang and Alli (2012) re- triplex, where X tends to be proline and Y tends to be hy- ported that chickpea legumin and provicilin precursors served droxyproline or others (Fu & Zhao, 2013; Gelse et al., 2003). The as good precursors of ACE-inhibitory peptides using in silico

Table2–TheACEinhibitory peptides released from either collagen alpha-1(I) or collagen alpha-2(I) by various proteases. Enzymes Collagen alpha-1(I) Collagen alpha-2(I) No. of Peptide sequence No. of Peptide sequence peptides peptides

Thermolysin 23 IR(1), AY(1), LPG(1), AP(1), VG(3), AG(13), FG(1), 24 IR(1), IPP(1), AY(1), LPG(1), AP(1), VG(3), AG(13), LG(1), IE(1) FG(1), LG(1), IE(1) Cathepsin G 11 RL(2), RY(1), GY(1), PGL(1), GF(1), SY(1), SF(2), 10 RL(2),VF(1), PGL(1), GF(1), GL(1), SF(1), SF(1), EY(1), TF(1) EK(1), TF(1) Chymotrypsin A 10 RL(2),VF(1), PGL(1), GF(1), GL(1), SF(1), SF(1), 9 RL(1), PGL(2), AF(1), GF(1), GL(2), SF(1), EY(1) EK(1), TF(1) Pepsin (pH 1.3) 7 RL(2), PGL(1), GF(1), SF(2), TF(1) 5 PGL(1), GF(1), SF(2), TF(1) journal of functional foods 24 (2016) 196–206 201

hydrolysis. Recently, the large and small subunits of RuBisCO in the tripeptide model was crucial for a tripeptide. In addi- have been demonstrated to contain several bioactive peptide tion, amino acid residues with side chain bulk and hydrophobic sequences based on in silico analysis (Udenigwe et al., 2013). side chains, e.g. aromatic acids (Phe, Tyr and Pro), contributed Moreover, a dipeptide (Phe-Cys) derived from in silico hydro- to higher ACE inhibitory activity. lysis of RuBisCO large subunit thermolysin attenuated H2O2- It was reported that impact of the structure of ACE inhibi- induced oxidative stress in human hepatocyte cell model tors on their activity relies on the amino acid residue of (Je et al., 2015). Therefore, the above studies provided a theo- C-terminal (Aluko, 2015b). The hydrophobic amino acids (par- retical feasibility for application of in silico analysis to predict ticularly Trp, Phe, Pro and Tyr) located in the C-terminal of and release ACE-inhibitory peptides from bovine collagen. peptides tend to induce stronger inhibition towards ACE (Aluko, Toxicity, immunogenicity and stability may impede the de- 2015b; Saito, Kawato, & Imayasu, 1994). Therefore, Leu-Arg- velopment of bioactive peptides in the food industry (Gupta Tyr, Gly-Arg-Pro, Gly-Lys-Pro, Val-Trp-Tyr and Gly-Leu-Pro were et al., 2013; Lafarga & Hayes, 2014). A number of amino acid among the most potent ACE inhibitory peptides with IC50 values residues (Pro, His, Cys and Asn) are predominant in toxic pep- below 20 μM. By means of the established optimised model, tides, while Val, Thr, Arg, Gln, Met, Leu, Lys, Ile, Phe and Ala the activities of the predicted di- and tripeptides prepared by are primary components in non-toxic peptides (Gupta et al., in silico hydrolysis were predicted as shown in Table S2. The

2013). Although the currently studied collagen-derived ACE- IC50 values from collagen-derived ACE-inhibitory peptides range inhibitory peptides were abundant in Pro, ToxinPred analysis from 1.19 to 17,273.49 μM; Leu-Arg-Tyr, a tripeptide derived from revealed that all in silico-derived collagen peptides are non- bromelain digestion, had the lowest IC50 value (1.19 μM), while toxic (SVM scores < 0) and they could be potentially used as Tyr-Trp, a dipeptide, was obtained from papain hydrolysis with functional ingredients. the higher IC50 value (4.72 μM). These values were compa- rable to those of other collagen-derived ACE-inhibitory tripeptides: Gly-Pro-Leu (Byun & Kim, 2001), Gly-Pro-Val (Kim, 3.3. Prediction and validation of ACE-inhibitory activity Byun, Park, & Shahidi, 2001) and Ile-Ala-Trp (Ewart et al., 2009)

with reported IC50 values of 2.6, 4.7 and 9.5 μM, respectively. It is widely accepted that di- and tripeptides can be easily ab- A number of peptides predicted in our study have been re- sorbed from the gastrointestinal tract into the cardiovascular ported to be powerful ACE-inhibitory peptides with IC50 values circulation system where they exhibit physiological-regulating that are close to values predicted in this work. For example, actions (Matthews & Adibi, 1976). The activities of ACE- Pro-Leu (BIOPEP id: 7620) had an IC50 value of 293.02 μM, which inhibitory di- and tripeptides produced during in silico hydrolysis is similar to the reported value of 337.32 μM(Iwaniak et al., were further predicted by the QSAR models. The basic hypoth- 2014), while the IC50 of Val-Trp (BIOPEP id: 3487) (6.71 μM) is in esis of QSAR is that the bioactivity is closely related to the the vicinity of the documented value (1.60 μM) (Marczak et al., structural characteristics of the molecules and this relation- 2003). Leu-Arg-Pro (BIOPEP id: 7743) (IC50=1.50 μM) also exhib- ship can be further modelled (Iwaniak et al., 2015). Modelling ited similar ACE-inhibitory activity to the experimentally of the reported di- and tripeptides was conducted according determined 1.00 μM value (Matsumura, Fujii, Takeda, Sugita, to the data sets in the supplementary material (Table S1A & Shimizu, 1993). Thus, the current QSAR models for di- and and B). Initial modelling of the 5-z scale descriptors with ACE- tripeptides can be used to predict IC50 values of ACE-inhibitory inhibitory activities resulted in a two-component PLS model, peptides released from in silico proteolysis. Based on the QSAR which displayed the relationship between X and Y. Further- models, a number of bovine collagen-derived peptides display more, the relationships between the predicted and the very low IC50 values, indicating the potential of bovine colla- measured values were examined. The R2, the multiple corre- gen as a good precursor for ACE-inhibitory peptides. lation coefficients for dipeptide model, was 74.6%, while the The two most potent peptides (Tyr-Trp and Leu-Arg-Tyr) from R2 value was relatively lower for the tri-peptide model (44.5%) di- and tripeptide groups were selected, synthesised and veri- due to larger differences between diverse determination ap- fied for in vitro ACE-inhibitory activities. This selection was based proaches used by different laboratories. It has been documented on the predicted results of the unreported ACE-inhibitory pep- that the extended 5-z scale gives rise to an improved quality tides from QSAR models as well as present knowledge in models, compared with 3-z scale for functional properties concerning peptide structural characteristics, especially the of protein with aid of amino acid compositions (Siebert, 2003). presence of hydrophobic amino acids at the C-terminal. ACE- When compared to the multiple correlation coefficient in a pre- inhibitory activities of the chemically synthesised peptides were 2 vious investigation (Wu et al., 2006), the R value in the dipeptide assayed and the IC50 values of Tyr-Trp and Leu-Arg-Tyr were model in the present study was slightly higher (74.6 vs. 73.2%), 3.73 and 8.12 μM, respectively.The values were close to the QSAR while it was slightly lower to the previous tripeptide model (44.5 model-predicted data (4.72 and 1.19 μM), which suggested good vs. 47.1%). Thus, the current models were analogous to these validity of the predictive models. In addition, the presence of previous ones due to the common sets of data employed for aromatic amino acids (Tyr and Trp) at the C-terminal of these model establishment. In addition, the PLS regression coeffi- two novel ACE-inhibitory peptides further implied their sig- cients were further analysed to exhibit the relationship between nificant contribution to ACE-inhibitory activity (Aluko, 2015b). X-variable and Y (Fig. 1A & B). The importance of a certain X However, it is noticeable that QSAR models help predict mag- for Y is proportional to its distance from zero and correspond- nitude of activity based on the structure–activity relationship ing to the PLS regression coefficients (Wold et al., 2001). In the of peptides, but a certain degree of the variation is inevitable. dipeptide model, position n2 corresponding to the C-terminal Therefore, in vitro activity verification by synthetic peptides is played an important role in the activity. Similarly, position n3 indispensable. 202 journal of functional foods 24 (2016) 196–206

Fig. 1 – PLS regression coefficients for dipeptides (A) and tripeptides (B). The importance for a certain X-variable is proportional to its distance (coefficient value) from the loading level (zero). The bars illustrate 95% confidence intervals based on jack-knifing.

Recently, several in silico investigations have demon- 3.4. The ACE-inhibitory peptide derived from strated food-derived ACE inhibitors from different protein papain-catalysed bovine collagen sources (Cavazos & Gonzalez de Mejia, 2013; Chang & Alli, 2012; Minkiewicz et al., 2011; Udenigwe et al., 2013). In the present According to in silico analysis, papain was the protease that pro- study, identification of novel ACE-inhibitory peptides based on duced the highest number of ACE-inhibitory peptides and one in silico approaches provides an alternative discovery strategy of the most potent peptides; therefore, this enzyme was se- for bovine collagen-derived peptides. Theoretically, this tar- lected for experimental hydrolysis of bovine collagen. The geted procedure contributes to screening of ACE-inhibitory separation procedures of collagen peptides as previously re- peptides from bovine collagen by various enzymatic hydroly- ported by Fu et al. (2015) were followed by RP-HPLC purification sis simultaneously. Nevertheless, it is not guaranteed that in and ACE-inhibitory activity screening (Fig. 2A & B). The most silico-derived peptides will be experimentally reproduced, given potent RP-HPLC purified ACE-inhibitory peptide (P5-A) was that in vitro hydrolysis may be hindered due to intricate process identified as a pentapeptide (Gly-Pro-Arg-Gly-Phe) with an of enzyme–protein interactions (Udenigwe, 2014). Therefore, ex- experimentally determined IC50 value (200.91 μM) (Fig. 2C). This perimental hydrolysis using best protein–enzyme combinations peptide was more active than the other pentapeptides with ACE-

(collagen–papain) to release ACE-inhibitory peptides was also inhibitory activity, such as Gly-Pro-Ser-Met-Arg (IC50 = 277.5 μM) investigated. and Ile-Thr-Thr-Asn-Pro (IC50 = 549.0 μM) derived from journal of functional foods 24 (2016) 196–206 203

Fig. 2 – Purification by RP-HPLC and characterisation of ACE-inhibitory peptides derived from bovine collagen. (A) P1–P8 represent main fractions purified from collagen peptide fractions. (B) P5-A and P5-B indicate main fractions of P5. (C) MS/MS spectrum of Gly-Pro-Arg-Gly-Phe. Different letters represent significantly different values (P < 0.05) by one-way ANOVA analysis.

mushroom (Lau, Abdullah, & Shuib, 2013) and porcine myosin antihypertensive effects (Toldrá et al., 2012). A collagen-

(Arihara, Nakashima, Mukai, Ishikawa, & Itoh, 2001), respec- derived ACE-inhibitory peptide (Gly-Pro) with IC50 value of tively. Some animal protein-derived ACE-inhibitory peptides 360 μM could significantly decrease blood pressure of sponta- with IC50 values above 200 μM still possessed pronounced neously hypertensive rats (Nakashima et al., 2002). Therefore, 204 journal of functional foods 24 (2016) 196–206

collagen peptides in this study were promising to be effec- in vitro digestion. The present study highlighted that in silico tive ACE inhibitors. In addition, this peptide was derived from and in vitro protein digestions of bovine collagen effectively gen- bovine collagen alpha-1(II) f(146–150) sequence and it struc- erated ACE-inhibitory peptides, which provided a theoretical turally contained the sequences of two in silico-predicted peptide basis for the development of bovine collagen as a precursor sequences (Gly-Pro-Arg and Gly-Phe) with documented ACE- of ACE-inhibitory peptides in the food industry. inhibitory activities. The IC50 values of Gly-Pro-Arg and Gly- Phe were 132.80 μM and 19.95 μM, respectively (Toldrá et al., 2012). Unfortunately, the smaller peptides from experimental hydrolysis with higher ACE-inhibitory activity were not present Acknowledgement in the most potent peptide fraction identified by mass spec- trometry.This difference between the experimental and in silico The authors gratefully acknowledge financial support by Future results can be attributed to several reasons. First, the most Food Innovation, regional consortium of Central Denmark and potent pentapeptide originates from bovine collagen alpha- Graduate School of Science and Technology (GSST) at Aarhus 1(II), which exhibits slight difference from collagen alpha-1(I) University. used in in silico hydrolysis, despite that these two collagen se- quences exhibit 72% homology based on a BLAST analysis. Second, in silico proteolysis also has several limitations and it is not guaranteed that virtually-generated peptides can be ex- Appendix: Supplementary material perimentally reproduced with regard to complete protease digestion of proteins (Udenigwe, 2014). In addition, in vitro hy- Supplementary data to this article can be found online at drolysis failed to release the smaller peptides probably due to doi:10.1016/j.jff.2016.03.026. external factors during hydrolysis, e.g. substrate condition, pro- tease efficiency, temperature and pH (Gu & Wu, 2013). Also, in terms of intricate interactions of protease–proteins, the complex REFERENCES tertiary and quaternary protein structure might reduce the enzyme access, preventing a match between the predicted pep- tides and the experimentally generated peptides. Collagen Alderman, C. P. (1996). Adverse effects of the angiotensin- contains a number of interchain disulphide bonds between the converting enzyme inhibitors. Annals of Pharmacotherapy, 30, C- and the N-terminal propeptide sequences, which contrib- 55–61. Altschul, S. F., Wootton, J. C., Gertz, E. M., Agarwala, R., Morgulis, ute to the collagen triple-helix structure (Byers, Click, Harper, A., Schaffer, A. A., & Yu, Y. K. (2005). Protein database searches & Bornstein, 1975) and thus prevent enzyme access and cleav- using compositionally adjusted substitution matrices. FEBS age. Alternatively, a number of smaller peptides were excluded Journal, 272, 5101–5109. during the purification procedures as a result of co-elution with Aluko, R. E. (2015a). Antihypertensive peptides from food some collagen peptide fractions with weaker ACE-inhibitory proteins. Annual Review of Food Science and Technology, 6, 235– activities. In the present study, the theoretically-released col- 262. lagen peptides were a result of complete digestion, whereas Aluko, R. E. (2015b). Structure and function of plant protein- derived antihypertensive peptides. Current Opinion in Food in vitro digestion of collagen might be limited by external factors. Science, 4, 44–50. Overall, in silico analyses can serve as an alternative ap- Arihara, K., Nakashima, Y., Mukai, T., Ishikawa, S., & Itoh, M. proach for extrapolation of the potential ACE-inhibitory peptides (2001). Peptide inhibitors for angiotensin I-converting enzyme from bovine collagen, while in vitro digestion of bovine colla- from enzymatic hydrolysates of porcine skeletal muscle gen by papain also generated potent ACE-inhibitory peptides. proteins. Meat Science, 57, 319–324. Byers, P. H., Click, E. M., Harper, E., & Bornstein, P. (1975). Interchain disulfide bonds in procollagen are located in a large nontriple-helical COOH-terminal domain. Proceedings of 4. Conclusions the National Academy of Sciences, 72, 3009–3013. Byun, H. G., & Kim, S. K. (2001). Purification and characterization of angiotensin I converting enzyme (ACE) inhibitory peptides Bovine collagen can serve as a promising precursor of ACE- from Alaska pollack (Theragra chalcogramma) skin. Process inhibitory peptides. In silico proteolysis of collagen alpha-1(I) Biochemistry, 36, 1155–1162. and alpha-2(I) sequences coupled with activity prediction iden- Cavazos, A., & Gonzalez de Mejia, E. (2013). Identification of bioactive peptides from cereal storage proteins and their tified potent ACE-inhibitory peptides, two of which were further potential role in prevention of chronic diseases. Comprehensive verified by chemically synthesised peptides. Based on structure– Reviews in Food Science and Food Safety, 12, 364–380. function properties of ACE-inhibitory peptides, collagen- Chang, Y.-W., & Alli, I. (2012). In silico assessment: Suggested derived peptides containing aromatic amino acids (Trp, Tyr and homology of chickpea (Cicer arietinum L.) legumin and Phe) at the C-terminal significantly contributed to potent ACE- prediction of ACE-inhibitory peptides from chickpea proteins inhibitory activity. In vitro hydrolysis of bovine collagen by papain using BLAST and BIOPEP analyses. Food Research International, generated ACE-inhibitory peptides. However, the most active 49, 477–486. Cheung, I. W. Y., Nakayama, S., Hsu, M. N. K., Samaranayaka, A. G. peptide was identified as a pentapeptide, which was not iden- P., & Li-Chan, E. C. Y. (2009). Angiotensin-I converting enzyme tical to any of the in silico peptides derived from papain inhibitory activity of hydrolysates from oat (Avena sativa) digestion. The difference was mainly attributed to complete proteins by in silico and in vitro analyses. Journal of digestion by in silico digestion and the interference factors of Agricultural and Food Chemistry, 57, 9234–9242. journal of functional foods 24 (2016) 196–206 205

Damgaard, T., Lametsch, R., & Otte, J. (2015). Antioxidant capacity application as functional ingredients. Meat Science, 98, 227– of hydrolyzed animal by-products and relation to amino acid 239. composition and peptide size distribution. Journal of Food Lafarga, T., O’Connor, P., & Hayes, M. (2014). Identification of Science and Technology, 52, 6511–6519. novel dipeptidyl peptidase-IV and angiotensin-I-converting Efron, B., & Gong, G. (1983). A leisurely look at the bootstrap, the enzyme inhibitory peptides from meat proteins using in silico jackknife, and cross-validation. The American Statistician, 37, analysis. Peptides, 59, 53–62. 36–48. Lau, C., Abdullah, N., & Shuib, A. (2013). Novel angiotensin Ewart, H. S., Dennis, D., Potvin, M., Tiller, C., Fang, L. H., Zhang, R., I-converting enzyme inhibitory peptides derived from an Zhu, X. M., Curtis, J. M., Cloutier, S., Du, G. H., & Barrow, C. J. edible mushroom, Pleurotus cystidiosus O.K. Miller identified by (2009). Development of a salmon protein hydrolysate that LC-MS/MS. BMC Complementary and Alternative Medicine, 13, lowers blood pressure. European Food Research and Technology, 313. 229, 561–569. Majumder, K., & Wu, J. (2010). A new approach for identification Fu, Y., Young, J. F., Dalsgaard, T. K., & Therkildsen, M. (2015). of novel antihypertensive peptides from egg proteins by Separation of angiotensin I-converting enzyme inhibitory QSAR and bioinformatics. Food Research International, 43, 1371– peptides from bovine connective tissue and their stability 1378. towards temperature, pH and digestive enzymes. International Marczak, E. D., Usui, H., Fujita, H., Yang, Y., Yokoo, M., Lipkowski, Journal of Food Science and Technology, 50, 1234–1243. A. W., & Yoshikawa, M. (2003). New antihypertensive peptides Fu, Y., & Zhao, X.-H. (2013). In vitro responses of hFOB1.19 cells isolated from rapeseed. Peptides, 24, 791–798. towards chum salmon (Oncorhynchus keta) skin gelatin Matsumura, N., Fujii, M., Takeda, Y., Sugita, K., & Shimizu, T. hydrolysates in cell proliferation, cycle progression and (1993). Angiotensin I-converting enzyme inhibitory peptides apoptosis. Journal of Functional Foods, 5, 279–288. derived from bonito bowels autolysate. Biosciences Gelse, K., Poschl, E., & Aigner, T. (2003). Collagens – Structure, Biotechnology and Biochemistry, 57, 695–697. function, and biosynthesis. Advanced Drug Delivery Reviews, 55, Matthews, D. M., & Adibi, S. A. (1976). Peptide absorption. 1531–1546. Gastroenterology, 71, 151–161. Gómez-Guillén, M. C., Giménez, B., López-Caballero, M. E., & Miguel, M., Aleixandre, M. A., Ramos, M., & López-Fandiño, R. Montero, M. P. (2011). Functional and bioactive properties of (2006). Effect of simulated gastrointestinal digestion on the collagen and gelatin from alternative sources: A review. Food antihypertensive properties of ACE-inhibitory peptides Hydrocolloids, 25, 1813–1827. derived from ovalbumin. Journal of Agricultural and Food Gu, Y., & Wu, J. (2013). LC-MS/MS coupled with QSAR modeling in Chemistry, 54, 726–731. characterising of angiotensin I-converting enzyme inhibitory Minkiewicz, P., Dziuba, J., Iwaniak, A., Dziuba, M., & Darewicz, M. peptides from soybean proteins. Food Chemistry, 141, 2682– (2008). BIOPEP database and other programs for processing 2690. bioactive peptide sequences. Journal of AOAC International, 91, Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A., Kumar, R., 965–980. Raghava, G. P., & Consortium, O. S. D. D. (2013). In silico Minkiewicz, P., Dziuba, J., & Michalska, J. (2011). Bovine meat approach for predicting toxicity of peptides and proteins. PLoS proteins as potential precursors of biologically active peptides ONE, 8, e73957. – A computational study based on the BIOPEP database. Food Ibrahim, M. M., & Damasceno, A. (2012). Hypertension in Science and Technology International, 17, 39–45. developing countries. The Lancet, 380, 611–619. Nakashima, Y., Arihara, K., Sasaki, A., Mio, H., Ishikawa, S., & Itoh, Iwaniak, A., & Dziuba, J. (2011). BIOPEP-PBIL tool for the analysis M. (2002). Antihypertensive activities of peptides derived from of the structure of biologically active motifs derived from food porcine skeletal muscle myosin in spontaneously proteins. Food Technology and Biotechnology, 49, 118–127. hypertensive rats. Journal of Food Science, 67, 434–437. Iwaniak, A., Minkiewicz, P., & Darewicz, M. (2014). Food- Ondetti, M. A., & Cushman, D. W. (1977). Design of specific originating ACE inhibitors, including antihypertensive inhibitors of angiotensin-converting enzyme: New class of peptides, as preventive food components in blood pressure orally active antihypertensive agents. Science, 196, 441– reduction. Comprehensive Reviews in Food Science and Food 444. Safety, 13, 114–134. Otte, J., Shalaby, S. M., Zakora, M., & Nielsen, M. S. (2007). Iwaniak, A., Minkiewicz, P., Darewicz, M., Protasiewicz, M., & Fractionation and identification of ACE-inhibitory peptides Mogut, D. (2015). Chemometrics and cheminformatics in the from α-lactalbumin and β-casein produced by thermolysin- analysis of biologically active peptides from food sources. catalysed hydrolysis. International Dairy Journal, 17, 1460– Journal of Functional Foods, 16, 334–351. 1472. Je, J.-Y., Cho, Y.-S., Gong, M., & Udenigwe, C. C. (2015). Dipeptide Petrat-Melin, B., Andersen, P., Rasmussen, J. T., Poulsen, N. A., Phe-Cys derived from in silico thermolysin-hydrolysed Larsen, L. B., & Young, J. F. (2015). In vitro digestion of purified RuBisCO large subunit suppresses oxidative stress in cultured β-casein variants A1, A2, B, and I: Effects on antioxidant and human hepatocytes. Food Chemistry, 171, 287–291. angiotensin-converting enzyme inhibitory capacity. Journal of Kim, D. W., Park, K., Ha, G., Jung, J. R., Chang, O., Ham, J.-S., Jeong, Dairy Science, 98, 15–26. S.-G., Park, B.-Y., Song, J., & Jang, A. (2013). Anti-oxidative and Pripp, A. H., Isaksson, T., Stepaniak, L., & Sørhaug, T. (2004). neuroprotective activities of pig skin gelatin hydrolysates. Quantitative structure-activity relationship modelling of ACE- Korean Journal for Food Science of Animal Resources, 33, 258–267. inhibitory peptides derived from milk proteins. European Food Kim, S. K., Byun, H. G., Park, P. J., & Shahidi, F. (2001). Angiotensin Research and Technology, 219, 579–583. I converting enzyme inhibitory peptides purified from bovine Saiga, A., Iwai, K., Hayakawa, T., Takahata, Y., Kitamura, S., skin gelatin hydrolysate. Journal of Agricultural and Food Nishimura, T., & Morimatsu, F. (2008). Angiotensin Chemistry, 49, 2992–2997. I-converting enzyme-inhibitory peptides obtained from Lacroix, I. M. E., & Li-Chan, E. C. Y. (2012). Evaluation of the chicken collagen hydrolysate. Journal of Agricultural and Food potential of dietary proteins as precursors of dipeptidyl Chemistry, 56, 9586–9591. peptidase (DPP)-IV inhibitors by an in silico approach. Journal of Saito, Y., Kawato, A., & Imayasu, S. (1994). Structure and activity Functional Foods, 4, 403–422. of angiotensin I converting enzyme inhibitory peptides from Lafarga, T., & Hayes, M. (2014). Bioactive peptides from meat sake and sake lees. Biosciences Biotechnology and Biochemistry, muscle and by-products: Generation, functionality and 58, 1767–1771. 206 journal of functional foods 24 (2016) 196–206

Sandberg, M., Eriksson, L., Jonsson, J., Sjöström, M., & Wold, S. Udenigwe, C. C., Gong, M., & Wu, S. (2013). In silico analysis of the (1998). New chemical descriptors relevant for the design of large and small subunits of cereal RuBisCO as precursors of biologically active peptides. A multivariate characterization of cryptic bioactive peptides. Process Biochemistry, 48, 1794–1799. 87 amino acids. Journal of Medicinal Chemistry, 41, 2481–2491. Udenigwe, C. C., & Howard, A. (2013). Meat proteome as source of Siebert, K. J. (2003). Modeling protein functional properties from functional biopeptides. Food Research International, 54, 1021– amino acid composition. Journal of Agricultural and Food 1032. Chemistry, 51, 7792–7797. Vercruysse, L., Smagghe, G., van der Bent, A., van Amerongen, A., Skeggs, L. T., Kahn, J. R., & Shumway, N. P. (1956). The preparation Ongenaert, M., & Van Camp, J. (2009). Critical evaluation of the and function of the hypertensin-converting enzyme. The use of bioinformatics as a theoretical tool to find high- Journal of Experimental Medicine, 103, 295–299. potential sources of ACE inhibitory peptides. Peptides, 30, 575– Toldrá, F., Aristoy, M. C., Mora, L., & Reig, M. (2012). Innovations in 582. value-addition of edible meat by-products. Meat Science, 92, Wold, S., Sjostrom, M., & Eriksson, L. (2001). PLS-regression: A 290–296. basic tool of chemometrics. Chemometrics and Intelligent Udenigwe, C. C. (2014). Bioinformatics approaches, prospects and Laboratory Systems, 58, 109–130. challenges of food bioactive peptide research. Trends in Food Wu, J. P., Aluko, R. E., & Nakai, S. (2006). Structural requirements Science & Technology, 36, 137–143. of angiotensin I-converting enzyme inhibitory peptides: Udenigwe, C. C., & Aluko, R. E. (2012). Food protein-derived Quantitative structure-activity relationship study of di- and bioactive peptides: Production, processing, and potential tripeptides. Journal of Agricultural and Food Chemistry, 54, 732– health benefits. Journal of Food Science, 77, R11–R24. 738. Table S1A Descriptor scores for dipeptides

Peptide n1z1 n1z2 n1z3 n1z4 n1z4 n2z1 n2z2 n2z3 n2z4 n2z5 logIC50 VW -2.59 -2.64 -1.54 -0.85 -0.02 -4.36 3.94 0.59 3.44 -1.59 0.15

IW -3.89 -1.73 -1.71 -0.84 0.26 -4.36 3.94 0.59 3.44 -1.59 0.18

VW -2.59 -2.64 -1.54 -0.85 -0.02 -4.36 3.94 0.59 3.44 -1.59 0.20

VW -2.59 -2.64 -1.54 -0.85 -0.02 -4.36 3.94 0.59 3.44 -1.59 0.20

KW 2.29 0.89 -2.49 1.49 0.31 -4.36 3.94 0.59 3.44 -1.59 0.21

FY -4.22 1.94 1.06 0.54 -0.62 -2.54 2.44 0.43 0.04 -1.47 0.22

VW -2.59 -2.64 -1.54 -0.85 -0.02 -4.36 3.94 0.59 3.44 -1.59 0.23

IY -3.89 -1.73 -1.71 -0.84 0.26 -2.54 2.44 0.43 0.04 -1.47 0.30

IY -3.89 -1.73 -1.71 -0.84 0.26 -2.54 2.44 0.43 0.04 -1.47 0.32

IY -3.89 -1.73 -1.71 -0.84 0.26 -2.54 2.44 0.43 0.04 -1.47 0.36

IY -3.89 -1.73 -1.71 -0.84 0.26 -2.54 2.44 0.43 0.04 -1.47 0.38

VW -2.59 -2.64 -1.54 -0.85 -0.02 -4.36 3.94 0.59 3.44 -1.59 0.40

IY -3.89 -1.73 -1.71 -0.84 0.26 -2.54 2.44 0.43 0.04 -1.47 0.43

VW -2.59 -2.64 -1.54 -0.85 -0.02 -4.36 3.94 0.59 3.44 -1.59 0.52

IY -3.89 -1.73 -1.71 -0.84 0.26 -2.54 2.44 0.43 0.04 -1.47 0.57

FY -4.22 1.94 1.06 0.54 -0.62 -2.54 2.44 0.43 0.04 -1.47 0.57

MW -2.85 -0.22 0.47 1.94 -0.98 -4.36 3.94 0.59 3.44 -1.59 0.58

IW -3.89 -1.73 -1.71 -0.84 0.26 -4.36 3.94 0.59 3.44 -1.59 0.67 YH -2.54 2.44 0.43 0.04 -1.47 2.47 1.95 0.26 3.9 0.09 0.71

IY -3.89 -1.73 -1.71 -0.84 0.26 -2.54 2.44 0.43 0.04 -1.47 0.79

LY -4.28 -1.3 -1.49 -0.72 0.84 -2.54 2.44 0.43 0.04 -1.47 0.81

FY -4.22 1.94 1.06 0.54 -0.62 -2.54 2.44 0.43 0.04 -1.47 0.81

LY -4.28 -1.3 -1.49 -0.72 0.84 -2.54 2.44 0.43 0.04 -1.47 0.83

LW -4.28 -1.3 -1.49 -0.72 0.84 -4.36 3.94 0.59 3.44 -1.59 0.83

KY 2.29 0.89 -2.49 1.49 0.31 -2.54 2.44 0.43 0.04 -1.47 0.89

VF -2.59 -2.64 -1.54 -0.85 -0.02 -4.22 1.94 1.06 0.54 -0.62 0.96

MW -2.85 -0.22 0.47 1.94 -0.98 -4.36 3.94 0.59 3.44 -1.59 1.00

AW 0.24 -2.32 0.6 -0.14 1.3 -4.36 3.94 0.59 3.44 -1.59 1.00

YW -2.54 2.44 0.43 0.04 -1.47 -4.36 3.94 0.59 3.44 -1.59 1.02

RY 3.52 2.5 -3.5 1.99 -0.17 -2.54 2.44 0.43 0.04 -1.47 1.02

IY -3.89 -1.73 -1.71 -0.84 0.26 -2.54 2.44 0.43 0.04 -1.47 1.02

KW 2.29 0.89 -2.49 1.49 0.31 -4.36 3.94 0.59 3.44 -1.59 1.03

VW -2.59 -2.64 -1.54 -0.85 -0.02 -4.36 3.94 0.59 3.44 -1.59 1.03

VY -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 1.05

AW 0.24 -2.32 0.6 -0.14 1.3 -4.36 3.94 0.59 3.44 -1.59 1.08

DG 3.98 0.93 1.93 -2.46 0.75 2.05 -4.06 0.36 -0.82 -0.38 1.09

IW -3.89 -1.73 -1.71 -0.84 0.26 -4.36 3.94 0.59 3.44 -1.59 1.09

VK -2.59 -2.64 -1.54 -0.85 -0.02 2.29 0.89 -2.49 1.49 0.31 1.11 AF 0.24 -2.32 0.6 -0.14 1.3 -4.22 1.94 1.06 0.54 -0.62 1.18

VY -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 1.20

FL -4.22 1.94 1.06 0.54 -0.62 -4.28 -1.3 -1.49 -0.72 0.84 1.20

RW 3.52 2.5 -3.5 1.99 -0.17 -4.36 3.94 0.59 3.44 -1.59 1.20

YL -2.54 2.44 0.43 0.04 -1.47 -4.28 -1.3 -1.49 -0.72 0.84 1.21

LW -4.28 -1.3 -1.49 -0.72 0.84 -4.36 3.94 0.59 3.44 -1.59 1.24

TF 0.75 -2.18 -1.12 -1.46 -0.4 -4.22 1.94 1.06 0.54 -0.62 1.25

VY -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 1.25

AW 0.24 -2.32 0.6 -0.14 1.3 -4.36 3.94 0.59 3.44 -1.59 1.27

AY 0.24 -2.32 0.6 -0.14 1.3 -2.54 2.44 0.43 0.04 -1.47 1.28

RP 3.52 2.5 -3.5 1.99 -0.17 -1.66 0.27 1.84 0.7 2 1.32

KP 2.29 0.89 -2.49 1.49 0.31 -1.66 0.27 1.84 0.7 2 1.34

VY -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 1.34

RW 3.52 2.5 -3.5 1.99 -0.17 -4.36 3.94 0.59 3.44 -1.59 1.34

LW -4.28 -1.3 -1.49 -0.72 0.84 -4.36 3.94 0.59 3.44 -1.59 1.37

FY -4.22 1.94 1.06 0.54 -0.62 -2.54 2.44 0.43 0.04 -1.47 1.40

VY -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 1.41

HY 2.47 1.95 0.26 3.9 0.09 -2.54 2.44 0.43 0.04 -1.47 1.42

KF 2.29 0.89 -2.49 1.49 0.31 -4.22 1.94 1.06 0.54 -0.62 1.45

AP 0.24 -2.32 0.6 -0.14 1.3 -1.66 0.27 1.84 0.7 2 1.46 GW 2.05 -4.06 0.36 -0.82 -0.38 -4.36 3.94 0.59 3.44 -1.59 1.48

WL -4.36 3.94 0.59 3.44 -1.59 -4.28 -1.3 -1.49 -0.72 0.84 1.48

KP 2.29 0.89 -2.49 1.49 0.31 -1.66 0.27 1.84 0.7 2 1.48

NY 3.05 1.62 1.04 -1.15 1.61 -2.54 2.44 0.43 0.04 -1.47 1.51

LY -4.28 -1.3 -1.49 -0.72 0.84 -2.54 2.44 0.43 0.04 -1.47 1.51

VY -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 1.55

LY -4.28 -1.3 -1.49 -0.72 0.84 -2.54 2.44 0.43 0.04 -1.47 1.59

FY -4.22 1.94 1.06 0.54 -0.62 -2.54 2.44 0.43 0.04 -1.47 1.63

VY -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 1.64

MF -2.85 -0.22 0.47 1.94 -0.98 -4.22 1.94 1.06 0.54 -0.62 1.65

NF 3.05 1.62 1.04 -1.15 1.61 -4.22 1.94 1.06 0.54 -0.62 1.67

LW -4.28 -1.3 -1.49 -0.72 0.84 -4.36 3.94 0.59 3.44 -1.59 1.70

RY 3.52 2.5 -3.5 1.99 -0.17 -2.54 2.44 0.43 0.04 -1.47 1.71

FQ -4.22 1.94 1.06 0.54 -0.62 1.75 0.5 -1.44 -1.34 0.66 1.71

KP 2.29 0.89 -2.49 1.49 0.31 -1.66 0.27 1.84 0.7 2 1.71

VF -2.59 -2.64 -1.54 -0.85 -0.02 -4.22 1.94 1.06 0.54 -0.62 1.72

IL -3.89 -1.73 -1.71 -0.84 0.26 -4.28 -1.3 -1.49 -0.72 0.84 1.74

VY -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 1.76

SY 2.39 -1.07 1.15 -1.39 0.67 -2.54 2.44 0.43 0.04 -1.47 1.82

GY 2.05 -4.06 0.36 -0.82 -0.38 -2.54 2.44 0.43 0.04 -1.47 1.86 AF -4.28 -1.3 -1.49 -0.72 0.84 -4.22 1.94 1.06 0.54 -0.62 1.88

YL -2.54 2.44 0.43 0.04 -1.47 -4.28 -1.3 -1.49 -0.72 0.84 1.91

AY 0.24 -2.32 0.6 -0.14 1.3 -2.54 2.44 0.43 0.04 -1.47 1.94

TF 0.75 -2.18 -1.12 -1.46 -0.4 -4.22 1.94 1.06 0.54 -0.62 1.95

RP 3.52 2.5 -3.5 1.99 -0.17 -1.66 0.27 1.84 0.7 2 1.96

RF 3.52 2.5 -3.5 1.99 -0.17 -4.22 1.94 1.06 0.54 -0.62 1.97

WM -4.36 3.94 0.59 3.44 -1.59 -2.85 -0.22 0.47 1.94 -0.98 1.98

DY 3.98 0.93 1.93 -2.46 0.75 -2.54 2.44 0.43 0.04 -1.47 2.00

AY 0.24 -2.32 0.6 -0.14 1.3 -2.54 2.44 0.43 0.04 -1.47 2.00

KF 2.29 0.89 -2.49 1.49 0.31 -4.22 1.94 1.06 0.54 -0.62 2.06

YL -2.54 2.44 0.43 0.04 -1.47 -4.28 -1.3 -1.49 -0.72 0.84 2.09

LF -4.28 -1.3 -1.49 -0.72 0.84 -4.22 1.94 1.06 0.54 -0.62 2.10

SF 2.39 -1.07 1.15 -1.39 0.67 -4.22 1.94 1.06 0.54 -0.62 2.11

IP -3.89 -1.73 -1.71 -0.84 0.26 -1.66 0.27 1.84 0.7 2 2.11

IA -3.89 -1.73 -1.71 -0.84 0.26 0.24 -2.32 0.6 -0.14 1.3 2.18

RP 3.52 2.5 -3.5 1.99 -0.17 -1.66 0.27 1.84 0.7 2 2.26

AF 0.24 -2.32 0.6 -0.14 1.3 -4.22 1.94 1.06 0.54 -0.62 2.28

MY -2.85 -0.22 0.47 1.94 -0.98 -2.54 2.44 0.43 0.04 -1.47 2.29

GY 2.05 -4.06 0.36 -0.82 -0.38 -2.54 2.44 0.43 0.04 -1.47 2.32

AP 0.24 -2.32 0.6 -0.14 1.3 -1.66 0.27 1.84 0.7 2 2.36 RF 3.52 2.5 -3.5 1.99 -0.17 -4.22 1.94 1.06 0.54 -0.62 2.36

GY 2.05 -4.06 0.36 -0.82 -0.38 -2.54 2.44 0.43 0.04 -1.47 2.41

GY 2.05 -4.06 0.36 -0.82 -0.38 -2.54 2.44 0.43 0.04 -1.47 2.42

AP 0.24 -2.32 0.6 -0.14 1.3 -1.66 0.27 1.84 0.7 2 2.43

GF 2.05 -4.06 0.36 -0.82 -0.38 -4.22 1.94 1.06 0.54 -0.62 2.44

WA -4.36 3.94 0.59 3.44 -1.59 0.24 -2.32 0.6 -0.14 1.3 2.44

TP 0.75 -2.18 -1.12 -1.46 -0.4 -1.66 0.27 1.84 0.7 2 2.46

FP -4.22 1.94 1.06 0.54 -0.62 -1.66 0.27 1.84 0.7 2 2.50

LF -4.28 -1.3 -1.49 -0.72 0.84 -4.22 1.94 1.06 0.54 -0.62 2.54

GP 2.05 -4.06 0.36 -0.82 -0.38 -1.66 0.27 1.84 0.7 2 2.56

DF 3.98 0.93 1.93 -2.46 0.75 -4.22 1.94 1.06 0.54 -0.62 2.56

VP -2.59 -2.64 -1.54 -0.85 -0.02 -1.66 0.27 1.84 0.7 2 2.62

GP 2.05 -4.06 0.36 -0.82 -0.38 -1.66 0.27 1.84 0.7 2 2.65

VP -2.59 -2.64 -1.54 -0.85 -0.02 -1.66 0.27 1.84 0.7 2 2.76

YV -2.54 2.44 0.43 0.04 -1.47 -2.59 -2.64 -1.54 -0.85 -0.02 2.76

DM 3.98 0.93 1.93 -2.46 0.75 -2.85 -0.22 0.47 1.94 -0.98 2.78

YE -2.54 2.44 0.43 0.04 -1.47 3.11 0.26 -0.11 -3.04 -0.25 2.80

GF 2.05 -4.06 0.36 -0.82 -0.38 -4.22 1.94 1.06 0.54 -0.62 2.80

IR -3.89 -1.73 -1.71 -0.84 0.26 3.52 2.5 -3.5 1.99 -0.17 2.84

GF 2.05 -4.06 0.36 -0.82 -0.38 -4.22 1.94 1.06 0.54 -0.62 2.85 YP -2.54 2.44 0.43 0.04 -1.47 -1.66 0.27 1.84 0.7 2 2.86

IR -3.89 -1.73 -1.71 -0.84 0.26 3.52 2.5 -3.5 1.99 -0.17 2.92

YP -2.54 2.44 0.43 0.04 -1.47 3.98 0.93 1.93 -2.46 0.75 2.95

QK 1.75 0.5 -1.44 -1.34 0.66 2.29 0.89 -2.49 1.49 0.31 2.95

IF -3.89 -1.73 -1.71 -0.84 0.26 -4.22 1.94 1.06 0.54 -0.62 2.97

YG -2.54 2.44 0.43 0.04 -1.47 2.05 -4.06 0.36 -0.82 -0.38 3.04

VG -2.59 -2.64 -1.54 -0.85 -0.02 2.05 -4.06 0.36 -0.82 -0.38 3.04

GP 2.05 -4.06 0.36 -0.82 -0.38 -1.66 0.27 1.84 0.7 2 3.08

GI 2.05 -4.06 0.36 -0.82 -0.38 -3.89 -1.73 -1.71 -0.84 0.26 3.08

IG -3.89 -1.73 -1.71 -0.84 0.26 -2.59 -2.64 -1.54 -0.85 -0.02 3.08

RG 3.52 2.5 -3.5 1.99 -0.17 -2.59 -2.64 -1.54 -0.85 -0.02 3.08

GI 2.05 -4.06 0.36 -0.82 -0.38 -3.89 -1.73 -1.71 -0.84 0.26 3.11

VQ -2.59 -2.64 -1.54 -0.85 -0.02 1.75 0.5 -1.44 -1.34 0.66 3.11

GM 2.05 -4.06 0.36 -0.82 -0.38 -2.85 -0.22 0.47 1.94 -0.98 3.15

YG -2.54 2.44 0.43 0.04 -1.47 2.05 -4.06 0.36 -0.82 -0.38 3.18

TK 0.75 -2.18 -1.12 -1.46 -0.4 2.29 0.89 -2.49 1.49 0.31 3.21

DL 3.98 0.93 1.93 -2.46 0.75 -4.28 -1.3 -1.49 -0.72 0.84 3.30

GA 2.05 -4.06 0.36 -0.82 -0.38 0.24 -2.32 0.6 -0.14 1.3 3.30

YG -2.54 2.44 0.43 0.04 -1.47 2.05 -4.06 0.36 -0.82 -0.38 3.30

TP 0.75 -2.18 -1.12 -1.46 -0.4 -1.66 0.27 1.84 0.7 2 3.32 NP 3.05 1.62 1.04 -1.15 1.61 -1.66 0.27 1.84 0.7 2 3.36

RL 3.52 2.5 -3.5 1.99 -0.17 -4.28 -1.3 -1.49 -0.72 0.84 3.39

GL 2.05 -4.06 0.36 -0.82 -0.38 -4.28 -1.3 -1.49 -0.72 0.84 3.40

AG 0.24 -2.32 0.6 -0.14 1.3 2.05 -4.06 0.36 -0.82 -0.38 3.40

GH 2.05 -4.06 0.36 -0.82 -0.38 2.47 1.95 0.26 3.9 0.09 3.49

GR 2.05 -4.06 0.36 -0.82 -0.38 3.52 2.5 -3.5 1.99 -0.17 3.51

KG 2.29 0.89 -2.49 1.49 0.31 2.05 -4.06 0.36 -0.82 -0.38 3.51

LF -4.28 -1.3 -1.49 -0.72 0.84 -4.22 1.94 1.06 0.54 -0.62 3.52

FG -4.22 1.94 1.06 0.54 -0.62 2.05 -4.06 0.36 -0.82 -0.38 3.57

GS 2.05 -4.06 0.36 -0.82 -0.38 2.39 -1.07 1.15 -1.39 0.67 3.58

GV 2.05 -4.06 0.36 -0.82 -0.38 -2.59 -2.64 -1.54 -0.85 -0.02 3.66

MG -2.85 -0.22 0.47 1.94 -0.98 2.05 -4.06 0.36 -0.82 -0.38 3.68

GK 2.05 -4.06 0.36 -0.82 -0.38 2.29 0.89 -2.49 1.49 0.31 3.73

GQ 2.05 -4.06 0.36 -0.82 -0.38 1.75 0.5 -1.44 -1.34 0.66 3.73

GQ -2.54 2.44 0.43 0.04 -1.47 1.75 0.5 -1.44 -1.34 0.66 3.75

GT 2.05 -4.06 0.36 -0.82 -0.38 0.75 -2.18 -1.12 -1.46 -0.4 3.76

WG -4.36 3.94 0.59 3.44 -1.59 2.05 -4.06 0.36 -0.82 -0.38 3.77

HG 2.47 1.95 0.26 3.9 0.09 2.05 -4.06 0.36 -0.82 -0.38 3.80

GE 2.05 -4.06 0.36 -0.82 -0.38 3.11 0.26 -0.11 -3.04 -0.25 3.85

GG 2.05 -4.06 0.36 -0.82 -0.38 2.05 -4.06 0.36 -0.82 -0.38 3.86 EG 3.11 0.26 -0.11 -3.04 -0.25 2.05 -4.06 0.36 -0.82 -0.38 3.87

SG 2.39 -1.07 1.15 -1.39 0.67 2.05 -4.06 0.36 -0.82 -0.38 3.93

GG 2.05 -4.06 0.36 -0.82 -0.38 2.05 -4.06 0.36 -0.82 -0.38 3.94

LG -4.28 -1.3 -1.49 -0.72 0.84 2.05 -4.06 0.36 -0.82 -0.38 3.94

GD 2.05 -4.06 0.36 -0.82 -0.38 3.98 0.93 1.93 -2.46 0.75 3.96

TG 0.75 -2.18 -1.12 -1.46 -0.4 2.05 -4.06 0.36 -0.82 -0.38 4.00

QG 1.75 0.5 -1.44 -1.34 0.66 2.05 -4.06 0.36 -0.82 -0.38 4.00

PG -1.66 0.27 1.84 0.7 2 2.05 -4.06 0.36 -0.82 -0.38 4.23

Table S1B Descriptor scores for tripeptides

Peptide n1z1 n1z2 n1z3 n1z4 n1z5 n2z1 n2z2 n2z3 n2z4 n2z5 n3z1 n3z2 n3z3 n3z4 n3z5 logIC50 LIY -4.28 -1.3 -1.49 -0.72 0.84 -3.89 -1.73 -1.71 -0.84 0.26 -2.54 2.44 0.43 0.04 -1.47 -0.09

GGY 2.05 -4.06 0.36 -0.82 -0.38 2.05 -4.06 0.36 -0.82 -0.38 -2.54 2.44 0.43 0.04 -1.47 0.11

YVA -2.54 2.44 0.43 0.04 -1.47 -2.59 -2.64 -1.54 -0.85 -0.02 0.24 -2.32 0.6 -0.14 1.3 0.15

IEP -3.89 -1.73 -1.71 -0.84 0.26 3.11 0.26 -0.11 -3.04 -0.25 -1.66 0.27 1.84 0.7 2 0.20

MRW -2.85 -0.22 0.47 1.94 -0.98 3.52 2.5 -3.5 1.99 -0.17 -4.36 3.94 0.59 3.44 -1.59 -0.22

IKP -3.89 -1.73 -1.71 -0.84 0.26 2.29 0.89 -2.49 1.49 0.31 -1.66 0.27 1.84 0.7 2 0.23

LSP -4.28 -1.3 -1.49 -0.72 0.84 2.39 -1.07 1.15 -1.39 0.67 -1.66 0.27 1.84 0.7 2 0.23

IMY -3.89 -1.73 -1.71 -0.84 0.26 -2.85 -0.22 0.47 1.94 -0.98 -2.54 2.44 0.43 0.04 -1.47 0.26

IRP -3.89 -1.73 -1.71 -0.84 0.26 3.52 2.5 -3.5 1.99 -0.17 -1.66 0.27 1.84 0.7 2 0.26

IKW -3.89 -1.73 -1.71 -0.84 0.26 2.29 0.89 -2.49 1.49 0.31 -4.36 3.94 0.59 3.44 -1.59 -0.27

LEP -4.28 -1.3 -1.49 -0.72 0.84 3.11 0.26 -0.11 -3.04 -0.25 -1.66 0.27 1.84 0.7 2 0.28

VAP -2.59 -2.64 -1.54 -0.85 -0.02 0.24 -2.32 0.6 -0.14 1.3 -1.66 0.27 1.84 0.7 2 0.30

IVY -3.89 -1.73 -1.71 -0.84 0.26 -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 -0.32

DGL 3.98 0.93 1.93 -2.46 0.75 2.05 -4.06 0.36 -0.82 -0.38 -4.28 -1.3 -1.49 -0.72 0.84 0.33

VRP -2.59 -2.64 -1.54 -0.85 -0.02 3.52 2.5 -3.5 1.99 -0.17 -1.66 0.27 1.84 0.7 2 0.34

GPL 2.05 -4.06 0.36 -0.82 -0.38 -1.66 0.27 1.84 0.7 2 -4.28 -1.3 -1.49 -0.72 0.84 0.35

TKY 0.75 -2.18 -1.12 -1.46 -0.4 2.29 0.89 -2.49 1.49 0.31 -2.54 2.44 0.43 0.04 -1.47 0.36 IVY 2.29 0.89 -2.49 1.49 0.31 -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 0.38

PRY -1.66 0.27 1.84 0.7 2 3.52 2.5 -3.5 1.99 -0.17 -2.54 2.44 0.43 0.04 -1.47 0.40

GPL 2.05 -4.06 0.36 -0.82 -0.38 -1.66 0.27 1.84 0.7 2 -4.28 -1.3 -1.49 -0.72 0.84 0.41

IAP -3.89 -1.73 -1.71 -0.84 0.26 0.24 -2.32 0.6 -0.14 1.3 -1.66 0.27 1.84 0.7 2 0.43

LTF -4.28 -1.3 -1.49 -0.72 0.84 0.75 -2.18 -1.12 -1.46 -0.4 -4.22 1.94 1.06 0.54 -0.62 0.44

LKP -4.28 -1.3 -1.49 -0.72 0.84 2.29 0.89 -2.49 1.49 0.31 -1.66 0.27 1.84 0.7 2 -0.49

LKP -4.28 -1.3 -1.49 -0.72 0.84 2.29 0.89 -2.49 1.49 0.31 -1.66 0.27 1.84 0.7 2 -0.49

LKP -4.28 -1.3 -1.49 -0.72 0.84 2.29 0.89 -2.49 1.49 0.31 -1.66 0.27 1.84 0.7 2 -0.49

AKK 0.24 -2.32 0.6 -0.14 1.3 2.29 0.89 -2.49 1.49 0.31 2.29 0.89 -2.49 1.49 0.31 0.50

GQP 2.05 -4.06 0.36 -0.82 -0.38 1.75 0.5 -1.44 -1.34 0.66 -1.66 0.27 1.84 0.7 2 0.51

LAP -4.28 -1.3 -1.49 -0.72 0.84 0.24 -2.32 0.6 -0.14 1.3 -1.66 0.27 1.84 0.7 2 0.54

IWH -3.89 -1.73 -1.71 -0.84 0.26 -4.36 3.94 0.59 3.44 -1.59 2.47 1.95 0.26 3.9 0.09 0.54

TAP 0.75 -2.18 -1.12 -1.46 -0.4 0.24 -2.32 0.6 -0.14 1.3 -1.66 0.27 1.84 0.7 2 0.54

LRP -4.28 -1.3 -1.49 -0.72 0.84 3.52 2.5 -3.5 1.99 -0.17 -1.66 0.27 1.84 0.7 2 -0.57

FAP -4.22 1.94 1.06 0.54 -0.62 0.24 -2.32 0.6 -0.14 1.3 -1.66 0.27 1.84 0.7 2 0.58

LAY -4.28 -1.3 -1.49 -0.72 0.84 0.24 -2.32 0.6 -0.14 1.3 -2.54 2.44 0.43 0.04 -1.47 0.59

GKV 2.05 -4.06 0.36 -0.82 -0.38 2.29 0.89 -2.49 1.49 0.31 -2.59 -2.64 -1.54 -0.85 -0.02 0.59

VLP -2.59 -2.64 -1.54 -0.85 -0.02 -4.28 -1.3 -1.49 -0.72 0.84 -1.66 0.27 1.84 0.7 2 0.59

YEY -2.54 2.44 0.43 0.04 -1.47 3.11 0.26 -0.11 -3.04 -0.25 -2.54 2.44 0.43 0.04 -1.47 0.60 FAP -4.22 1.94 1.06 0.54 -0.62 0.24 -2.32 0.6 -0.14 1.3 -1.66 0.27 1.84 0.7 2 0.62

GPV 2.05 -4.06 0.36 -0.82 -0.38 -1.66 0.27 1.84 0.7 2 -2.59 -2.64 -1.54 -0.85 -0.02 0.67

DLP 3.98 0.93 1.93 -2.46 0.75 -4.28 -1.3 -1.49 -0.72 0.84 -1.66 0.27 1.84 0.7 2 0.68

IKY -3.89 -1.73 -1.71 -0.84 0.26 2.29 0.89 -2.49 1.49 0.31 -2.54 2.44 0.43 0.04 -1.47 -0.68

IKW -3.89 -1.73 -1.71 -0.84 0.26 2.29 0.89 -2.49 1.49 0.31 -4.36 3.94 0.59 3.44 -1.59 -0.68

IPP -3.89 -1.73 -1.71 -0.84 0.26 -1.66 0.27 1.84 0.7 2 -1.66 0.27 1.84 0.7 2 0.70

LRY -4.28 -1.3 -1.49 -0.72 0.84 3.52 2.5 -3.5 1.99 -0.17 -2.54 2.44 0.43 0.04 -1.47 0.70

HHL 2.47 1.95 0.26 3.9 0.09 2.47 1.95 0.26 3.9 0.09 -4.28 -1.3 -1.49 -0.72 0.84 0.73

LYP -4.28 -1.3 -1.49 -0.72 0.84 -2.54 2.44 0.43 0.04 -1.47 -1.66 0.27 1.84 0.7 2 0.82

IKP -3.89 -1.73 -1.71 -0.84 0.26 2.29 0.89 -2.49 1.49 0.31 -1.66 0.27 1.84 0.7 2 0.84

FNF -4.22 1.94 1.06 0.54 -0.62 3.05 1.62 1.04 -1.15 1.61 -4.22 1.94 1.06 0.54 -0.62 0.84

AVL 0.24 -2.32 0.6 -0.14 1.3 -2.59 -2.64 -1.54 -0.85 -0.02 -4.28 -1.3 -1.49 -0.72 0.84 0.85

MKY -2.85 -0.22 0.47 1.94 -0.98 2.29 0.89 -2.49 1.49 0.31 -2.54 2.44 0.43 0.04 -1.47 0.86

VIY -2.59 -2.64 -1.54 -0.85 -0.02 -3.89 -1.73 -1.71 -0.84 0.26 -2.54 2.44 0.43 0.04 -1.47 0.88

SVY 2.39 -1.07 1.15 -1.39 0.67 -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 0.91

VQV -2.59 -2.64 -1.54 -0.85 -0.02 1.75 0.5 -1.44 -1.34 0.66 -2.59 -2.64 -1.54 -0.85 -0.02 0.94

GLY 2.05 -4.06 0.36 -0.82 -0.38 -4.28 -1.3 -1.49 -0.72 0.84 -2.54 2.44 0.43 0.04 -1.47 0.95

VPP -2.59 -2.64 -1.54 -0.85 -0.02 -1.66 0.27 1.84 0.7 2 -1.66 0.27 1.84 0.7 2 0.95

VWY -2.59 -2.64 -1.54 -0.85 -0.02 -4.36 3.94 0.59 3.44 -1.59 -2.54 2.44 0.43 0.04 -1.47 0.97 MPP -2.85 -0.22 0.47 1.94 -0.98 -1.66 0.27 1.84 0.7 2 -1.66 0.27 1.84 0.7 2 0.98

LPP -4.28 -1.3 -1.49 -0.72 0.84 -1.66 0.27 1.84 0.7 2 -1.66 0.27 1.84 0.7 2 0.98

VSP -2.59 -2.64 -1.54 -0.85 -0.02 2.39 -1.07 1.15 -1.39 0.67 -1.66 0.27 1.84 0.7 2 1.00

FEP -4.22 1.94 1.06 0.54 -0.62 3.11 0.26 -0.11 -3.04 -0.25 -1.66 0.27 1.84 0.7 2 1.08

LVL -4.28 -1.3 -1.49 -0.72 0.84 -2.59 -2.64 -1.54 -0.85 -0.02 -4.28 -1.3 -1.49 -0.72 0.84 1.09

LWA -4.28 -1.3 -1.49 -0.72 0.84 -4.36 3.94 0.59 3.44 -1.59 0.24 -2.32 0.6 -0.14 1.3 1.10

IAY -3.89 -1.73 -1.71 -0.84 0.26 0.24 -2.32 0.6 -0.14 1.3 -2.54 2.44 0.43 0.04 -1.47 1.10

IRA -3.89 -1.73 -1.71 -0.84 0.26 3.52 2.5 -3.5 1.99 -0.17 0.24 -2.32 0.6 -0.14 1.3 1.11

LAA -4.28 -1.3 -1.49 -0.72 0.84 0.24 -2.32 0.6 -0.14 1.3 0.24 -2.32 0.6 -0.14 1.3 1.11

LVQ -4.28 -1.3 -1.49 -0.72 0.84 -2.59 -2.64 -1.54 -0.85 -0.02 1.75 0.5 -1.44 -1.34 0.66 1.15

LVR -4.28 -1.3 -1.49 -0.72 0.84 -2.59 -2.64 -1.54 -0.85 -0.02 3.52 2.5 -3.5 1.99 -0.17 1.15

TVY 0.75 -2.18 -1.12 -1.46 -0.4 -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 1.18

PSY -1.66 0.27 1.84 0.7 2 2.39 -1.07 1.15 -1.39 0.67 -2.54 2.44 0.43 0.04 -1.47 1.20

LLP -4.28 -1.3 -1.49 -0.72 0.84 -4.28 -1.3 -1.49 -0.72 0.84 -1.66 0.27 1.84 0.7 2 1.20

YPR -2.54 2.44 0.43 0.04 -1.47 -1.66 0.27 1.84 0.7 2 3.52 2.5 -3.5 1.99 -0.17 1.22

GPM 2.05 -4.06 0.36 -0.82 -0.38 -1.66 0.27 1.84 0.7 2 -2.85 -0.22 0.47 1.94 -0.98 1.23

GPP 2.05 -4.06 0.36 -0.82 -0.38 -1.66 0.27 1.84 0.7 2 -1.66 0.27 1.84 0.7 2 1.25

FYN -4.22 1.94 1.06 0.54 -0.62 -2.54 2.44 0.43 0.04 -1.47 3.05 1.62 1.04 -1.15 1.61 1.26

GPR 2.05 -4.06 0.36 -0.82 -0.38 -1.66 0.27 1.84 0.7 2 3.52 2.5 -3.5 1.99 -0.17 1.30 VSW -2.59 -2.64 -1.54 -0.85 -0.02 2.39 -1.07 1.15 -1.39 0.67 -4.36 3.94 0.59 3.44 -1.59 1.37

IAP -3.89 -1.73 -1.71 -0.84 0.26 0.24 -2.32 0.6 -0.14 1.3 -1.66 0.27 1.84 0.7 2 1.40

LAY -4.28 -1.3 -1.49 -0.72 0.84 0.24 -2.32 0.6 -0.14 1.3 -2.54 2.44 0.43 0.04 -1.47 1.40

FAL -4.22 1.94 1.06 0.54 -0.62 0.24 -2.32 0.6 -0.14 1.3 -4.28 -1.3 -1.49 -0.72 0.84 1.42

VGP -2.59 -2.64 -1.54 -0.85 -0.02 2.05 -4.06 0.36 -0.82 -0.38 -1.66 0.27 1.84 0.7 2 1.42

RIY 3.52 2.5 -3.5 1.99 -0.17 -3.89 -1.73 -1.71 -0.84 0.26 -2.54 2.44 0.43 0.04 -1.47 1.45

LGI -4.28 -1.3 -1.49 -0.72 0.84 2.05 -4.06 0.36 -0.82 -0.38 -3.89 -1.73 -1.71 -0.84 0.26 1.46

VLY -2.59 -2.64 -1.54 -0.85 -0.02 -4.28 -1.3 -1.49 -0.72 0.84 -2.54 2.44 0.43 0.04 -1.47 1.49

GSH 2.05 -4.06 0.36 -0.82 -0.38 2.39 -1.07 1.15 -1.39 0.67 2.47 1.95 0.26 3.9 0.09 1.51

ILP -3.89 -1.73 -1.71 -0.84 0.26 -4.28 -1.3 -1.49 -0.72 0.84 -1.66 0.27 1.84 0.7 2 1.51

IAQ -3.89 -1.73 -1.71 -0.84 0.26 0.24 -2.32 0.6 -0.14 1.3 1.75 0.5 -1.44 -1.34 0.66 1.54

VVF -2.59 -2.64 -1.54 -0.85 -0.02 -2.59 -2.64 -1.54 -0.85 -0.02 -4.22 1.94 1.06 0.54 -0.62 1.55

PLW -1.66 0.27 1.84 0.7 2 -4.28 -1.3 -1.49 -0.72 0.84 -4.36 3.94 0.59 3.44 -1.59 1.56

FFL -4.22 1.94 1.06 0.54 -0.62 -4.22 1.94 1.06 0.54 -0.62 -4.28 -1.3 -1.49 -0.72 0.84 1.57

LPF -4.28 -1.3 -1.49 -0.72 0.84 -1.66 0.27 1.84 0.7 2 -4.22 1.94 1.06 0.54 -0.62 1.60

LEP -4.28 -1.3 -1.49 -0.72 0.84 3.11 0.26 -0.11 -3.04 -0.25 -1.66 0.27 1.84 0.7 2 1.63

IFL -3.89 -1.73 -1.71 -0.84 0.26 -4.22 1.94 1.06 0.54 -0.62 -4.28 -1.3 -1.49 -0.72 0.84 1.65

ITF -3.89 -1.73 -1.71 -0.84 0.26 0.75 -2.18 -1.12 -1.46 -0.4 -4.22 1.94 1.06 0.54 -0.62 1.69

HHL 2.47 1.95 0.26 3.9 0.09 2.47 1.95 0.26 3.9 0.09 -4.28 -1.3 -1.49 -0.72 0.84 1.73 HLL 2.47 1.95 0.26 3.9 0.09 -4.28 -1.3 -1.49 -0.72 0.84 -4.28 -1.3 -1.49 -0.72 0.84 1.76

LLP -4.28 -1.3 -1.49 -0.72 0.84 -4.28 -1.3 -1.49 -0.72 0.84 -1.66 0.27 1.84 0.7 2 1.76

AQL 0.24 -2.32 0.6 -0.14 1.3 1.75 0.5 -1.44 -1.34 0.66 -4.28 -1.3 -1.49 -0.72 0.84 1.76

LNP -4.28 -1.3 -1.49 -0.72 0.84 3.05 1.62 1.04 -1.15 1.61 -1.66 0.27 1.84 0.7 2 1.76

RPP 3.52 2.5 -3.5 1.99 -0.17 -1.66 0.27 1.84 0.7 2 -1.66 0.27 1.84 0.7 2 1.78

AFL 0.24 -2.32 0.6 -0.14 1.3 -4.22 1.94 1.06 0.54 -0.62 -4.28 -1.3 -1.49 -0.72 0.84 1.80

MNP -2.85 -0.22 0.47 1.94 -0.98 3.05 1.62 1.04 -1.15 1.61 -1.66 0.27 1.84 0.7 2 1.82

LLF -4.28 -1.3 -1.49 -0.72 0.84 -4.28 -1.3 -1.49 -0.72 0.84 -4.22 1.94 1.06 0.54 -0.62 1.90

LNY -4.28 -1.3 -1.49 -0.72 0.84 3.05 1.62 1.04 -1.15 1.61 -2.54 2.44 0.43 0.04 -1.47 1.91

VLP -2.59 -2.64 -1.54 -0.85 -0.02 -4.28 -1.3 -1.49 -0.72 0.84 -1.66 0.27 1.84 0.7 2 1.91

IPP -3.89 -1.73 -1.71 -0.84 0.26 -1.66 0.27 1.84 0.7 2 -1.66 0.27 1.84 0.7 2 1.92

AGP 0.24 -2.32 0.6 -0.14 1.3 2.05 -4.06 0.36 -0.82 -0.38 -1.66 0.27 1.84 0.7 2 1.95

IVQ -3.89 -1.73 -1.71 -0.84 0.26 -2.59 -2.64 -1.54 -0.85 -0.02 1.75 0.5 -1.44 -1.34 0.66 1.98

LEE -4.28 -1.3 -1.49 -0.72 0.84 3.11 0.26 -0.11 -3.04 -0.25 3.11 0.26 -0.11 -3.04 -0.25 2.00

LEE -4.28 -1.3 -1.49 -0.72 0.84 3.11 0.26 -0.11 -3.04 -0.25 3.11 0.26 -0.11 -3.04 -0.25 2.00

VTR -2.59 -2.64 -1.54 -0.85 -0.02 0.75 -2.18 -1.12 -1.46 -0.4 3.52 2.5 -3.5 1.99 -0.17 2.13

IPA -3.89 -1.73 -1.71 -0.84 0.26 -1.66 0.27 1.84 0.7 2 0.24 -2.32 0.6 -0.14 1.3 2.15

FGK -4.22 1.94 1.06 0.54 -0.62 2.05 -4.06 0.36 -0.82 -0.38 2.29 0.89 -2.49 1.49 0.31 2.20

LKL -4.28 -1.3 -1.49 -0.72 0.84 2.29 0.89 -2.49 1.49 0.31 -4.28 -1.3 -1.49 -0.72 0.84 2.27 RVY 3.52 2.5 -3.5 1.99 -0.17 -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 2.31

TNP 0.75 -2.18 -1.12 -1.46 -0.4 3.05 1.62 1.04 -1.15 1.61 -1.66 0.27 1.84 0.7 2 2.32

PYP -1.66 0.27 1.84 0.7 2 -2.54 2.44 0.43 0.04 -1.47 -1.66 0.27 1.84 0.7 2 2.34

ALP 0.24 -2.32 0.6 -0.14 1.3 -4.28 -1.3 -1.49 -0.72 0.84 -1.66 0.27 1.84 0.7 2 2.38

NPP 3.05 1.62 1.04 -1.15 1.61 -1.66 0.27 1.84 0.7 2 -1.66 0.27 1.84 0.7 2 2.46

VYP -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 -1.66 0.27 1.84 0.7 2 2.46

RFH 3.52 2.5 -3.5 1.99 -0.17 -4.22 1.94 1.06 0.54 -0.62 2.47 1.95 0.26 3.9 0.09 2.52

FNE -4.22 1.94 1.06 0.54 -0.62 3.05 1.62 1.04 -1.15 1.61 3.11 0.26 -0.11 -3.04 -0.25 2.53

AVP 0.24 -2.32 0.6 -0.14 1.3 -2.59 -2.64 -1.54 -0.85 -0.02 -1.66 0.27 1.84 0.7 2 2.53

GKP 2.05 -4.06 0.36 -0.82 -0.38 2.29 0.89 -2.49 1.49 0.31 2.29 0.89 -2.49 1.49 0.31 2.55

FDK -4.22 1.94 1.06 0.54 -0.62 3.98 0.93 1.93 -2.46 0.75 2.29 0.89 -2.49 1.49 0.31 2.59

GVY 2.05 -4.06 0.36 -0.82 -0.38 -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 2.60

YGL -2.54 2.44 0.43 0.04 -1.47 2.05 -4.06 0.36 -0.82 -0.38 -4.28 -1.3 -1.49 -0.72 0.84 2.61

EAP 3.11 0.26 -0.11 -3.04 -0.25 0.24 -2.32 0.6 -0.14 1.3 -1.66 0.27 1.84 0.7 2 2.61

AGP 0.24 -2.32 0.6 -0.14 1.3 2.05 -4.06 0.36 -0.82 -0.38 -1.66 0.27 1.84 0.7 2 2.75

ITT -3.89 -1.73 -1.71 -0.84 0.26 0.75 -2.18 -1.12 -1.46 -0.4 0.75 -2.18 -1.12 -1.46 -0.4 2.83

TTN 0.75 -2.18 -1.12 -1.46 -0.4 0.75 -2.18 -1.12 -1.46 -0.4 3.05 1.62 1.04 -1.15 1.61 2.83

HQG 2.47 1.95 0.26 3.9 0.09 1.75 0.5 -1.44 -1.34 0.66 2.05 -4.06 0.36 -0.82 -0.38 2.87

HHT 2.47 1.95 0.26 3.9 0.09 2.47 1.95 0.26 3.9 0.09 0.75 -2.18 -1.12 -1.46 -0.4 2.90 LEK -4.28 -1.3 -1.49 -0.72 0.84 3.11 0.26 -0.11 -3.04 -0.25 2.29 0.89 -2.49 1.49 0.31 2.90

HIR 2.47 1.95 0.26 3.9 0.09 -3.89 -1.73 -1.71 -0.84 0.26 3.52 2.5 -3.5 1.99 -0.17 2.98

PPK -1.66 0.27 1.84 0.7 2 -1.66 0.27 1.84 0.7 2 2.29 0.89 -2.49 1.49 0.31 3.00

RML 3.52 2.5 -3.5 1.99 -0.17 -2.85 -0.22 0.47 1.94 -0.98 -4.28 -1.3 -1.49 -0.72 0.84 3.01

VFK -2.59 -2.64 -1.54 -0.85 -0.02 -4.22 1.94 1.06 0.54 -0.62 2.29 0.89 -2.49 1.49 0.31 3.01

SVY 2.39 -1.07 1.15 -1.39 0.67 -2.59 -2.64 -1.54 -0.85 -0.02 -2.54 2.44 0.43 0.04 -1.47 3.23

AQK 0.24 -2.32 0.6 -0.14 1.3 1.75 0.5 -1.44 -1.34 0.66 2.29 0.89 -2.49 1.49 0.31 3.26

RPK 3.52 2.5 -3.5 1.99 -0.17 -1.66 0.27 1.84 0.7 2 2.29 0.89 -2.49 1.49 0.31 3.27

DYG 3.98 0.93 1.93 -2.46 0.75 -2.54 2.44 0.43 0.04 -1.47 2.05 -4.06 0.36 -0.82 -0.38 3.43

YGG -2.54 2.44 0.43 0.04 -1.47 2.05 -4.06 0.36 -0.82 -0.38 2.05 -4.06 0.36 -0.82 -0.38 4.00

Table S2 Summary of predicted activities of ACE inhibitory peptide sequences from bovine collagen

Collagen alpha-1(I) Collagen alpha-2(I) BIOPEP BIOPEP No. of peptide Sequence IC (μM) No. of peptide Sequence IC (μM) ID 50 ID 50 7831 1 LRY 1.19 7743 1 LRP 1.5 7595 4 GRP 1.88 7618 2 GRP 1.88 7743 1 IRA 4.04 7751 1 IRA 4.04 7649 1 YW 4.72 7685 1 YW 4.72 7647 1 VWY 5.41 7831 1 SHP 6.21 7610 2 VW 6.71 7683 1 VW 6.71 7828 1 LTF 6.83 7838 1 IAP 10.15 7742 1 VAY 8.04 8227 1 IEY 15.01 7684 1 VAP 10.19 7752 1 FY 15.15 7685 1 IPP 11.16 7742 1 GGY 16.86 8254 1 IEY 15.01 7820 1 LGP 17.05 7683 1 GGY 16.86 3492 26 GPP 17.9 3385 37 GPP 17.9 3563 13 GLP 19.76 7827 1 MKG 19.7 7819 1 GPL 25.01 3546 10 GLP 19.76 7697 1 RF 26.51 7638 1 MF 20.39 7844 1 EW 26.79 7632 1 VF 24.55 7698 1 VY 28.82 7680 1 RF 26.51 7585 7 GEP 30.5 7681 1 VY 28.82 7582 7 GPV 48.49 7554 8 GEP 30.5 7827 1 AF 61.4 7609 2 RY 31.13 3502 25 GPA 61.46 7697 1 LAA 44.58 7810 1 AY 72.08 7613 2 GPV 48.49 7833 1 NF 75.99 8227 1 TF 59.32 7601 4 FP 83.65 3486 29 GPA 61.46 7586 6 PGL 86.58 7751 1 AY 72.08 7596 5 IP 96.25 7833 1 NF 75.99 7834 1 SF 108.24 7586 5 FP 83.65 7506 12 LPG 111.26 7587 5 PGL 86.58 7624 2 EY 115.12 7615 2 IP 96.25 7508 11 GF 137.96 7830 1 YGG 102.89 7837 1 CF 149 7622 2 SF 108.24 7606 3 GY 161.97 3553 9 LPG 111.26 7595 5 IR 171.16 7604 3 EY 115.12 7608 3 RP 171.9 7834 1 SY 127.08 7841 1 LN 174.09 3537 11 GF 137.96 3489 29 PP 195.11 7836 1 CF 149 7612 3 KP 202 7590 5 VP 159.13 7615 3 LQ 254.75 7612 2 GY 161.97 7600 4 RL 258.17

Table S2 continued

collagen alpha-1(I) collagen alpha-2(I) BIOPEP BIOPEP No. of peptide Sequence IC (μM) No. of peptide Sequence IC (μM) ID 50 ID 50 7608 2 IR 171.16 7680 2 HP 261.46 7588 5 RP 171.9 7829 1 VR 282.99 7840 1 LN 174.09 7620 2 PL 293.02 3380 45 PP 195.11 7836 1 YK 304.18 7605 3 KP 202 7817 1 YP 340.29 7607 3 LQ 254.75 7562 8 PR 346.95 7598 3 RL 258.17 7828 1 KR 359.23 7601 3 VR 282.99 7628 2 IE 367.74 7752 1 PL 293.02 7511 10 IG 368.17 7835 1 YK 304.18 3556 14 AP 398.06 3532 11 PR 346.95 7826 1 PLG 404.43 7810 1 KR 359.23 7621 2 VK 432.37 7606 3 IE 367.74 7588 6 LA 515.49 7596 4 IG 368.17 7607 3 IA 605.59 8185 1 AH 376.24 7599 5 PQ 606.64 3487 24 AP 398.06 7840 1 VE 608 8193 1 ME 505.07 7510 11 AR 707.86 7616 2 LA 515.49 8193 1 HK 710.4 7618 2 GM 519.47 7681 2 AI 755.86 7614 2 IA 605.59 7832 1 QK 765.7 7507 9 PQ 606.64 7617 3 KE 771.79 7545 8 AR 707.86 7590 6 GH 845.38 7625 2 AI 755.86 3257 108 GP 894.42 7832 1 QK 765.7 7609 3 RA 1081.56 7624 2 KE 771.79 7625 2 EI 1207.12 7820 1 WG 837.97 7605 4 KA 1270.98 7619 2 GH 845.38 7636 2 PT 1312.08 3257 118 GP 894.42 7611 3 NK 1338.54 7617 2 RA 1081.56 3488 29 GL 1343.29 7623 2 EI 1207.12 7598 5 LG 1485.9 7603 3 KA 1270.98 7622 2 FG 1516.98 7585 6 PT 1312.08 7509 11 GR 1590.52 3489 21 GL 1343.29 7581 8 GI 1698.36 7839 1 TE 1469.64 7616 3 EK 1727.23 7621 2 LG 1485.9 7603 4 MG 2397.51 7817 1 FG 1516.98 7513 10 GK 2430.19 7512 8 GR 1590.52 7614 3 EV 2431.64 7582 6 GI 1698.36 7554 9 AA 2504.49 7597 4 EK 1727.23 7842 1 TQ 2586.84

Table S2 continued

collagen alpha-1(I) collagen alpha-2(I) BIOPEP BIOPEP No. of peptide Sequence IC (μM) No. of peptide Sequence IC (μM) ID 50 ID 50 7599 3 YG 2030.39 7545 10 GD 2718.69 7600 3 MG 2397.51 7584 7 GQ 2781.06 3563 9 GK 2430.19 3506 25 VG 2886.16 7837 1 EV 2431.64 3486 37 GE 3417.2 7591 5 AA 2504.49 7512 10 GV 3421.29 7841 1 TQ 2586.84 3258 95 PG 3538.43 3506 17 GD 2718.69 3515 23 KG 3663.62 3539 11 GQ 2781.06 7604 4 EA 3999.72 7511 8 VG 2886.16 7623 2 HG 4741.87 3384 42 GE 3417.2 3537 16 GS 4807.18 7513 8 GV 3421.29 3543 16 QG 5111.05 3258 114 PG 3538.43 3378 45 GA 5627.56 3488 23 KG 3663.62 7610 3 DA 5992.53 7594 5 EA 3999.72 7591 6 GT 6015.06 7826 1 HG 4741.87 3666 13 TG 6976.22 3502 17 GS 4807.18 3342 47 AG 7219.38 3492 18 QG 5111.05 7507 12 NG 8934.7 3342 59 GA 5627.56 7594 6 EG 11529.22 7584 6 DA 5992.53 3532 20 SG 12727.41 7620 2 GT 6015.06 7558 9 GG 16221.46 3515 17 TG 6976.22 3547 16 DG 17273.49 3378 57 AG 7219.38 7581 7 NG 8934.7 3547 10 EG 11529.22 3521 16 SG 12727.41 7562 7 GG 16221.46 3522 16 DG 17273.49

Paper III

Fu, Y., Young, J. F., Dalsgaard, T. K., Lametsch, R., Aluko, R. E., & Therkildsen, M. Angiotensin I–converting enzyme–inhibitory peptides from bovine collagen: insights into inhibitory mechanism and transepithelial transport. Food Research International. Submitted.

*Manuscript Click here to view linked References

1 Angiotensin I–converting enzyme–inhibitory peptides from bovine collagen:

2 insights into inhibitory mechanism and transepithelial transport

3 Yu Fu1, Jette Feveile Young1, Martin Krøyer Rasmussen1, Trine Kastrup Dalsgaard1, René Lametsch2, 4 Rotimi E. Aluko3, Margrethe Therkildsen1*

5

6 Author affiliations:

1 7 Department of Food Science, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark

8 2 Department of Food Science, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958 9 Frederiksberg C, Denmark

10 3Department of Human Nutritional Sciences, University of Manitoba, Winnipeg, MB, Canada R3T

11 2N2

12

13

14

15 *Corresponding author:

16 Margrethe Therkildsen

17 Department of Food Science, Aarhus University, Blichers Allé 20, Postbox 50, 8830 Tjele, Denmark

18 E-mail: [email protected], Tel: +45 87158007, Fax: +45 87154891

19

20

21

1

22 ABSTRACT

23 The inhibitory mechanism and transepithelial transport of angiotensin I-converting enzyme (ACE)-

24 inhibitory peptides (VGPV and GPRGF) derived from Alcalase- and papain-hydrolyzed bovine

25 collagen were investigated. The inhibitory mechanism of VGPV and GPRGF was experimentally

26 determined to be non-competitive; the results were supported by molecular docking data. In silico and

27 in vitro gastrointestinal digestion indicated that VGPV remained resistant to digestive enzymes, while

28 GPRGF was degraded into smaller ACE-inhibitory peptides (GPR and GF) with documented IC50

29 values lower than GPRGF. VGPV and GPRGF were transported across the monolayer of human

30 intestinal Caco-2 cells through paracellular pathway and retained their ACE-inhibitory effects. These

31 results highlight VGPV and GPRGF as bioavailable ACE-inhibitory peptides that could serve as bio-

32 functional ingredients in the food and nutrition industry.

33 Keywords: Collagen peptides; angiotensin I-converting enzyme; digestive enzymes; non-competitive

34 inhibition; molecular docking; transepithelial transport

2

35 1. Introduction

36 Hypertension, one of the key risk factors closely related to cardiovascular diseases, is estimated to

37 affect nearly one third of the world’s population (Sharp et al., 2011; Daien et al., 2012). The renin-

38 angiotensin system (RAS) acts as the main pathway responsible for regulating blood pressure and

39 ensuring fluid homeostasis. Within RAS, renin can catalyze the conversion of angiotensinogen into the

40 inactive angiotensin I that is further converted to angiotensin II by angiotensin I-converting enzyme

41 (ACE). Angiotensin II is a potent vasoconstrictor peptide that regulates blood pressure (Crowley &

42 Coffman, 2012). Moreover, ACE also inactivates and degrades the peptide bradykinin, a vasodilator in

43 the kallikrein–kinin system that contributes to blood pressure reduction (Fitzgerald, 2011). In recent

44 years, effective ACE activity inhibition has been recognized as a suitable approach for lowering blood

45 pressure. However, synthesized anti-hypertensive drugs such as Captopril, Enalapril, Lisinopril and

46 Alacepril, have been reported to exhibit several undesirable side effects (e.g. dry cough and edema) or

47 other health complications following long-term administration (Israili & Hall, 1992; Girgih, He, &

48 Aluko, 2014). Therefore, utilization of natural source-derived peptides as ACE inhibitors with fewer

49 side effects for the management of hypertension has become increasingly attractive.

50 The meat industry generates large quantities of slaughter by-products during processing, which are

51 usually utilized for low-value purposes or discarded as waste (Mokrejs et al., 2009; Mora, Reig, &

52 Toldra, 2014). Bovine connective tissue containing high amounts of collagen is a major constituent of

53 meat slaughter as well as processing waste-products (Jayathilakan, Sultana, Radhakrishna, & Bawa,

54 2012). Thus, conversion of bovine collagen into high value-added ingredients with bio-functional

55 properties, e.g. collagen peptides, can contribute to a high benefit-to-cost rate. Although collagen

56 peptides derived from other sources are reported to possess potentials as ACE inhibitors (Gómez-

3

57 Guillén, Giménez, López-Caballero, & Montero, 2011) and an ACE-inhibitory pentapeptide (GPRGF)

58 from papain-hydrolyzed bovine collagen was previously identified (Fu et al. 2016), the ACE-inhibitory

59 mechanism and transepithelial transport of collagen peptides have not been previously reported.

60 Therefore, the aim of this study was to explore the inhibitory mechanism and transepithelial transport

61 of GPRGF and VGPV, a potential ACE-inhibitory peptide released from Alcalase-hydrolyzed collagen.

62 In the present work, and molecular docking studies were performed to understand the

63 mode of ACE inhibition by GPRGF and VGPV. The Caco-2 cell line that is derived from human colon

64 adenocarcinoma and normally cultivated as a model for small intestinal transport of drugs and bioactive

65 compounds (Hidalgo, Raub, & Borchardt, 1989) was employed to predict the absorption of collagen

66 peptides (GPRGF and VGPV) in the small intestine.

67 2. Materials and methods

68 2.1 Materials

69 The bovine connective tissue was obtained from the nuchal ligament of bovine carcasses at a

70 commercial slaughter house (Danish Crown, Aalborg, Denmark). Abz-Gly-p-nitro-Phe-Pro-OH (ACE

71 substrate) was obtained from Bachem (Bubendorf, Switzerland). Angiotensin I-converting enzyme

72 from rabbit lung, 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), 2-(N-

73 morpholino) ethanesulfonic acid (MES), glycylsarcosine (Gly-Sar), wortamanin (a transcytosis

74 inhibitor), cytochalasin D (tight junction disruptor), Lucifer yellow and dimethyl sulfoxide (DMSO),

75 were purchased from Sigma Chemical Co. (St. Louis, MO, USA); Alcalase® (13.7 U/mL) was obtained

76 from Millipore (Billerica, USA). Caco-2 cells were purchased from American Type Tissue Collection

77 (Manassas, VA, USA). Dulbecco’s Modified Eagle Medium (DMEM), fetal bovine serum (FBS) and

4

78 Hanks balanced salt solution (HBSS) was from Gibco (Naerum, DK). All other chemicals and reagents

79 were of analytical grade and commercially available. The amino acid sequences of collagen peptides

80 (VGPV and GPRGF) were synthesized (purity above 95%) using Fmoc solid phase peptide approach

81 (Schafer-N ApS, Copenhagen, Denmark).

82 2.2 Purification and identification of collagen peptide

83 Collagen hydrolysate was generated through Alcalase hydrolysis of the pepsin-solubilized collagen

84 extracted from bovine connective tissue, followed by a 2-step purification process (Fu, Young,

85 Dalsgaard, & Therkildsen, 2015). The resultant collagen peptide fraction displaying the most active

86 ACE-inhibitory activities was further purified using our previous RP-HPLC methods (Fu et al., 2016).

87 with and In brief, peptide elution was carried out using a C18 column and a non-linear gradient method

88 from solvent A (0.1% trifluoroacetic acid in Milli-Q water) to solvent B (0.1% trifluoroacetic acid in 90%

89 acetonitrile). Flow rate was fixed at 2.5 mL/min and monitoring absorbance was at 215 nm.

90 The amino acid sequences of the purified peptides from Alcalase-catalyzed group and the media from

91 apical and basolateral chambers of Caco-2 cell monolayer were analyzed according to Fu et al (2016).

92 using mass spectrometer coupled with a Dionex 3000 RSLC UHPLC system (ThermoFisher Scientific,

93 Hvidovre, Denmark). Briefly, the samples were loaded on an Aeris PEPTIDE 1.7 μm XB-C18, 150 ×

94 2.1 mm column (Phenomenex, Værløse, Denmark). The peptides were eluted with chromatographic

95 gradient ranging from 0−60 % solvent B (80 % acitonitril, 0.1 % formic acid) for 60 min at a flow rate

96 of 250 µl/min. A survey MS scan (200−2000 m/z) was acquired in the Orbitrap analyzer with a

97 resolution of 70,000 at 400 m/z. The peptides were subjected to higher-energy collisional dissociation

98 (HCD) MS/MS (normalized collision energy = 28; 3500 resolution at 400 m/z). The obtained MS/MS

5

99 data were further analyzed by PEAKS de novo sequencing (PEAKS Studio 7.0, BSI, Waterloo, ON,

100 Canada). The peptides with average local confidence (ALC) over 75% were selected for further

101 analysis.

102 2.3 Measurement of ACE-inhibitory activity and kinetics

103 ACE-inhibitory activity was determined according to the approach of Petrat-Melin et al. (2015). The

104 IC50 values of the most potent ACE-inhibitory peptides were estimated by nonlinear regression-global

105 curve fitting (Sigmaplot 11, Systat Software Inc.). In addition, the ACE inhibition kinetics studies were

106 performed using 0.025, 0.05, 0.1, 0.2 and 1.0 mol/L of substrate in the absence or presence of collagen

107 peptides. The Km and Vmax values for the reaction at different concentrations of collagen peptides were

108 calculated according to Lineweaver–Burk plots.

109 2.4 In silico and in vitro digestion of the synthesised peptides

110 The active collagen peptide sequences (VGPV and GPRGF) were evaluated for cleavage sites and

111 susceptibility to gastrointestinal digestion using the program Expasy PeptideCutter tool. In silico

112 digestion of collagen peptides was performed using proteases, including pepsin (pH 2.0 and pH > 1.3),

113 chymotrypsin (high and low specificity) and trypsin in order to predict the peptide stability towards the

114 gastrointestinal digestion. In vitro digestion of GPRGF by trypsin was performed according to

115 Shanmugam, Kapila, Sonfack, & Kapila (2015) with slight modifications. VGPV was not included for

116 in vitro proteolysis due to the lack of enzyme cleavage sites according to the in silico results. Briefly,

117 trypsin was added to the pre-incubated GPRGF solution (5 mg/mL, pH 8.0 containing 0.03 mol/L NaCl)

118 at the ratio of 1:100 and digested for 4 h at 37 °C. The hydrolysis was monitored based on assessment

6

119 of free amino concentrations by determining the amount of N-terminal amines using fluorescamine

120 (Petrat-Melin et al., 2015).

121 2.5 Molecular docking

122 Molecular docking between collagen peptides and ACE was carried out with aid of Accelrys Discovery

123 Studio software 2.5 (DS 2.5, Accelrys Software Inc., San Diego, USA) as previously described by He,

124 Aluko, & Ju (2014) with slight modifications. The crystal structure of human ACE was obtained from

125 (1O8A.pdb). The molecular structures of collagen peptides (VGPV and GPRGF)

126 were generated with DS 2.5 and energy was minimized with the CHARMm program. Automated

127 molecular docking was performed by the partial flexibility CDocker protocol. A with a

128 radius of 15 Å and coordinates x: 36.189, y: 43.643 and z: 55.175 were employed. The molecular

129 docking was evaluated based on the scores and binding energy values aiming at getting the best peptide

130 poses. Also, DS 2.5 software was applied to identify the hydrogen bonds and the coordination

131 interactions between some of the residues within the ACE active site.

132 2.6 Caco-2 cell culture

133 Caco-2 (a human colon carcinoma-derived cell line) was cultured in DMEM with 10% FBS, 100 U/mL

134 penicillin, 100 μg/mL streptomycin and 1% non-essential amino acids. The cells were incubated in a

135 fully humidified atmosphere at 37 °C, 95% air and 5% CO2. Cell culture medium was replaced every

136 two days, and cells were sub-cultured at split ratio of 1:4 by trypsinization. Caco-2 cells were seeded (1

137 × 105 cells) on polycarbonate inserts (0.6 cm2, 0.4 μm pore size, Merck-Millipore, Darmstadt, Germany)

138 at a density of 1 × 105 cells/insert and kept in 24-well plates. Cells were allowed to proliferate and

139 differentiate into a cell monolayer for 21 days. The integrity of the cell monolayer was assessed by

7

140 determination of the transepithelial electrical resistance (TEER) using an ERS-2 Volt-ohm meter

141 (Merck-Millipore, Darmstadt, Germany) and Lucifer yellow permeation. Only cell monolayers with

142 TEER values (Hidalgo et al., 1989) higher than 250 Ω·cm2 and permeability of Lucifer yellow (< 5%)

143 were used for transport experiments (Dempe, Scheerle, Pfeiffer, & Metzler, 2013).

144 2.7 Cytotoxicity evaluation of collagen peptides

145 The cytotoxicity of collagen peptides in Caco-2 cells was evaluated by MTT assay (Mosmann, 1983).

146 Caco-2 cells grown in 96-well culture plates were incubated with 0.5-2.0 mM of VGPV or GPRGF,

147 respectively at 37 °C in 5% CO2 for 4 h. Thereafter, 20 μL MTT (5 mg/mL) was added to each well for

148 further incubation of 3 h. The culture medium was gently removed and replaced with 150 μL of DMSO

149 to dissolve cell pellets. The absorbance at 590 nm was measured using a multiscan BioTek Gen 5

150 microplate reader.

151 2.8 Transepithelial transport of collagen peptides

152 The transepithelial transport of VGPV and GPRGF were evaluated using intestinal Caco-2 cell

153 monolayer according to Miguel et al. (2008) with a minor modification. The HBSS buffer was adjusted

154 to pH 6.0 using 10 mM MES and pH 7.4 with NaOH. Prior to transport assay, the Caco-2 cell

155 monolayers were washed twice with HBSS (pH 7.4) and then equilibrated for 30 min 37 °C in the

156 incubator. Afterwards, the medium on the apical side (AP) was replaced by 0.5 mM of VGPV or

157 GPRGF dissolved in HBSS (pH 7.4 or 6.0). After 2 h incubation at 37°C, both the solution on the AP

158 and basolateral side (BL) were collected and subjected to mass spectrometry analysis and measurement

159 of their ACE-inhibitory activities. In order to check integrity of the Caco-2 monolayer, the AP and BL

160 sides were rinsed and replaced by 0.4 mL of HBSS containing 8.0 μM Lucifer yellow in AP and HBSS

8

161 in BL (pH 7.4), respectively. After incubation for another 2 h, the BL solution was collected for

162 analysis of permeability of Lucifer yellow.

163 The transport mechanism of VGPV and GPRGF across Caco-2 cell monolayers was studied by

164 employment of transport modulators according to Miguel et al. (2008). In brief, prior to the transport

165 assay, the Caco-2 cell monolayer was preincubated with Gly-Sar (a peptide transport PepT 1 substrate,

166 10 mM), wortmannin (a transcytosis inhibitor, 500 nM) or cytochalasin D (a tight junction disruptor,

167 0.5 μg/mL) for 30 min. Wortmannin, and cytochalasin D were dissolved in DMSO (the final

168 concentration of DMSO in HBSS was 0.044%), while Gly-Sar was dissolved in HBSS. In addition, the

169 two collagen peptides at the same concentration in HBSS (pH 7.4) were added into the AP and BL

170 sides and incubated for 2 h in order to examine the effects of passive diffusion on the transport of ACE

171 inhibition. After incubation by each treatment, the solution on the each side was collected and analyzed

172 for ACE-inhibitory activity.

173 2.9 Statistical analysis

174 All reported data were reported as means ± standard error (SD) from three independent measurements,

175 each performed in triplicate. Differences between groups were analyzed using one-way analysis of

176 variance (ANOVA). Statistical significance was considered at P < 0.05 with Duncan’s procedure of the

177 SPSS version 20.0 program (SPSS Inc., Chicago, IL, USA).

178 3. Results and discussion

179 3.1 Purification and identification of ACE-inhibitory peptides from bovine collagen

9

180 In this work, the lyophilized fraction with highest ACE-inhibitory activities through Alcalase

181 hydrolysis was further purified by RP-HPLC. The final yield of the most active peptides was 0.19%.

182 From HPLC chromatogram, the elution was separated into 10 major fractions (A1–A10) based on

183 elution time (3-30 min) and the elution profiles of the peptides are shown in Fig. 1A. Among all

184 fractions collected, fraction A8 (elution time ~18 min) exhibited the strongest (P < 0.05) ACE-

185 inhibitory activity of 79.8 ± 1.1% (Fig. 1A). Therefore, the most potent fraction A8 was collected,

186 lyophilized and rechromatographed by RP-HPLC. After the second chromatography, two single pure

187 peptides were isolated from each group and designated to A8-A and A8-B, respectively (Fig. 1B). The

188 ACE-inhibitory activity of each fraction was evaluated and fraction A8-B showed higher potency at

189 48.0 ± 3.6% (Fig. 1B). Subsequently, A8-B was collected for further identification by mass

190 spectrometry. The MS/MS spectrum of peptide (A8-B) is displayed in Fig. 1C and identified as a

191 tetrapeptide (VGPV) with estimated IC50 value of 405.12 μM. The pentapeptide (GPRGF) was

192 previously identified from papain-hydrolyzed bovine collagen of the same source with estimated IC50

193 value of 200.91 μM (Fu et al 2016) through a similar process.

194 The parent protein of VGPV and GPRGF was identified to be collagen α2 chain, which confirmed that

195 the peptide was encrypted within the primary structure of bovine collagen. It is documented that a

196 repeating pattern of collagen peptides (GPX) tends to be satisfactory ACE inhibitors (Gu & Wu, 2013).

197 Moreover, hydrophobic amino acids located in the C-terminal of peptides may play important roles in

198 enhancing potency of ACE due to interactions between non-polar amino acid residues and the enzyme

199 active site or non-active site residues (Aluko, 2015; Girgih et al., 2014). Therefore, Val located at the

200 C-terminus of the identified collagen peptide contribute to greater ACE-inhibitory potency. In recent

201 years, a number of peptides derived from animal muscle proteins have been shown to be suitable ACE

10

202 inhibitors. Escudero, Toldra, Sentandreu, Nishimura, & Arihara (2012) reported ACE-inhibitory

203 peptides derived from in vitro digest of pork meat, namely RPR and PTPVP with IC50 values of 382

204 and 256 μM, respectively. Furthermore, in vitro gastrointestinal digestion of squid skin collagen

205 generated ACE-inhibitory peptides; two oligopeptides were identified as GRGSVPAPOHGP and

206 DPVAPGGPOHQP with the determined IC50 values of 48 and 246 μM (Alemán, Gómez-Guillén, &

207 Montero, 2013). Escudero et al. (2013) isolated and identified several ACE-inhibitory peptides from

208 Spanish dry-cured ham and the most active one was found to be AAATP (IC50 value = 100 μM). A

209 similarity among these ACE-inhibitory peptides is that hydrophobic (aromatic or branched side chain)

210 amino acid residues, such as Pro, Arg and Val at C-terminal positions contribute to stronger ACE-

211 inhibitory activity (Aluko, 2015). Hence, bovine collagen-derived peptides (VGPV and GPRGF) can

212 act as strong ACE inhibitors with comparable activities to other food protein-derived ACE-inhibitory

213 peptides as indicated above.

214 3.2 In silico and in vitro stability of collagen peptides

215 The ability of peptides to withstand further breakdown, e.g. gastrointestinal digestion, is a major

216 concern when ACE-inhibitory peptides are used as functional ingredients, as further degradation may

217 lead to the impaired activity in vivo (Tavares et al., 2011; Lafarga, O’Connor, & Hayes, 2014). Thus, it

218 is necessary to investigate the resistance of collagen peptides against digestive proteases. VGPV and

219 GPRGF (Fu et al., 2016) were subjected to in silico gastrointestinal digestion using PeptideCutter-

220 ExPASy. In silico digestion showed that VGPV remained stable without being cleaved by any

221 investigated enzyme (Table S1). In contrast, trypsin hydrolyzed GPRGF at positions 3, releasing a

222 tripeptide (GPR) and a dipeptide (GF) (Table S1). In order to investigate the experimental digestion of

223 GPRGF by trypsin, tryptic hydrolysis of GPRGF was then carried out. After a 3-h digestion, the

11

224 concentration of free amino groups in the resulting peptide solution was increased from 3.26 to 6.76

225 mM. This fact suggested that GPRGF can be further degraded into smaller peptides by trypsin. As

226 trypsin theoretically cleaves the peptide bond at the C-terminal of arginine residue, the theoretical

227 tripeptide (GPR) and dipeptide (GF) might be released in vitro. These two peptides (GPR and GF) are

228 reported ACE-inhibitory peptides with the documented IC50 values of 1000 μM (Escudero, Sentandreu,

229 Arihara, & Toldra, 2010) and 708 μM (Fahmi et al., 2004), respectively. In the current work, the ACE-

230 inhibitory activity of the tryptic digest was also examined, and the IC50 value of 163.5 μM was obtained.

231 This value is slightly lower than that of GPRGF, which suggests that the ACE inhibition by GPR + GF

232 mixture is additive and a synergistic effect after hydrolysis may lead to the higher ACE-inhibitory

233 potency.

234 Several investigations concerning the stability of ACE-inhibitory peptides have been documented in

235 recent years. Quirós, del Mar Contreras, Ramos, Amigo, & Recio (2009) reported that an ACE-

236 inhibitory peptide derived from β-casein was totally hydrolyzed and its activity substantially decreased

237 after digestion by pepsin and a pancreatic extract. Moreover, del Mar Contreras, Sanchez, Sevilla,

238 Recio, & Amigo (2013) revealed that the simulated gastrointestinal digestion of two casein-derived

239 peptides (YAEERYPIL and RADHPFL) led to decreased in vitro ACE-inhibitory activity. In contrast,

240 VGPV exhibited resistance towards digestive enzymes, while GPRGF was further degraded into

241 smaller peptides with higher individual ACE-inhibitory activity. Similarly, ACE-inhibitory peptides

242 derived from Spanish dry-cured ham maintained almost the same ACE-inhibitory activity after

243 simulated in vitro digestion using gastrointestinal enzymes (Elizabeth Escudero, Mora, & Toldra, 2014).

244 Also, our previous study reported that collagen peptide fractions maintained potent ACE-inhibitory

245 activity after in vitro simulated digestion probably due to post-translational hydroxylation of proline in

12

246 collagen which contributed to higher resistance against gastrointestinal proteases (Fu et al., 2015).

247 Therefore, VGPV with resistance to gastrointestinal proteases as well as GPRGF could be an important

248 bio-functional ingredient to formulate antihypertensive products.

249 3.3 Enzyme kinetics and molecular docking studies of collagen peptides

250 To elucidate ACE-inhibitory patterns by collagen peptides, Lineweaver–Burk plots were constructed

251 for VGPV and GPRGF. Various substrate concentrations (1, 5, 10, 20 and 40 μM) were incubated with

252 ACE enzyme solution in the absence or presence of 100, 200 or 400 μM collagen peptides (VGPV and

253 GPRGF, respectively). As shown in Fig. 2A & B, all the lines intersected at similar points on the X-

254 axis but at different slopes and Y-axis intercepts, suggesting that both peptides are non-competitive

255 ACE inhibitors. The Km and Vmax values for different concentrations of collagen peptides were

256 calculated to elucidate the effectiveness of peptides in exhibiting their inhibitory potential. A decrease

257 in the Vmax value could be observed after incubating ACE with the two peptides. In the presence of 0,

258 100, 200 and 400 μM of substrate, the Vmax values were 20.62, 12.71, 11.55 and 10.57 for VGPV, or

259 20.62, 11.55, 10.09, and 8.99 for GPRGF.

260 The present results are in agreement with a previous study reporting that non-competitive inhibition led

261 to a decreased Vmax with minimal impact on Km (Xiang et al., 2014). Thus, VGPV and GPRGF, as non-

262 competitive inhibitors each interacted with ACE enzyme molecule to generate an enzyme–inhibitor

263 complex, regardless of whether a substrate molecule is present or not. In addition, such inhibitors might

264 induce enzyme protein conformational alterations, which alter the active site and reduce substrate to

265 product conversion rate (Pokora et al., 2014; Sheih, Fang, & Wu, 2009). Our present results are also

266 similar to previous studies on food-derived peptides, which have identified non-competitive ACE

13

267 inhibitors from oyster (Shiozaki et al., 2010), egg white protein (Pokora et al., 2014) and abalone (Wu

268 et al., 2015). Furthermore, the non-competitive ACE inhibition has also been observed for collagen

269 peptides derived from skate and pacific cod skin (Himaya, Ngo, Ryu, & Kim, 2012; Lee, Jeon, & Byun,

270 2011).

271 To explore the molecular mechanism of the interactions between collagen peptides (VGPV and

272 GPRGF) and ACE, docking simulations were performed using CDocker docking tool of DS 2.5. The

273 most stabilized poses of the VGPV and GPRGF with ACE were obtained (Fig. 3A & B) with

274 electrostatic energy (Eele) values of -40.07 and -63.53 kJ/mol, Van der Waals energy (Evdw) values of -

275 4.10 and -4.47 kJ/mol and potential binding energy (Epot) values of -38.97 and -61.63 kJ/mol,

276 respectively (Table S2). In comparison, the predicted binding energies between Lisinopril and ACE are

277 presented in Table S2. Hydrogen bonds were formed between collagen peptides (VGPV and GPRGF)

278 and ACE, suggesting that the peptides effectively interacted with the ACE protein molecule and thus

279 contributed to their inhibitory effects (Chaudhary, Vats, Chopra, Biswas, & Pasha, 2009). As shown in

280 Fig. 3A & B, collagen peptides established four hydrogen bonds with ACE residues, Tyr62, Arg124,

281 Tyr360 and Ser517 for VGPV and Glu123, Glu411, Leu129 and Ser516 for GPRGF (Table S3). When

282 compared to collagen peptides, a higher number of H-bonds (6 in total) was observed in Lisinopril (a

283 commercial antihypertensive drug), which accounted for its greater ACE-inhibitory activity (Table S3).

284 Although VGPV had a confirmed in vitro ACE-inhibitory activity, no direct interaction between the

285 peptide and the Zn(II) in the active site of ACE was observed in this simulation (Fig. 3A). In contrast,

286 GPRGF formed two H-bonds with the active site zinc ion, which may have contributed to the higher

287 ACE-inhibitory activity of GPRGF relative to VGPV (Fig. 3B).

14

288 The present results of VGPV agrees with the findings of Rawendra, Chang, Chen, Huang, & Hsu (2013)

289 and Wu, Du, Jia, & Kuang (2016) who reported that peptides that did not interact with the Zn(II) of

290 ACE still exhibited high ACE-inhibitory activity. The ACE active site is composed of a zinc ion and a

291 HEXXH(n)E motif, which includes His383, Glu384, His387 and Glu411 (Natesh, Schwager, Sturrock,

292 & Acharya, 2003). ACE accommodates two substrate residues that are C-terminal to the zinc ion (in

293 the S1’ and S2’ subsites) and has dipeptidase activity (Natesh, Schwager, Evans, Sturrock, & Acharya,

294 2004). Generally, the S1’ and S2’ subsites are conserved and important subsites in ACE and are

295 occupied by competitive inhibitors (Ni, Li, Liu, & Hu, 2012; Pina & Roque, 2009). However, we found

296 that collagen peptides in the present study did not occupy the S1’ and S2’ subsites and cannot compete

297 with substrate for the active site of ACE. This fact indicates that VGPV and GPRGF displayed ACE-

298 inhibitory activities primarily through non-competitive mechanism, which is in line with the kinetics

299 results.

300 3.4 Transepithelial transport of collagen peptides

301 The transepithelial transport of collagen peptides (VGPV and GPRGF) was investigated using Caco-2

302 cell monolayer as a model. Prior to exposure to Caco-2 cell monolayer, VGPV and GPRGF were

303 subjected to MTT-based cytotoxicity assay. The results indicated neither VGPV nor GPRGF exerted

304 cytotoxic effects on Caco-2 cells (viability above 95%), when collagen peptides were incubated with

305 Caco-2 cells at the concentrations from 0.5 to 2.0 mM for 2 h (Supplementary data, Fig. S1). Thus, for

306 further transport experiments, 0.5 mM peptide concentration was selected. Following addition of

307 VGPV and GPRGF to the AP side of Caco-2 cell monolayer and incubation for 2 h, ACE-inhibitory

308 activity was detected in both AP and BL side (control group), suggesting that the peptides were

309 successfully transported from the AP to BL side remaining ACE-inhibitory effects. In addition, no

15

310 ACE-inhibitory activity was detected in HBSS buffer without peptide (data not shown). In order to

311 confirm peptide transport, the media collected from the AP and BL sides were characterized by LC-

312 MS/MS (Fig. 4). As illustrated in Fig. 4, no degradation products of collagen peptides were detectable,

313 while only VGPV and GPRGF were observed in their intact forms in the AP and BL chambers, though

314 several small unidentified peaks were also observed (Fig. 4B & D). This demonstrated that the VGPV

315 and GPRGF possessed high degree of structural stability and were transported across the Caco-2

316 monolayers.

317 With regards to bioavailability of ACE-inhibitory peptides, resistance towards intestinal peptidases and

318 transport through the brush border raise two major issues (Vermeirssen et al., 2005; Vij, Reddi, Kapila,

319 & Kapila, 2016). The brush border peptidases expressed by Caco-2 cells contain a series of

320 exopeptidases, of which dipeptidyl peptidase IV (DPP-IV) might cleave N-terminal residues of some

321 natural peptides (Aertgeerts et al., 2004). However, resistance towards these peptidases depends on the

322 specific peptide and the amino acid positioning (Miguel et al., 2008; Pauletti et al., 1996). It is

323 demonstrated that peptides with Pro residue located in the third position are resistant to hydrolysis by

324 DPP-IV (Bejjani & Wu, 2013). Furthermore, peptides with aromatic amino acids at the C-terminal are

325 less susceptible to intestinal peptidases (Bejjani & Wu, 2013). In addition, phenylalanine-based

326 peptides were reported to be DPP-IV inhibitors (Xu et al., 2005). The above information provided a

327 theoretic basis for the fact that VGPV and GPRGF displayed resistance towards peptidase and

328 remained intact in AP side, as revealed by LC-MS/MS analysis (Fig. 4A & C). A similar study by Ding

329 et al. (2014) found that QIGLF was stable to transportation across Caco-2 cell monolayers. In contrast,

330 a recent investigation by Gallego et al. (2016) reported that dry-cured ham peptides (AAATP,

331 AAPLAP, and KPVAAP) were partially degraded throughout the transport assay, in spite of KPVAAP

16

332 that was further absorbed to exert ACE-inhibitory activity. Therefore, our present work demonstrated

333 that VGPV and GPRGF have stability against epithelial cell peptidases, which enhanced peptide

334 transportation across epithelia cells.

335 In an attempt to clarify the transport route responsible for collagen peptides, several known modulators

336 of peptide transport were added to AP side of Caco-2 cell monolayers for 30 min prior to the transport

337 experiment. Moreover, the transepithelial proton-gradient (pH 6.0/7.4) treatment was also employed. In

338 the VGPV-treated group, pretreatment using cytochalasin D (a tight junction disruptor) led to a

339 significantly increased ACE inhibition rate (P < 0.05) on the BL side and a concomitant reduction on

340 the AP side, whereas no significant effect of Gly-Sar and wortmanin was observed (Fig. 5A & B).

341 Transepithelial proton-gradient (pH 6.0/7.4) gave rise to a non-significant increase of the ACE

342 inhibition rate on BL side (P > 0.05). Taken together, the results suggest that VGPV transport may be

343 mediated through the intercellular junctions of the intestinal cells via paracellular route, a non-

344 degradative route of transport that keeps the peptide intact (Shimizu, 2004). Similarly, only

345 pretreatment by cytochalasin D prior to GPRGF exposure also caused a significant rise of ACE

346 inhibition rate in BL (P < 0.05), suggesting the passive absorption through intercellular junctions (Fig.

347 5D). In addition, there was no significant effect on the transport of GPRGF with use of Gly-Sar and

348 wortamanin (Fig. 5C). A slight but significant rise of ACE inhibition rate under pH 6.0/7.4 condition

349 was observed in BL side (P < 0.05), suggesting that GPRGF might be transported with aid of a proton-

350 dependent carrier in the apical membrane.

351 To further confirm that the paracellular route was responsible for collagen peptide’s efflux, the same

352 concentration of VGPV or GPRGF (0.5 mM) was added into the AP and BL sides of Caco-2 cell

353 monolayer. After incubation for 2 h, the degrees of apical-to-basolateral (AP–BL) and basolateral-to-

17

354 apical (BL–AP) flux based on the ACE inhibition rates were determined (Fig. 5E & F). In the VGPV

355 group, a non-significant rise of ACE inhibition rate in the (AP–BL) direction flux was observed,

356 suggesting that paracellular passive diffusion was a main route for the observed transepithelial flux of

357 this peptide in the Caco-2 monolayer model. A similar phenomenon was also observed in GPRGF

358 group. The paracellular passive diffusion is further revealed by the reduction in ACE inhibition rate on

359 the AP side and a minor but significant rise of ACE-inhibitory activity in the AP–BL direction (Fig.

360 5E). Hence, paracellular passive diffusion may be suggested as a major transport pathway for VGPV

361 and GPRGF.

362 In general, several possible mechanisms are responsible for peptide transport across the intestinal

363 epithelium, including transcytosis, peptide transporter 1 (PepT1)-assisted transport (Regazzo et al.,

364 2010; Shimizu, Tsunogai, & Arai, 1997) and paracellular pathway through tight junctions. Transcytosis

365 via internalized vesicle is predominantly responsible for the transport of large molecular weight

366 peptides (Regazzo et al., 2010). On the contrary, PepT1 can transport smaller peptides, such as di- and

367 tripeptides (Adibi, 1997). Therefore, VGPV and GPRGF, as collagen-derived tetrapeptide and

368 pentapeptide, were not involved in the above two transport pathways, as shown in results from Gly-Sar

369 and wortmanin treatments (Fig. 5). The paracellular pathway is mainly regulated by tight junctions,

370 which can be estimated by measurement of the Lucifer yellow permeation. In this study, the impacts of

371 paracellular pathway on transport of collagen peptides were evaluated using cytochalasin D, a tight

372 junction disruptor (Quirós, Dávalos, Lasunción, Ramos, & Recio, 2008; Shimizu et al., 1997). Addition

373 of cytochalasin D to AP of Caco-2 cell monolayers and preincubation for 30 min led to impaired tight

374 junction, revealed by the significantly increased fluorescence of Lucifer yellow by approximately 15%

375 (Fig. S2) and the increased ACE inhibition rates upon both VGPV and GPRGF exposure at the BL side

18

376 significantly as well (P < 0.05, Fig. 5B & D). In addition, the transepithelial proton-gradient (pH

377 6.0/7.4) treatments indicated that the collagen peptide uptake at pH 6.0 was remarkably increased

378 compared to the uptake rate at pH 7.4. The paracellular pathway is the major route for passive

379 permeation across the mammalian intestinal epithelium (D'Atri & Citi, 2002). The present study

380 indicated that VGPV and GPRGF were transported through Caco-2 monolayers mainly via the

381 paracellular route, in line with a number of other food-derived bioactive peptides (Satake et al., 2002;

382 Vij et al., 2016). Paracellular permeation of VGPV and GPRGF resembled some previous studies of

383 tight junction-mediated transport, such as the tetrapeptide, GGYR (Shimizu et al., 1997) and

384 pentapeptides (RVPSL, QIGLF, VLPVP and HLPLP) (Ding, Wang, Zhang, & Liu, 2015; Ding et al.,

385 2014; Lei, Sun, Liu, Liu, & Li, 2008; Quirós et al., 2008). It was reported that compounds transported

386 through a passive mechanism in vitro using Caco-2 monolayers might exhibit relatively higher

387 transport in vivo for collagen peptides (Artursson, Palm, & Luthman, 2012; Conradi et al., 1993). To

388 our knowledge, this study for the first time reported the bioavailability and transport of collagen-

389 derived ACE-inhibitory peptides through intestinal epithelium model, which provides a theoretical

390 basis for further development of collagen bio-functional ingredients in the food and nutrition industry.

391 4. Conclusions

392 A novel collagen-derived ACE-inhibitory peptide was identified as VGPV. Lineweaver−Burk plots and

393 molecular docking revealed that VGPV and GPRGF, also derived from bovine collagen were non-

394 competitive ACE inhibitors. VGPV remained strongly resistant to gastrointestinal enzymes, while

395 GPRGF could be further degraded by trypsin into GPR and GF with ACE-inhibitory potencies. VGPV

396 and GPRGF could be transported cross the Caco-2 cell monolayer through paracellular pathway and

397 retained ACE-inhibitory activities. The present results indicated that collagen-derived ACE-inhibitory

19

398 peptides with good bioavailability might be potential as bio-functional ingredient in the food and

399 nutrition industry.

400 Acknowledgements

401 The authors gratefully acknowledge financial support by Future Food Innovation, regional consortium

402 of Central Denmark and Graduate School of Science and Technology (GSST) at Aarhus University,

403 Denmark. The technical assistance of Hanne Søndergaard Møller and Caroline Nebel at Aarhus

404 University, Dr. Cristian De Gobba at University of Copenhagen and Dr. Rong He at Nanjing University

405 of Finance and Economics, China, are much appreciated.

406 References

407 Adibi, S. A. (1997). The oligopeptide transporter (Pept-1) in human intestine: Biology and function.

408 Gastroenterology, 113(1), 332-340.

409 Aertgeerts, K., Ye, S., Tennant, M. G., Kraus, M. L., Rogers, J., Sang, B. C., . . . Prasad, G. S. (2004).

410 Crystal structure of human dipeptidyl peptidase IV in complex with a decapeptide reveals

411 details on substrate specificity and tetrahedral intermediate formation. Protein science, 13(2),

412 412-421.

413 Alemán, A., Gómez-Guillén, M. C., & Montero, P. (2013). Identification of ace-inhibitory peptides

414 from squid skin collagen after in vitro gastrointestinal digestion. Food Research International,

415 54(1), 790-795.

416 Aluko, R. E. (2015). Antihypertensive Peptides from Food Proteins. Annual Review of Food Science

417 and Technology, 6(1), 235-262.

20

418 Artursson, P., Palm, K., & Luthman, K. (2012). Caco-2 monolayers in experimental and theoretical

419 predictions of drug transport. Advanced Drug Delivery Reviews, 64, 280-289.

420 Bejjani, S., & Wu, J. (2013). Transport of IRW, an Ovotransferrin-Derived Antihypertensive Peptide,

421 in Human Intestinal Epithelial Caco-2 Cells. Journal of Agricultural and Food Chemistry, 61(7),

422 1487-1492.

423 Chaudhary, S., Vats, I. D., Chopra, M., Biswas, P., & Pasha, S. (2009). Effect of varying chain length

424 between P-1 and P-1 ' position of tripeptidomimics on activity of angiotensin-converting

425 enzyme inhibitors. Bioorganic & Medicinal Chemistry Letters, 19(15), 4364-4366.

426 Conradi, R. A., Wilkinson, K. F., Rush, B. D., Hilgers, A. R., Ruwart, M. J., & Burton, P. S. (1993). In

427 vitro/in vivo models for peptide oral absorption: Comparison of Caco-2 cell permeability with

428 rat intestinal absorption of renin inhibitory peptides. Pharmaceutical research, 10(12), 1790-

429 1792.

430 Crowley, S. D., & Coffman, T. M. (2012). Recent advances involving the renin–angiotensin system.

431 Experimental Cell Research, 318(9), 1049-1056.

432 D'Atri, F., & Citi, S. (2002). Molecular complexity of vertebrate tight junctions (Review). Molecular

433 Membrane Biology, 19(2), 103-112.

434 Daien, V., Duny, Y., Ribstein, J., Cailar, G. d., Mimran, A., Villain, M., . . . Fesler, P. (2012).

435 Treatment of Hypertension With Renin–Angiotensin System Inhibitors and Renal Dysfunction:

436 A Systematic Review and Meta-Analysis. American Journal of Hypertension, 25(1), 126-132.

437 del Mar Contreras, M., Sanchez, D., Sevilla, M. Á., Recio, I., & Amigo, L. (2013). Resistance of

438 casein-derived bioactive peptides to simulated gastrointestinal digestion. International Dairy

439 Journal, 32(2), 71-78.

21

440 Dempe, J. S., Scheerle, R. K., Pfeiffer, E., & Metzler, M. (2013). Metabolism and permeability of

441 curcumin in cultured Caco-2 cells. Molecular Nutrition & Food Research, 57(9), 1543-1549.

442 Ding, L., Wang, L., Zhang, Y., & Liu, J. (2015). Transport of Antihypertensive Peptide RVPSL,

443 Ovotransferrin 328–332, in Human Intestinal Caco-2 Cell Monolayers. Journal of Agricultural

444 and Food Chemistry, 63(37), 8143-8150.

445 Ding, L., Zhang, Y., Jiang, Y., Wang, L., Liu, B., & Liu, J. (2014). Transport of egg white ACE-

446 inhibitory peptide, Gln-Ile-Gly-Leu-Phe, in human intestinal Caco-2 cell monolayers with

447 cytoprotective effect. Journal of agricultural and food chemistry, 62(14), 3177-3182.

448 Escudero, E., Angel Sentandreu, M., Arihara, K., & Toldra, F. (2010). Angiotensin I-Converting

449 Enzyme Inhibitory Peptides Generated from in Vitro Gastrointestinal Digestion of Pork Meat.

450 Journal of Agricultural and Food Chemistry, 58(5), 2895-2901.

451 Escudero, E., Mora, L., Fraser, P. D., Aristoy, M.-C., Arihara, K., & Toldrá, F. (2013). Purification and

452 Identification of antihypertensive peptides in Spanish dry-cured ham. Journal of Proteomics, 78,

453 499-507.

454 Escudero, E., Mora, L., & Toldra, F. (2014). Stability of ACE inhibitory ham peptides against heat

455 treatment and in vitro digestion. Food Chemistry, 161, 305-311.

456 Escudero, E., Toldra, F., Sentandreu, M. A., Nishimura, H., & Arihara, K. (2012). Antihypertensive

457 activity of peptides identified in the in vitro gastrointestinal digest of pork meat. Meat Science,

458 91(3), 382-384.

459 Fahmi, A., Morimura, S., Guo, H. C., Shigematsu, T., Kida, K., & Uemura, Y. (2004). Production of

460 angiotensin I converting enzyme inhibitory peptides from sea bream scales. Process

461 Biochemistry, 39(10), 1195-1200.

22

462 Fitzgerald, M. A. (2011). Hypertension treatment update: Focus on direct renin inhibition. Journal of

463 the American Academy of Nurse Practitioners, 23(5), 239-248.

464 Fu, Y., Young, J. F., Dalsgaard, T. K., & Therkildsen, M. (2015). Separation of angiotensin I-

465 converting enzyme inhibitory peptides from bovine connective tissue and their stability towards

466 temperature, pH and digestive enzymes. International Journal of Food Science & Technology,

467 50(5), 1234-1243.

468 Fu, Y., Young, J. F., Løkke, M. M., Lametsch, R., Aluko, R. E., & Therkildsen, M. (2016).

469 Revalorisation of bovine collagen as a potential precursor of angiotensin I-converting enzyme

470 (ACE) inhibitory peptides based on in silico and in vitro protein digestions. Journal of

471 Functional Foods, 24, 196-206.

472 Gallego, M., Grootaert, C., Mora, L., Aristoy, M. C., Van Camp, J., & Toldrá, F. (2016).

473 Transepithelial transport of dry-cured ham peptides with ACE inhibitory activity through a

474 Caco-2 cell monolayer. Journal of Functional Foods, 21, 388-395.

475 Girgih, A. T., He, R., & Aluko, R. E. (2014). Kinetics and Molecular Docking Studies of the

476 Inhibitions of Angiotensin Converting Enzyme and Renin Activities by Hemp (Cannabis

477 sativa L.) Peptides. Journal of Agricultural and Food Chemistry, 62(18), 4135-4144.

478 Gómez-Guillén, M. C., Giménez, B., López-Caballero, M. E., & Montero, M. P. (2011). Functional

479 and bioactive properties of collagen and gelatin from alternative sources: A review. Food

480 Hydrocolloids, 25(8), 1813-1827.

481 Gu, Y., & Wu, J. (2013). LC-MS/MS coupled with QSAR modeling in characterising of angiotensin I-

482 converting enzyme inhibitory peptides from soybean proteins. Food Chemistry, 141(3), 2682-

483 2690.

23

484 He, R., Aluko, R. E., & Ju, X.-R. (2014). Evaluating Molecular Mechanism of Hypotensive Peptides

485 Interactions with Renin and Angiotensin Converting Enzyme. Plos One, 9(3).

486 Hidalgo, I. J., Raub, T. J., & Borchardt, R. T. (1989). Characterization of the human-colon carcinoma

487 cell-line (caco-2) as a model system for intestinal epithelial permeability. Gastroenterology,

488 96(3), 736-749.

489 Himaya, S. W. A., Ngo, D.-H., Ryu, B., & Kim, S.-K. (2012). An active peptide purified from

490 gastrointestinal enzyme hydrolysate of Pacific cod skin gelatin attenuates angiotensin-1

491 converting enzyme (ACE) activity and cellular oxidative stress. Food Chemistry, 132(4), 1872-

492 1882.

493 Israili, Z. H., & Hall, W. D. (1992). Cough and angioneurotic edema associated with angiotensin-

494 converting therapy: a review of the literature and pathophysiology. Annals of

495 Internal Medicine, 117(3), 234-242.

496 Jayathilakan, K., Sultana, K., Radhakrishna, K., & Bawa, A. (2012). Utilization of byproducts and

497 waste materials from meat, poultry and fish processing industries: a review. Journal of Food

498 Science and Technology, 49(3), 278-293.

499 Lafarga, T., O’Connor, P., & Hayes, M. (2014). Identification of novel dipeptidyl peptidase-IV and

500 angiotensin-I-converting enzyme inhibitory peptides from meat proteins using in silico analysis.

501 Peptides, 59(0), 53-62.

502 Lee, J. K., Jeon, J.-K., & Byun, H.-G. (2011). Effect of angiotensin I converting enzyme inhibitory

503 peptide purified from skate skin hydrolysate. Food Chemistry, 125(2), 495-499.

504 Lei, L., Sun, H., Liu, D., Liu, L., & Li, S. (2008). Transport of Val-Leu-Pro-Val-Pro in human

505 intestinal epithelial (Caco-2) cell monolayers. Journal of Agricultural and Food Chemistry,

506 56(10), 3582-3586.

24

507 Miguel, M., Dávalos, A., Manso, M. A., de la Peña, G., Lasunción, M. A., & López‐Fandiño, R. (2008).

508 Transepithelial transport across Caco‐2 cell monolayers of antihypertensive egg‐derived

509 peptides. PepT1‐mediated flux of Tyr‐Pro‐Ile. Molecular Nutrition & Food Research, 52(12),

510 1507-1513.

511 Mokrejs, P., Langmaier, F., Mladek, M., Janacova, D., Kolomaznik, K., & Vasek, V. (2009).

512 Extraction of collagen and gelatine from meat industry by-products for food and non food uses.

513 Waste Management & Research, 27(1), 31-37.

514 Mora, L., Reig, M., & Toldra, F. (2014). Bioactive peptides generated from meat industry by-products.

515 Food Research International, 65, 344-349.

516 Mosmann, T. (1983). Rapid colorimetric assay for cellular growth and survival: application to

517 proliferation and cytotoxicity assays. Journal of Immunological Methods, 65(1-2), 55-63.

518 Natesh, R., Schwager, S. L. U., Evans, H. R., Sturrock, E. D., & Acharya, K. R. (2004). Structural

519 Details on the Binding of Antihypertensive Drugs Captopril and Enalaprilat to Human

520 Testicular Angiotensin I-Converting Enzyme. Biochemistry, 43(27), 8718-8724.

521 Natesh, R., Schwager, S. L. U., Sturrock, E. D., & Acharya, K. R. (2003). Crystal structure of the

522 human angiotensin-converting enzyme-lisinopril complex. Nature, 421(6922), 551-554.

523 Ni, H., Li, L., Liu, G., & Hu, S.-Q. (2012). Inhibition Mechanism and Model of an Angiotensin I-

524 Converting Enzyme (ACE)-Inhibitory Hexapeptide from Yeast (Saccharomyces cerevisiae).

525 Plos One, 7(5).

526 Pauletti, G. M., Gangwar, S., Knipp, G. T., Nerurkar, M. M., Okumu, F. W., Tamura, K., . . . Borchardt,

527 R. T. (1996). Structural requirements for intestinal absorption of peptide drugs. Journal of

528 Controlled Release, 41(1), 3-17.

25

529 Petrat-Melin, B., Andersen, P., Rasmussen, J. T., Poulsen, N. A., Larsen, L. B., & Young, J. F. (2015).

530 In vitro digestion of purified β-casein variants A1, A2, B, and I: Effects on antioxidant and

531 angiotensin-converting enzyme inhibitory capacity. Journal of Dairy Science, 98(1), 15-26.

532 Pina, A. S., & Roque, A. C. A. (2009). Studies on the molecular recognition between bioactive peptides

533 and angiotensin-converting enzyme. Journal of Molecular Recognition, 22(2), 162-168.

534 Pokora, M., Zambrowicz, A., Dabrowska, A., Eckert, E., Setner, B., Szoltysik, M., . . . Chrzanowska, J.

535 (2014). An attractive way of egg white protein by-product use for producing of novel anti-

536 hypertensive peptides. Food Chemistry, 151, 500-505.

537 Quirós, A., Dávalos, A., Lasunción, M. A., Ramos, M., & Recio, I. (2008). Bioavailability of the

538 antihypertensive peptide LHLPLP: Transepithelial flux of HLPLP. International Dairy Journal,

539 18(3), 279-286.

540 Quirós, A., del Mar Contreras, M., Ramos, M., Amigo, L., & Recio, I. (2009). Stability to

541 gastrointestinal enzymes and structure–activity relationship of β-casein-peptides with

542 antihypertensive properties. Peptides, 30(10), 1848-1853.

543 Rawendra, R. D., Chang, C.-I., Chen, H.-H., Huang, T.-C., & Hsu, J.-L. (2013). A novel angiotensin

544 converting enzyme inhibitory peptide derived from proteolytic digest of Chinese soft-shelled

545 turtle egg white proteins. Journal of Proteomics, 94, 359-369.

546 Regazzo, D., Mollé, D., Gabai, G., Tomé, D., Dupont, D., Leonil, J., & Boutrou, R. (2010). The (193–

547 209) 17‐residues peptide of bovine β‐casein is transported through Caco‐2 monolayer.

548 Molecular Nutrition & Food Research, 54(10), 1428-1435.

549 Satake, M., Enjoh, M., Nakamura, Y., Takano, T., Kawamura, Y., Arai, S., & Shimizu, M. (2002).

550 Transepithelial Transport of the Bioactive Tripeptide, Val-Pro-Pro, in Human Intestinal Caco-2

551 Cell Monolayers. Bioscience, Biotechnology, and Biochemistry, 66(2), 378-384.

26

552 Shanmugam, V. P., Kapila, S., Sonfack, T. K., & Kapila, R. (2015). Antioxidative peptide derived from

553 enzymatic digestion of buffalo casein. International Dairy Journal, 42, 1-5.

554 Sharp, S. I., Aarsland, D., Day, S., Sonnesyn, H., Ballard, C., & Alzheimer's Soc Vasc Dementia, S.

555 (2011). Hypertension is a potential risk factor for vascular dementia: systematic review.

556 International Journal of Geriatric Psychiatry, 26(7), 661-669.

557 Sheih, I. C., Fang, T. J., & Wu, T.-K. (2009). Isolation and characterisation of a novel angiotensin I-

558 converting enzyme (ACE) inhibitory peptide from the algae protein waste. Food Chemistry,

559 115(1), 279-284.

560 Shimizu, M. (2004). Food‐derived peptides and intestinal functions. Biofactors, 21(1‐4), 43-47.

561 Shimizu, M., Tsunogai, M., & Arai, S. (1997). Transepithelial transport of oligopeptides in the human

562 intestinal cell, Caco-2. Peptides, 18(5), 681-687.

563 Shiozaki, K., Shiozaki, M., Masuda, J., Yamauchi, A., Ohwada, S., Nakano, T., . . . Sato, M. (2010).

564 Identification of oyster-derived hypotensive peptide acting as angiotensin-I-converting enzyme

565 inhibitor. Fisheries Science, 76(5), 865-872.

566 Tavares, T., del Mar Contreras, M., Amorim, M., Pintado, M., Recio, I., & Malcata, F. X. (2011).

567 Novel whey-derived peptides with inhibitory effect against angiotensin-converting enzyme: In

568 vitro effect and stability to gastrointestinal enzymes. Peptides, 32(5), 1013-1019.

569 Vermeirssen, V., Augustijns, P., Morel, N., Van Camp, J., Opsomer, A., & Verstraete, W. (2005). In

570 vitro intestinal transport and antihypertensive activity of ACE inhibitory pea and whey digests.

571 International Journal of Food Sciences and Nutrition, 56(6), 415-430.

572 Vij, R., Reddi, S., Kapila, S., & Kapila, R. (2016). Transepithelial transport of milk derived bioactive

573 peptide VLPVPQK. Food Chemistry, 190, 681-688.

27

574 Wu, Q., Cai, Q.-F., Tao, Z.-P., Sun, L.-C., Shen, J.-D., Zhang, L.-J., . . . Cao, M.-J. (2015). Purification

575 and characterization of a novel angiotensin I-converting enzyme inhibitory peptide derived

576 from abalone (Haliotis discus hannai Ino) gonads. European Food Research and Technology,

577 240(1), 137-145.

578 Wu, Q., Du, J., Jia, J., & Kuang, C. (2016). Production of ACE inhibitory peptides from sweet sorghum

579 grain protein using alcalase: Hydrolysis kinetic, purification and molecular docking study. Food

580 Chemistry, 199, 140-149.

581 Xiang, D., Fengfeng, W., Mei, L., Na, Y., Chunsen, W., Yamei, J., . . . Xueming, X. (2014). Naturally

582 occurring angiotensin I-converting enzyme inhibitory peptide from a fertilized egg and its

583 inhibitory mechanism. Journal of Agricultural and Food Chemistry, 62(24), 5500-5506.

584 Xu, J., Wei, L., Mathvink, R., He, J., Park, Y.-J., He, H., . . . Patel, R. A. (2005). Discovery of potent

585 and selective phenylalanine based dipeptidyl peptidase IV inhibitors. Bioorganic & Medicinal

586 Chemistry Letters, 15(10), 2533-2536.

587 Figure captions

588 Fig. 1 RP-HPLC chromatography and characterisation of ACE-inhibitory peptides derived from bovine

589 collagen. (A) A1-A10 represents main fractions separated by RP-HPLC. (B) A8-A and A8-B indicate

590 main fractions of A8. (C) MS/MS spectrum of VGPV identify by UHPLC system coupled with a Q

591 Exactive mass spectrometer. Data are presented as mean ± SD from three independent experiments.

592 Different letters indicate significantly different values (P < 0.05, n = 3) by one-way ANOVA analysis.

28

593 Fig. 2 Lineweaver–Burk plots of ACE inhibition by VGPV (A) and GPRGF (B). The ACE activities

594 were measured in the absence or presence of the collagen peptide (●, control; ○, 100 μM; ▼, 200 μM;

595 ▲, 400 μM). 1/V and 1/S represent the reciprocal of velocity and substrate, respectively.

596 Fig. 3 Molecular docking between ACE and collagen peptides, VGPV (A) and GPRGF (B). The

597 hydrogen bonds are marked with green dotted lines; amino acid residues and the zinc atom are labelled.

598 Fig. 4 Mass spectra of the AP and BL solution in Caco-2 cell monolayers characterized by LC-MS/MS.

599 (A) AP side of VGPV. (B) BL side of VGPV. (C) AP side of GPRGF. (D) BL side of GPRGF.

600 Fig. 5 Effects of wortamanin, cytochalasin D, and Gly-Sar on the transport of VGPV and GPRGF in

601 Caco-2 cell monolayers. ACE inhibition rate (%) of VGPV (A & B) and GPRGF (C & D) observed in

602 the AP and BL chambers of Caco-2 monolayer, respectively. The transepithelial flux (AP-BL) of 0.5

603 mM VGPV (E) and GPRGF (F) across Caco-2 cell monolayers. Data are presented as mean ± SD from

604 three independent experiments and bars with asterisk differ significantly (P < 0.05, n = 3).

605 Fig. S1 Cytotoxicity of VGPV and GPRGF in Caco-2 Cells evaluated using MTT assay. Results are

606 shown as a percentage of control values. Data are presented as mean ± SD from three independent

607 determinations (n = 3).

608 Fig. S2 Effects of various treatments on the Lucifer yellow permeability (%) on the in Caco-2 cell

609 monolayers. Data are presented as mean ± SD from three independent experiments and bars with

610 asterisk differ significantly (P < 0.05, n = 3).

29

Figure

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. S1

1

Fig. S2

2

Table S1 In silico gastrointestinal digestion using the PeptideCutter – ExPASy

Peptides Enzyme No. of cleavage Positions of cleavage sites Fragments released by digestion

VGPV Pepsin (pH 1.3) 0 - - Pepsin (pH >2) 0 - - Trypsin 0 - - Chymotrypsin 0 - - GPRGF Pepsin (pH 1.3) 0 - - Pepsin (pH >2) 0 - - Trypsin 1 3 GPR, GF Chymotrypsin 0 - -

3

Table S2 Predicted binding energies (Electrostatic energy: Eele; Van der Waals energy: Evdw; Potential energy: Epot , kJ/mol)

ACE (PDB: 1O86) Ligand Eele Evdw Epot VGPV -40.07 -4.10 -38.97 GPRGF -63.53 -4.47 -61.63 Lisinopril -60.76 -4.51 -58.93

4

Table S3 Hydrogen bonds observed between ACE (PDB: 1O86) and the docked top ranked pose of peptides

ACE residues Number of H-bonds and their in H bonds corresponding distance (Å)

VPGV Tyr62:HH 1 2.83 Arg124:HH2 1 2.08

Tyr360:HH 1 2.16

Ser517:HG 1 2.04 GPRGF Glu123:OE2 1 2.27 Glu411:OE2 1 2.19

Leu129:O 1 2.39

Ser516:O 1 2.10 Lisinopril Glu123:OE1 1 2.04 Glu123:OE2 1 2.03 Glu403:OE1 1 1.95 Tyr520:HH 1 2.09 Gln281:HE22 1 2.43 Lys118:HZ1 1 2.26

5

Paper IV

Fu, Y., Young, J. F., & Therkildsen, M. Bioactive peptides in beef: endogenous generation through postmortem aging. Meat Science. Submitted.

*Manuscript Click here to view linked References

1 Bioactive peptides in beef: endogenous generation through postmortem

2 aging

3 Yu Fu, Jette F. Young, Margrethe Therkildsen*

4 Author affiliations:

5 Department of Food Science, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark

6

7

8 *Corresponding author:

9 Margrethe Therkildsen

10 Department of Food Science, Aarhus University, Blichers Allé 20, Postbox 50, 8830 Tjele, Denmark

11 E-mail: [email protected], Tel: +45 87158007, Fax: +45 87154891

12

1

13 Abstract

14 The present research was performed to investigate the role of different aging times (1, 10 and 20 days)

15 on bioactivity of beef peptides. As evidenced by the gradually decreased Warner-Bratzler shear force

16 (WBSF) values of longissimus thoracis (LT) and semitendinosus (ST) muscles, structural proteins and

17 collagen were degraded, releasing low-molecular weight (< 3 kDa) peptides. These peptides were

18 analyzed for 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging capacity, ACE- and renin-

19 inhibitory activities. The peptide sequences were identified by liquid chromatography-electrospray

20 ionization-mass spectrometry (LC-ESI-MS) and their bioactivity potentials were further investigated

21 through in silico analysis (PeptideRanker and BIOPEP). The results demonstrated the peptides with the

22 predicted scores (> 0.8) as well as collagen peptides (0.6-0.8) may contribute to the bioactivities. The

23 present findings provide insights on development of healthy beef through postmortem aging.

24

25

26 Keywords: Bioactive peptides; Postmortem aging; Tenderness; Beef; Collagen

27

2

28 1. Introduction

29 Tenderness, juiciness, color, flavor and aroma are all key attributes of meat palatability with regards to

30 consumers’ satisfaction, with tenderness being the most significant factor (Huffman et al., 1996; Miller

31 et al., 2001; Wu et al., 2015). As important as this attribute is, it seems to be the most variable of all

32 meat palatability traits and has been attributed to several factors, including degradation of myofibrillar

33 proteins and to a large extent the connective tissue. The connective tissue mainly consists of collagen,

34 elastin and reticulin, each of which contributes to the background toughness of the meat to different

35 levels (Ashie Sorensen & Nielsen, 2002; Belew et al., 2003). Postmortem aging of beef is a very

36 effective method to improve tenderness (Huff-Lonergan et al. 1996). It is defined as the storage of fresh

37 beef at refrigerated temperatures to allow the natural enzymatic and biochemical processes to take

38 place resulting in increased tenderness (Nishimura, Hattori, & Takahashi, 1995) due to the weakening

39 of the myofibrils and the intramuscular connective tissue (Dransfield, 1994). During this process,

40 postmortem aging not only contributes to meat tenderness but it may also generate peptide fractions

41 with physiological significance, such as antioxidant and blood pressure lowering effects.

42 Hypertension is a global leading risk factor for cardiovascular diseases (Ahhmed & Muguruma, 2010).

43 It is estimated that approximately 17.5 million people died from cardiovascular diseases in 2012 (WHO,

44 2015). The renin-angiotensin system (RAS) is a main pathway responsible for regulating blood

45 pressure and ensuring fluid homeostasis. Within RAS, renin can catalyze the conversion of

46 angiotensinogen into angiotensin I that is further converted to angiotensin II by angiotensin I-

47 converting enzyme (ACE), leading to the elevated blood pressure and hypertension (Ahhmed &

48 Muguruma, 2010; Crowley & Coffman, 2012). Moderate hypertension can be controlled through

49 dietary approaches and a number of investigations have reported the antihypertensive and ACE-

3

50 inhibitory functions of different food sources (Wijesekara & Kim, 2010). Oxidative stress, an

51 imbalance between oxidants and antioxidants, might further induce and exacerbate hypertension

52 (Kizhakekuttu & Widlansky, 2010). However, administration of food-derived peptidic antioxidants

53 contributes to a lower occurrence of oxidative stress (Samaranayaka & Li-Chan, 2011). In recent years,

54 proteolysis of meat proteins (myofibrillar, sarcoplasmic or collagen) has been documented to release

55 several potential bioactive sequences to exhibit in vitro antihypertensive and antioxidant activities

56 (Ryan, Ross, Bolton, Fitzgerald, & Stanton, 2011; Mora, Reig, & Toldrá, 2014; Fu et al., 2015).

57 Although some health-promoting compounds from meat with positive physiological effects have been

58 reported (Decker & Park, 2010; Young et al., 2013), few studies investigated the peptides generated

59 during postmortem aging and their potential as a natural source of antihypertensive and antioxidant

60 peptides to maintain blood pressure and health. Therefore, the aim of this study is to investigate

61 inherent bioactivity developed through aging/tenderization in beef. In this study, beef was aged for 1,

62 10 or 20 days postmortem and the bioactivity of the released peptides was determined. In addition, their

63 peptide sequences were characterized, and bioactivity potentials from the individual peptides were

64 predicted based on in silico analysis in order to estimate their relative contribution to the sample

65 bioactivity.

66 2. Materials and methods

67 2.1 Materials and sample preparation

68 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ACE from rabbit lung were purchased from Sigma

69 Chemical Co. (St. Louis, MO, USA). Human recombinant renin inhibitor screening assay kit was

70 purchased from Cayman Chemicals (Ann Arbor, MI, USA). Amicon Ultra-15 centrifugal filters (3 kDa

4

71 molecular weight cut-off) were purchased from Merck Millipore (Cork, Ireland). The Oasis HLB

72 cartridges (1 mL) were obtained from Waters (Dublin, Ireland).

73 Six bulls (3 Danish Holstein + 3 Danish Holstein cross breeds; age: 16 ± 2 months; live weight: 245 ±

74 35 kg) were slaughtered at a Danish Crown slaughter house (Aalborg, Denmark). Carcasses were hung

75 in the chiller at 4 °C. One day postmortem, longissimus thoracis (LT) and semitendinosus (ST) muscles

76 were removed from each carcass. The pH value was measured in each muscle with a PHM201 pH

77 meter (Radiometer, Denmark) equipped with Metrohm probe type glass electrode (Metrohm,

78 Switzerland). The electrode was calibrated in pH 4.01 and 7.00 IUPAC buffers. Subsequently, each

79 muscle was divided into three (8 cm×5cm×5cm) blocks, labelled and randomly distributed in three

80 groups of aging (day 1, day 10 and day 20). Day 1 samples were analyzed for texture at the same day

81 and sub-samples from both raw and cooked samples at day 1 were stored at -20 °C for further

82 bioactivity analyses, whereas day 10 and 20 samples were vacuum-packed individually and stored at

83 4 °C for additional 9 or 19 days before texture analysis and subsampling for bioactivity measurement

84 as described above.

85 2.2 Warner-Bratzler shear force (WBSF)

86 The WBSF of aged beef samples was determined as per the method of Honikel (1998). Briefly, the

87 beef samples were heated in a water bath at 63C for 50 min (internal temperature 62C). After heating,

88 the samples were cooled in a water bath at 4C until next day. The next day rectangular blocks (1*1 cm

89 thick) were cut parallel to the longitudinal orientation of the muscle fibres and the shear force was

90 measured using a Texture Analyzer TX-T2 (Stable Micro Systems, Godalming, U.K.) with a Warner-

91 Bratzler shear blade with a rectangular hole. The blade speed was set to 100 mm/min and the average

92 maximum force (N/cm2) of 6 replicates cut from each sample was used.

5

93 2.3 Peptide extraction

94 The peptides were extracted from beef samples according to Bauchart et al. (2006) with slight

95 modifications. Frozen beef samples of both raw and cooked origin (2.5 g) were homogenized in 12.5

96 mL of 3% perchloric acid in centrifuge tubes on ice for 2 min using a Polytron PT 2100 homogenizer

97 from Kinematic AG (Luzern, Switzerland). Subsequently, the homogenate was centrifuged at 10,000 g

98 for 20 min at 4 °C and the supernatant was collected and filtered using a cellulose acetate filter of 0.2

99 μm pore size (Frisenette, Denmark). The extracts were neutralized to pH 7 using sodium hydroxide.

100 The salt precipitate was eliminated using the cellulose acetate filter twice. Subsequently, the

101 supernatant was subjected to ultra-filtration using 3 kDa cut-off centrifugal filters at 10,000 g for 30

102 min. The resulting filtrates were lyophilized and stored at -20 °C for further analysis.

103 2.4 Peptide identification by liquid chromatography-electrospray ionization-mass spectrometry (LC-

104 ESI-MS)

105 Prior to MS analysis, the extracted peptide samples were subjected to purification using Oasis HLB

106 (C18 solid phase) cartridges in order to remove the salts and impurity. Afterwards, the samples were

107 lyophilized and re-diluted using 0.1% formic acid before injection into LC/ESI-MS. The LC system

108 (Agilent Technologies, Waldbronn, Germany) was equipped with Jupiter Proteo C18 column of

109 dimensions 150 mm × 0.5 mm (Phenomenex, Denmark). The percentage of solvent B (90% acetonitrile,

110 0.1% formic acid) in solvent A (0.1% formic acid) was based on a linear gradient. The flow rate was

111 fixed at 100 μL/min for 110 min. Tandem MS spectra were further analyzed by PEAKS Studio 7.5

112 (Waterloo, ON, Canada) and searched against the customized bovine family (Bos taurus) from UniProt

113 database. The search was implemented using no specific enzyme cleavage sites and an MS/MS mass

114 tolerance of 0.5 Da. The peptides with average local confidence (ALC) over 75% were used for further

6

115 analysis. The analysis was performed using two individual samples and only peptides positively

116 identified in both samples were acceptable. Peptide sequences, their position in the parent proteins and

117 the observed masses and retention times were collected from the PEAKS. Basic Local Alignment

118 Search Tool (BLAST) was employed to search for regions of local similarities between the identified

119 peptides and the parent protein sequences within the Bos taurus database

120 (http://blast.ncbi.nlm.nih.gov/Blast.cgi). All the sequences of the peptides identified in this work were

121 searched and revealed 100% homology with proteins of Bos taurus.

122 2.5 Peptide concentration

123 The concentration of the extracted peptides was determined by calculating the amount of N-terminal

124 amines using fluorescamine according to Petrat-Melin et al. (2015). The extracted peptide

125 concentration was fixed at 30 mM for further determination of bioactivity.

126 2.6 Bioactivity determination of the extracted peptides

127 2.6.1 DPPH radical scavenging capacity

128 DPPH radical scavenging activity of isolated peptides was determined according to Li et al. (2007)

129 with slight modifications. 500 μL test sample (peptide fraction of 30 μM) was mixed with 500 μL of

130 99.5% ethanol and 125 μL of 99.5% ethanol containing 0.01% DPPH. This mixture was kept in the

131 dark at room temperature for 60 min before determination of absorbance at 517 nm. DPPH radical

132 scavenging activity was calculated as follows:

133 where Asample is the absorbance of the sample and Acontrol is the absorbance of the control.

7

134 2.6.2 ACE inhibitory-activity

135 ACE-inhibitory activity was determined according to the approach of Petrat-Melin et al. (2015).

136 2.6.3 Renin-inhibitory activity

137 Renin-inhibitory activity was determined by fluorescence as per the method of the manufacturers’

138 instructions. Briefly, 20 μL of substrate, 150 μL of assay buffer, and 10 μL of the extracted peptides

139 were added to the wells and the reaction was started by addition of renin (10 μL). Fluorescence

140 intensity was determined at excitation wavelength of 340 nm and emission wavelength of 490 nm.

141 Percentage inhibition was calculated using the following equation:

142 2.7 In silico prediction of the identified peptides

143 The identified peptides were assessed for potential of bioactivity by input of the peptide sequences into

144 PeptideRanker (http://bioware.ucd.ie) (Mooney et al. 2012). The assigned scores ranged from 0 to 1.0

145 with a threshold of 0.5. The peptide sequences were matched with the previously reported ACE-

146 inhibitory peptides deposited in the BIOPEP database

147 (http://www.uwm.edu.pl/biochemia/index.php/pl/biopep). Data were accessed on May, 2016.

148 2.8 Statistical analysis

149 The shear force and bioactivity data for LT and ST muscles measured after 1, 10 and 20 days of aging

150 were analyzed using SPSS version 20.0 program (Chicago, IL, USA). Differences between groups

151 were analyzed using one-way analysis of variance (ANOVA). Statistical significance among samples

152 was considered at P<0.05 with post-hoc Duncan test.

8

153 3. Results

154 3.1 Ultimate pH

155 The ultimate pH of LT was higher than that of the ST (5.71 ± 0.08 vs. 5.56 ± 0.04, respectively) (P <

156 0.001).

157 3.2 Warner-Bratzler shear force (WBSF) values

158 The shear force values of LT and ST muscles are displayed in Fig. 1. In general, high shear force

159 values are observed in both muscles at day 1 postmortem (57.3 and 60.3 N, respectively), with a

160 significant time-dependent decline in shear force values (P < 0.05). After 20 days postmortem, the

161 shear force values of LT and ST muscles were decreased to 37.5 and 41.6 N, respectively. However,

162 there was no significant difference between LT and ST samples at the same aging day (P > 0.05).

163 3.3 Peptides extracted from aged beef

164 During postmortem aging, the proteolysis of muscle proteins takes place, leading to generation of a

165 considerable amount of peptides. The extracted peptide (< 3 kDa) concentrations of raw LT and ST

166 samples at day 1 were 9.0 ± 2.1 and 8.1 ± 2.6 μM, respectively. There was a significant rise (P < 0.05)

167 after 10 and 20 days postmortem, reaching 15.5 ± 4.2 and 32.0 ± 4.8 μM for LT samples and 15.8 ± 1.5

168 and 30.0 ± 5.4 μM for ST samples. Cooked samples also followed a similar trend. The peptide

169 concentrations of LT and ST samples were remarkably increased (P < 0.05) from the initial 10.0 ± 3.2

170 μM and 10.8 ± 3.0 μM to 33.7 ± 1.4 μM and 34.3 μM ± 10.8 μM at day 20 postmortem, respectively.

171 However, there was an insignificant rise (P > 0.05) between cooked and raw samples at each aging

172 time.

173 3.4 Bioactivity of the extracted peptides

9

174 All the peptides derived from raw and cooked LT and ST samples at different aging days exhibited in

175 vitro DPPH radical scavenging, ACE- and renin-inhibitory activities (Fig. 2, 3 & 4).

176 As shown in Fig. 2A, there was a pronounced increase of DPPH radical scavenging activity after 10

177 days postmortem (P < 0.05) in raw LT samples. The highest DPPH radical scavenging activity in LT

178 (44.6%) was achieved in cooked LT sample at day 20 postmortem. In ST raw samples, there was no

179 pronounced change (P > 0.05) in DPPH radical scavenging activity from 1 to 10 days of aging,

180 whereas this was the case for the cooked samples (Fig 2B). However, from 10 to 20 days of ripening

181 there was a significant increase in DPPH radical scavenging activity, reaching 33.4% and 46.5% in raw

182 and cooked ST samples at 20 days postmortem, respectively (P < 0.05) (Fig. 2B). Overall, the cooked

183 samples possessed higher DPPH radical scavenging activity than raw samples (P < 0.05) and this was

184 increasing with time (except ST samples of day 1, where this was not significant, Fig. 2B).

185 In vitro ACE-inhibitory activities of the extracted peptides from LT and ST muscles are displayed in

186 Fig. 3A & B, respectively. A significant increase in ACE inhibition rate was detected in raw and

187 cooked samples of LT muscle with the progress of aging time (Fig 3 A). The ACE-inhibitory activity

188 was elevated from the initial 14.0% and 17.2% at 1 day post mortem to 34.9% and 40.4% at 20 days

189 post mortem (P < 0.05) in raw and cooked samples, respectively. However, there was no significant

190 difference (P > 0.05) in ACE-inhibitory activities between raw and cooked LT samples. In case of ST

191 muscle, compared to the ACE-inhibitory activity measured in day 1 samples, the activities of raw

192 samples were significantly increased to 39.8% after 20 days postmortem (P < 0.05) (Fig. 3B). In

193 cooked samples, the highest inhibitory activity (44.9%) was achieved at day 10 postmortem, followed

194 by a remarkable decline in activity of the sample of day 20 (P < 0.05). This decrease was not seen in

195 the raw ST samples.

10

196 Renin-inhibitory activities of the extracted peptides are shown in Fig. 4A & B. In general, each peptide

197 samples experienced a rise with increase of aging times, except raw LT sample at Day 20. A significant

198 increase in renin activity was detected in raw and cooked samples of LT and ST muscle with the

199 progress of aging time. The cooked samples displayed higher renin-inhibitory activity than raw samples.

200 At 10 days postmortem, a remarkable rise of renin-inhibitory activity was observed in raw LT samples

201 from initial 5.7 to 15.9 % (P < 0.05), followed by an insignificant decline (P > 0.05) at day 20. In terms

202 of ST samples, the highest inhibitory rates were achieved at day 20, reaching 24.2% and 39.3% in both

203 raw and cooked samples, respectively.

204 3.5 Peptide identification by LC-ESI-MS

205 The identified peptides (< 3kDa) from LT and ST samples after 1, 10 and 20 days postmortem are

206 presented in Supplementary Excel sheet 1–6 and sheet 7–12, respectively. The identified amino acid

207 sequences, parent proteins, observed m/z, calculated mass, accession No. and post-translational

208 modification (PTM) were listed. In general, the identified peptides from aged beef were dominated by

209 small peptides, ranging from 5 to 26 amino acid residues. Smaller peptides were absent probably due to

210 low concentrations not detectable by LC-ESI-MS.

211 The number of the extracted peptides was substantially increased (> 50) after 20 days postmortem,

212 compared to samples in each muscle after 1 and 10 days postmortem. In raw LT samples, some

213 peptides derived from metabolic enzymes and proteins were found after 1 day postmortem

214 (Supplementary Excel sheet 1), including fructose-1,6-bisphosphatase isozyme 2, glutaryl-CoA

215 dehydrogenase, 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase delta-4, /-

216 protein phosphatase 2A catalytic subunit alpha isoform and L-2-hydroxyglutarate dehydrogenase. From

217 day 10 onwards, several peptides from proteins closely related to tenderness were detected, such as

11

218 elastin, actin-related protein 10, putative malate dehydrogenase 1B and collagen alpha-2(I) chain,

219 collagen alpha-2(XI) chain, myosin light chain kinase, DnaJ homolog subfamily B member 6 and heat

220 shock protein 90-alpha (Supplementary Excel sheet 3). As expected more peptides originating from

221 collagen fragments were found in the raw ST richer in collagen (Supplementary Excel sheet 1-12).

222 After 10 and 20 days postmortem, a greater number of peptides were observed, which were derived

223 from several proteins closed related to tenderness, such as calpain small subunit 1, collagen alpha-1(I)

224 and alpha-2(I) chain (Supplementary Excel sheet 9) and tubulin beta-4A chain, elastin, heat shock 70

225 kDa protein 1, calpain small subunit 1 and collagen alpha-1(I) chain (Supplementary Excel sheet 11).

226 Cooking can provoke structural changes via proteolysis at specific sites in muscle. The peptides

227 originating from actin filament-associated protein 1-like 2, elastin, collagen alpha-1(I) chain and alpha-

228 1(III) chain could be found in cooked LT sample at day 1 postmortem (Supplementary Excel sheet 2).

229 Several peptides from collagen alpha-1(III) chain, elastin, tubulin-specific chaperone A, myosin light

230 chain kinase 2, tubulin polymerization-promoting member 3 (Supplementary Excel sheet

231 4) and alpha-actinin-3, myozenin-2, myosin heavy chain 7, myosin light chain kinase (Supplementary

232 Excel sheet 6) were identified in cooked LT samples of day 10 and day 20, respectively. In cooked ST

233 samples, a number of structural proteins were also found, including alpha-actinin-2, collagen alpha-1(II)

234 chain (Supplementary Excel sheet 8), collagen alpha-2(XI), collagen alpha-1(XI), collagen alpha-1(III),

235 collagen alpha-1(XVII) chain (Supplementary Excel sheet 10), and myosin regulatory light chain 2,

236 collagen alpha-1(XVII) chain, elastin and tubulin beta-5 chain (Supplementary Excel sheet 12), day 1,

237 10 and 20 respectively.

238 3.6 In silico analysis of potential bioactivity of the identified peptides

12

239 The extracted peptides from LT and ST samples were subjected to in silico analysis for their bioactive

240 potentials. The PeptideRanker scores (> 0.5) of bioactive peptides derived from meat peptides are listed

241 in Table 1 & 2, indicating that these peptides generated from LT and ST samples could be potentially

242 bioactive. A number of promising bioactive peptides (score > 0.8) were observed, including

243 CPSGPGTF from docking protein in raw LD sample of Day 1 (score=0.91), ARICAF from actin

244 filament-associated protein 1-like 2 in cooked LD sample of Day 1 (score=0.82),

245 SGAPGPAGSRGPPGP from collagen alpha-1(III) chain in cooked ST sample of Day 10,

246 KQAGFPLGILLL from putative sodium-coupled neutral amino acid transporter 11 in raw LD sample

247 of Day 20 (score=0.92) (Table 1), RPPKGF from AFG3-like protein 2 in raw ST sample of Day 1

248 (score=0.89), WPPLP from secretory carrier-associated membrane protein 3 in raw ST sample of Day

249 10 (score=0.98), PCCAPCPF from phosphatidylserine decarboxylase proenzyme (score=0.99) in raw

250 ST sample of Day 20 and CSAAGFF from lutropin-choriogonadotropic hormone receptor (score=0.96)

251 in cooked ST sample of Day 20 (Table 2). In addition, several peptides (LPLGG, FAGGRGG and

252 APPPPAEVP) identified from actin, actinin and troponin may also act as promising bioactive peptides

253 with scores of 0.64, 0.63 and 0.68, respectively. In this work, several peptides identified in the aged

254 beef samples listed in Supplementary Excel sheet 1-12 shared sequences with those reported as ACE-

255 inhibitory peptides from the BIOPEP database, which further underpinned the experimentally

256 determined ACE-inhibitory activities of aged beef samples.

257 4. Discussion

258 Postmortem aging is an effective approach to elevate meat tenderness. In this study, beef samples

259 followed the expected pattern that WBSF values were decreased with postmortem aging (Vitale et al.,

260 2014; Colle et al., 2015; Lepper-Blilie, Berg, Buchanan, & Berg, 2016). ST muscle is often reported to

13

261 have higher shear force values compared to LT muscle probably due to higher amount of intramuscular

262 connective tissue (collagen) than ST muscle (Rowe, 1986; Jeremiah & Gibson, 2003). In addition, LT

263 and ST muscles were expected to differ in aging profiles due to different patterns of metabolism in two

264 muscles based on the difference in muscle fiber types (Maltin, Balcerzak, Tilley, & Delday, 2003),

265 also indicated by their differences in ultimate pH in this study, but this did not cause significant

266 variations in WBSF values. Different breakdown patterns of the proteins in the two muscles might also

267 lead to different peptide profiles with different bioactivities. However, no systematic difference

268 between LT and ST was observed in the peptide profiles.

269 Postmortem proteolysis is a dynamic and variable procedure where endopeptidases exert their

270 cleavages randomly to produce a complex mixture of peptides (Gallego et al., 2014). In this work, more

271 peptides were identified after 20 days postmortem mainly due to extensive degradation of meat proteins

272 during extended aging periods (Kemp et al., 2010). Furthermore, many peptides released during

273 postmortem aging may further be hydrolyzed through the action of endopeptidases to smaller peptides

274 rendering a very dynamic matrix. For example, at any one time peptides are being generated from

275 larger proteins, while other peptides are further degraded to smaller peptides undetectable by the

276 methods applied in the present study.

277 Structural proteins and proteolytic enzymes are two major factors responsible for the meat tenderness

278 during aging (Lana & Zolla, 2016). Tenderization of meat is most likely due to the proteolytic

279 decomposition of myofibrillar and cytoskeletal proteins or collagen by action of a series of endogenous

280 peptidases (Sentandreu, Coulis, & Ouali, 2002; Christensen & Purslow, 2016; Nishimura et al., 1995;

281 Nishimura, 2015). In the present work, a number of peptide fragments derived from key myofibrillar

282 proteins were identified, such as actin, myosin, actinin, troponin and tubulin, as well as peptide

14

283 fragments from collagen and elastin including several peptide fragments from proteins identified as

284 markers of tenderness (Weston, Rogers & Althen, 2002). Heat shock protein 70 kDa, a biomarker of

285 beef tenderness (Jia et al., 2007) was identified in cooked LT and raw ST sample 20 days postmortem.

286 This result was in line with the lowest shear force value (37.5 N and 41.6 N) of LT and ST samples at

287 day 20. Malate dehydrogenase, a biomarker of tenderness for pork (Laville et al., 2007), was found in

288 raw LT sample at day 10 postmortem. Furthermore, structural proteins, such as myosin heavy chain 7

289 (Supplementary Excel sheet 6) and troponin T (Supplementary Excel sheet 9) reported as biomarkers

290 for meat tenderness (Lametsch et al., 2003; Muroya et al., 2007) were identified in this study together

291 with peptide fragments from proteolytic enzymes. The peptide fragments from calpain small subunit 1

292 were identified in raw ST muscle at 10 and 20 days postmortem (Supplementary Excel sheet 9 & 11,

293 respectively), suggesting that calpain small subunit autolyzed when exposed to calcium and further

294 exert a regulatory effect on the calpain (Li, Thompson, & Goll, 2004; Pomponio et al., 2008). It is

295 reported that cathepsins capable of catalyzing hydrolysis of collagen (Christensen et al., 2012) are

296 released with increased storage time (Ertbjerg, Mielche, Larsen, & Møller, 1999), as reflected in this

297 study by identification of peptide fragments from cathepsin L (Supplementary Excel sheet 10) and

298 cathepsin Z (Supplementary Excel sheet 8) in cooked ST samples. This indicated that cathepsins might

299 exert the weakening effects on muscle proteins and autolyze during cooking (Reville, Harrington &

300 Joseph, 1971; Christensen et al., 2013). Matrix metalloproteinases (MMPs) participate in degradation

301 of native collagen (Purslow, 2014). In lamb, the active MMP-2 was found at 21 days postmortem

302 (Sylvestre et al., 2002), underpinning the possible autolysis of MMP, as MMP fragments (a disintegrin

303 and metalloproteinase with thrombospondin motifs 2) were identified in raw LT and cooked ST at 10

304 days postmortem (Supplementary Excel sheet 3 & 10).

15

305 Molecular weight of peptides is one of the key factors responsible for antioxidant activities

306 (Samaranayaka & Li-Chan, 2011), where the most potent antioxidant peptides typically contain 2-20

307 amino acid residues per molecule (Elias, Kellerby & Decker 2008). In this study, the 3 kDa cut-off

308 centrifugal filter was employed to obtain low-molecular weight peptides, and possibly identify the

309 DPPH radical scavenging activities. Several meat-derived antioxidant peptides have been identified

310 from different proteins through hydrolysis of myofibrillar proteins (Saiga et al., 2003), beef brisket

311 sarcoplasmic proteins (Di Bernardini et al., 2012) and duck meat (Wang et al., 2015) by exogenous

312 enzymes. This work demonstrated that postmortem storage of beef also generated antioxidant peptides

313 most probably due to proteolytic activities by endogenous proteases in skeletal muscles (Sentandreu,

314 Coulis, & Ouali, 2002; Udenigwe & Howard, 2013).

315 It is well documented that the peptides displaying in vitro ACE-inhibitory activities may exert in vivo

316 antihypertensive effects when they reach the blood stream in an active state (Vercruysse, Van Camp, &

317 Smagghe, 2005; Aluko, 2015). Somemeat-derived peptides have ACE- and renin-inhibitory inhibitory

318 activities (Vercruysse, Van Camp, & Smagghe, 2005; Udenigwe & Howard, 2013), and ACE-

319 inhibitory peptides have also been identified in Spanish dry-cured ham (Escudero et al., 2012; Escudero

320 et al., 2013). In the current study, it is clearly evidenced that a number of peptides with ACE-inhibitory

321 activities were present in aged beef. It is worth noting that the highest ACE-inhibitory activity was

322 observed in cooked ST sample of day 10, suggesting that certain potent peptides were generated during

323 this period and made accessible by cooking. The decreased inhibitory activity at day 20 may be due to

324 further degradation of the potent peptides by endopeptidases in meat during aging (Gallego et al., 2014).

325 Limited literature was available regarding the renin-inhibitory peptides from meat sources, but several

326 bioactive hydrolysates from flaxseed protein (Udenigwe Lin, Hou & Aluko, 2009), kidney bean protein

16

327 (Mundi & Aluko, 2014) and hemp seed protein (He et al., 2013) were reported to display renin-

328 inhibitory activity and antihypertensive properties when tested in spontaneously hypertensive rats. The

329 present study for the first time reports peptides (below 3 kDa) extracted from aged beef with renin-

330 inhibitory activity.

331 Bioactivities of peptides are related to the amino acid composition, sequence and molecular mass

332 (Matsui & Matsumoto, 2006). The hydrophobic amino acids were reported to be effective amino acids

333 located at the C-terminal of ACE-inhibitory peptides (Wu, Aluko & Nakai, 2006; Aluko, 2015). In

334 addition, strong hydrophobicity of the N-terminal of peptides can potentiate the antioxidant capacity

335 (Pownall, Udenigwe, & Aluko, 2010; Udenigwe & Howard, 2013). In line with this, the identified

336 peptides extracted from the meat were abundant in Pro, Tyr, Phe, Leu and Ile (Table 1 & 2). The high

337 amounts of Gly in peptides enhance the antioxidant activity (Vercruysse et al., 2005; Udenigwe &

338 Howard, 2013; Di Bernardini et al., 2011), as also observed by the abundance of Gly in the present

339 study. In silico analysis by PeptideRanker further elucidated the bioactivity of the identified peptides

340 based on the predicted scores. The closer the predicted score is to 1, the higher possibility is that the

341 peptide is bioactive (Mooney et al., 2012). Meat proteins are good precursors for generating bioactive

342 peptides (Vercruysse et al., 2005; Udenigwe & Howard, 2013), as evidenced by the peptides from meat

343 samples in this study with predicted scores over 0.8. Muguruma et al. (2009) identified a pentapeptide

344 (VKAGF) derived from porcine actin as a potent ACE-inhibitor and antihypertensive peptide.

345 Katayama et al. (2008) isolated EKEREQ and KRQKYDI with active ACE-inhibitory activities from

346 pepsin-catalyzed porcine troponin. Minkiewicz, Dziuba & Michalska (2011) stated that bovine

347 collagen and elastin possess highest frequency of bioactive peptide sequences compared to other meat

348 proteins. Collagen peptides derived from bovine connective tissue have been reported to be potent ACE

17

349 inhibitors (Fu et al., 2015; Fu et al., 2016). Furthermore, collagen-derived peptides containing high

350 amount of Gly and Pro have been suggested to exhibit various health-related bioactivities based on in

351 silico analysis (Minkiewicz, Dziuba & Michalska, 2011). Hence, the peptides with moderate predicted

352 scores (0.6-0.8) may still be promising bioactive peptides. For example, in cooked ST sample (day 10),

353 the highest ACE-inhibitory activity was exhibited probably due to the presence of collagen derived

354 peptides (SPLPPPE, EGPQGPPGPVG and PGLIGARGPPGP). The above mentioned theories

355 underpin the experimentally determined bioactivities of the extracted peptides derived from aged beef.

356 However, the unreported bioactivities and potencies of several promising peptides presented in Table 1

357 & 2 remain to be confirmed in vitro using synthetic peptides.

358 Aging time and cooking can influence the generation of peptides and their related bioactivities (Fogle

359 et al., 2008; Christensen et al., 2012). The bioactivity of the extracted peptides derived from the raw

360 and cooked samples were evaluated based on the normalized concentration (30 μM). Therefore,

361 changes in bioactivity with aging time and cooking are ascribed to certain active peptides generated in

362 the different phases of aged or cooked beef. The higher peptide concentration is not taken into account,

363 which would also lead to higher potencies. In addition, 63 °C was selected as the cooking temperature.

364 It is documented that calpains become rapidly inactivated at 55°C (Christensen et al., 2013), but the

365 remaining endogenous peptidases (e.g. cathepsins, collagenase, metalloproteinase, etc) in cooked

366 samples may still catalyze the proteolysis in beef during cooking, resulting in higher abundance of

367 small peptides (Escudero et al., 2013). To some extent, this fact explains the increased DPPH radical

368 scavenging and renin-inhibitory activities in some cooked samples, compared to the raw samples.

369 However, it is not guaranteed that higher concentration of the smaller peptides always induces impaired

18

370 bioactivities as there may be antagonistic effects within a mixture of bioactive peptides (Hartmann &

371 Meisel, 2007; Hernández-Ledesma, Recio & Amigo 2008).

372 In the present study, the cooked beef aged for 20 days contained the highest concentration of low-

373 molecular weight peptides (approximately 0.12 μg/g beef) and also exhibited the highest ACE- and

374 renin-inhibitory activities. According to the data from Danish Agriculture & Food Council, the daily

375 intake of red meat in Denmark is around 100-150 g/day (McAfee et al., 2010). It is reported that a

376 commercialized fermented milk product (Evolus®) containing ACE-inhibitory peptides (0.14 μg/mL)

377 exerted a mild lowering effect on blood pressure when subjects were given 150 mL milk product per

378 day (Seppo et al., 2003; Tuomilehto et al., 2004). Thus, daily consumption of aged beef containing

379 ACE-, renin-inhibitory and antioxidant peptides may likewise play a vital role in maintaining normal

380 level of blood pressure. In addition, we have identified two collagen peptides, VGPV and GPRGF

381 derived from bovine connective tissue, which have been shown to be transported across a monolayer of

382 human intestinal Caco-2 cells (unreported data), contributing to the likelihood that these peptides are

383 bioavailable and could exhibit ACE-inhibitory activity in vivo. However, further in vivo and clinical

384 studies remain to be performed in order to confirm the antihypertensive effects of ingesting aged beef.

385 4. Conclusions

386 This work presented the endogenous generation of bioactive peptides in beef through postmortem

387 aging. Postmortem aging of LT and ST muscles gave rise to an increased tenderness as well as release

388 of peptides (< 3 kDa) in both LT and ST samples which exhibited DPPH radical scavenging, ACE- and

389 renin-inhibitory activities. The differences in bioactivities in the beef samples are the different peptide

390 profiles caused by different muscles or different aging times of beef. The identified peptides mainly

391 originated from metabolic enzymes and structural proteins in muscles. More peptides were found in the

19

392 samples after 20 days postmortem due to the extensive proteolysis. In silico analysis revealed that

393 peptides with high scores (> 0.8) predicted by PeptideRanker as well as collagen peptides (0.6-0.8)

394 may contribute to the measured bioactivities. The present findings provide insight in to the release of

395 bioactive peptides in beef through postmortem aging.

396 Acknowledgements

397 The authors acknowledge the financial support by Future Food Innovation, regional consortium of

398 Central Denmark and the Graduate School of Science and Technology at Aarhus University. The

399 access to meat samples from DC-Ingredients (Flaesketorvet 41, Copenhagen, Denmark) is highly

400 appreciated. The technical assistance from Hanne Søndergaard Møller and Caroline Nebel with LC-

401 ESI-MS at Department of Food Science, Aarhus University are much appreciated.

402 Reference

403 Ahhmed, A. M., & Muguruma, M. (2010). A review of meat protein hydrolysates and hypertension.

404 Meat Science, 86, 110-118.

405 Aluko, R. E. (2015). Antihypertensive peptides from food proteins. Annual Review of Food Science

406 and Technology, 6, 235-262.

407 Ashie, I. N. A., Sorensen, T. L., & Nielsen, P. M. (2002). Effects of papain and a microbial enzyme on

408 meat proteins and beef tenderness. Journal of Food Science, 67, 2138-2142

409 Bauchart, C., Rémond, D., Chambon, C., Mirand, P. P., Savary-Auzeloux, I., Reynes, C., & Morzel, M.

410 (2006). Small peptides (< 5kDa) found in ready-to-eat beef meat. Meat Science, 74, 658-666.

411 Belew, J. B., Brooks, J. C., McKenna, D. R., & Savell, J. W. (2003). Warner–Bratzler shear evaluations

412 of 40 bovine muscles. Meat Science, 64, 507-512.

20

413 Christensen, L., Ertbjerg, P., Løje, H., Risbo, J., van den Berg, F. W., & Christensen, M. (2013).

414 Relationship between meat toughness and properties of connective tissue from cows and young bulls

415 heat treated at low temperatures for prolonged times. Meat Science, 93, 787-795.

416 Christensen, S., & Purslow, P. P. (2016). The role of matrix metalloproteinases in muscle and adipose

417 tissue development and meat quality: A review. Meat Science, 119, 138-146.

418 Colle, M. J., Richard, R. P., Killinger, K. M., Bohlscheid, J. C., Gray, A. R., Loucks, W. I., Day, R. N.,

419 Cochran, A. S., Nasados, J. A., & Doumit, M. E. (2015). Influence of extended aging on beef quality

420 characteristics and sensory perception of steaks from the gluteus medius and longissimus lumborum.

421 Meat Science, 110, 32-39.

422 Crowley, S. D., & Coffman, T. M. (2012). Recent advances involving the renin–angiotensin system.

423 Experimental Cell Research, 318, 1049-1056.

424 Decker, E. A., & Park, Y. (2010). Healthier meat products as functional foods. Meat Science, 86, 49-55.

425 Di Bernardini, R., Rai, D. K., Bolton, D., Kerry, J., O'Neill, E., Mullen, A. M., et al. (2011). Isolation,

426 purification and characterization of antioxidant peptidic fractions from a bovine liver sarcoplasmic

427 protein thermolysin hyrolyzate. Peptides, 32, 388–400.

428 Dransfield, E. (1994). Optimization of tenderization, aging and tenderness. Meat Science, 36, 105-121.

429 Elias, R. J., Kellerby, S. S., and Decker, E. A. (2008). Antioxidant activity of proteins and peptides.

430 Critical Review Food Science Nutrition, 48, 430–441.

431 Ertbjerg, P., Larsen, L. M., & Møller, A. J. (1999). Effect of prerigor lactic acid treatment on lysosomal

432 enzyme release in bovine muscle. Journal of the Science of Food and Agriculture, 79, 95–100.

21

433 Escudero, E., Aristoy, M. C., Nishimura, H., Arihara, K., & Toldrá, F. (2012). Antihypertensive effect

434 and antioxidant activity of peptide fractions extracted from Spanish dry-cured ham. Meat Science, 91,

435 306-311.

436 Escudero, E., Mora, L., Fraser, P. D., Aristoy, M. C., Arihara, K., & Toldrá, F. (2013). Purification and

437 identification of antihypertensive peptides in Spanish dry-cured ham. Journal of Proteomics, 78,

438 499-507.

439 Fogle, D. R., Plimpton, R. F., Ockerman, H. W., Jarenback, L., & Persson, T. (1982). Tenderization of

440 beef: effect of enzyme, enzyme level, and cooking method. Journal of Food Science, 47, 1113-1118.

441 Fu, Y., Young, J. F., Dalsgaard, T. K., & Therkildsen, M. (2015). Separation of angiotensin I‐

442 converting enzyme inhibitory peptides from bovine connective tissue and their stability towards

443 temperature, pH and digestive enzymes. International Journal of Food Science & Technology, 50,

444 1234-1243.

445 Fu, Y., Young, J. F., Løkke, M. M., Lametsch, R., Aluko, R. E., & Therkildsen, M. (2016).

446 Revalorisation of bovine collagen as a potential precursor of angiotensin I-converting enzyme (ACE)

447 inhibitory peptides based on in silico and in vitro protein digestions. Journal of Functional Foods, 24,

448 196-206.

449 Gallego, M., Mora, L., Fraser, P. D., Aristoy, M. C., & Toldrá, F. (2014). Degradation of LIM domain-

450 binding protein three during processing of Spanish dry-cured ham. Food Chemistry, 149, 121-128.

451 Goll, D. E., V. F. Thompson, H. Li, W. Wei, and J. Cong. 2003. The calpain system. Physiological

452 Reviews, 83,731-801.

22

453 Hartmann, R., & Meisel, H. (2007). Food-derived peptides with biological activity: from research to

454 food applications. Current Opinion in Biotechnology, 18, 163-169.

455 He, R., Malomo, S. A., Alashi, A., Girgih, A. T., Ju, X., & Aluko, R. E. (2013). Purification and

456 hypotensive activity of rapeseed protein-derived renin and angiotensin converting enzyme inhibitory

457 peptides. Journal of Functional Foods, 5, 781-789.

458 Hernández-Ledesma, B., Recio, I., & Amigo, L. (2008). β-Lactoglobulin as source of bioactive

459 peptides. Amino Acids, 35, 257-265.

460 Honikel, K. O. (1998). Reference methods for the assessment of physical characteristics of meat. Meat

461 Science, 49, 447-457.

462 Huff-Lonergan, E., Mitsuhashi, T., Beekman, D. D., Parrish, F. C., Olson, D. G., & Robson, R M.

463 (1996). Proteolysis of specific muscle structural proteins by mu-calpain at low pH and temperature is

464 similar to degradation in postmortem bovine muscle. Journal of Animal Science, 74, 993-1008.

465 Huffman, K. L., Miller, M. F., Hoover, L. C., Wu, C. K., Brittin, H. C., & Ramsey, C. B. (1996).

466 Effect of beef tenderness on consumer satisfaction with steaks consumed in the home and restaurant.

467 Journal of Animal Science, 74, 91-97.

468 Jia, X., Ekman, M., Grove, H., Færgestad, E. M., Aass, L., Hildrum, K. I., & Hollung, K. (2007).

469 Proteome changes in bovine longissimus thoracis muscle during the early postmortem storage period.

470 Journal of Proteome research, 6, 2720-2731.

471 Katayama, K., Anggraeni, H. E., Mori, T., Ahhmed, A. M., Kawahara, S., Sugiyama, M. et al. (2008).

472 Porcine skeletal muscle troponin is a good source of peptides with angiotensin-I converting enzyme

23

473 inhibitory activity and antihypertensive effects in spontaneously hypertensive rats. Journal of

474 Agricultural and Food Chemistry, 56, 355–360.

475 Kemp, C. M., Sensky, P. L., Bardsley, R. G., Buttery, P. J., & Parr, T. (2010). Tenderness – An

476 enzymatic view. Meat Science, 84(2), 248-256.

477 Kim, S. K., & Wijesekara, I. (2010). Development and biological activities of marine-derived bioactive

478 peptides: A review. Journal of Functional Foods, 2, 1-9.

479 Kizhakekuttu, T. J., & Widlansky, M. E. (2010). Natural antioxidants and hypertension: promise and

480 challenges. Cardiovascular Therapeutics, 28, e20-e32.

481 Lametsch, R.; Karlsson, A.; Rosenvold, K.; Andersen, H.J.; Roepstorff, P.; Bendixen, E (2003).

482 Postmortem proteome changes of porcine muscle related to tenderness. Journal of Agricultural and

483 Food Chemistry. 51, 6992–6997.

484 Lana, A., & Zolla, L. (2016). Proteolysis in meat tenderization from the point of view of each single

485 protein: A proteomic perspective. Journal of Proteomics. Doi:10.1016/j.jprot.2016.02.011

486 Laville, E.; Sayd, T.; Terlouw, C.; Chambon, C.; Damon, M.; Larzul, C.; Glenisson J.; Chérel, P.

487 (2007). Comparison of sarcoplasmic proteomes between two groups of pig muscles selected for

488 shear force of cooked meat. Journal of Agricultural and Food Chemistry, 55, 5834–5841.

489 Lepper-Blilie, A. N., Berg, E. P., Buchanan, D. S., & Berg, P. T. (2016). Effects of post-mortem aging

490 time and type of aging on palatability of low marbled beef loins. Meat Science, 112, 63-68.

491 Li, B., Chen, F., Wang, X., Ji, B., & Wu, Y. (2007). Isolation and identification of antioxidative

492 peptides from porcine collagen hydrolysate by consecutive chromatography and electrospray

493 ionization–mass spectrometry. Food Chemistry, 102, 1135-1143.

24

494 Li, H., Thompson, V. F., & Goll, D. E. (2004). Effects of autolysis on properties of μ- and m-calpain.

495 Biochimica et Biophysica Acta (BBA) - Molecular Cell Research, 1691, 91-103.

496 Maltin, C., Balcerzak, D., Tilley, R., & Delday, M. (2003). Determinants of meat quality: tenderness.

497 Proceedings of the Nutrition Society, 62(02), 337-347.

498 Matsui, T., & Matsumoto, K. (2006). Antihypertensive peptides from natural resources. Advances in

499 Phytomedicine, 2, 255-271.

500 McAfee, A. J., McSorley, E. M., Cuskelly, G. J., Moss, B. W., Wallace, J. M., Bonham, M. P., &

501 Fearon, A. M. (2010). Red meat consumption: An overview of the risks and benefits. Meat Science,

502 84, 1-13.

503 Minkiewicz, P., Dziuba, J., & Michalska, J. (2011). Bovine meat proteins as potential precursors of

504 biologically active peptides-a computational study based on the BIOPEP database. Food Science and

505 Technology International, 17, 39-45.

506 Miller, M. F., Carr, M. A., Ramsey, C. B., Crockett, K. L., & Hoover, L. C. (2001). Consumer

507 thresholds for establishing the value of beef tenderness. Journal of Animal Science, 79, 3062-3068.

508 Mooney, C., Haslam, N. J., Pollastri, G., & Shields, D. C. (2012). Towards the improved discovery and

509 design of functional peptides: common features of diverse classes permit generalized prediction of

510 bioactivity. PloS one, 7, e45012.

511 Mora, L., Reig, M., & Toldrá, F. (2014). Bioactive peptides generated from meat industry by-products.

512 Food Research International, 65, 344-349.

25

513 Muguruma, M., Ahhmed, A. M., Katayama, K., Kawahara, S., Maruyama, M., & Nakamura, T. (2009).

514 Identification of pro-drug type ACE inhibitory peptides sourced from porcine myosin B: Evaluation

515 of its antihypertensive effects. Food Chemistry, 114, 516–522.

516 Mundi, S., & Aluko, R. E. (2014). Inhibitory properties of kidney bean protein hydrolysate and its

517 membrane fractions against renin, angiotensin converting enzyme, and free radicals. Austin Journal

518 of Nutrition and Food Sciences, 2, 1-11.

519 Muroya, S., Ohnishi-Kameyama, M., Oe, M., Nakajima, I., & Chikuni, K. (2007). Postmortem changes

520 in bovine troponin T isoforms on two-dimensional electrophoretic gel analyzed using mass

521 spectrometry and western blotting: The limited fragmentation into basic polypeptides. Meat Science,

522 75, 506-514.

523 Nishimura, T. (2015). Role of extracellular matrix in development of skeletal muscle and postmortem

524 aging of meat. Meat Science, 109, 48-55.

525 Nishimura, T., Hattori, A., & Takahashi, K.. (1995). Structural weakening of intramuscular connective

526 tissue during conditioning of beef. Meat Science, 39, 127-133.

527 Petrat-Melin, B., Andersen, P., Rasmussen, J. T., Poulsen, N. A., Larsen, L. B., & Young, J. F. (2015).

528 In vitro digestion of purified β-casein variants A 1, A 2, B, and I: Effects on antioxidant and

529 angiotensin-converting enzyme inhibitory capacity. Journal of Dairy Science, 98, 15-26.

530 Pomponio, L., Lametsch, R., Karlsson, A. H., Costa, L. N., Grossi, A., & Ertbjerg, P. (2008). Evidence

531 for post-mortem m-calpain autolysis in porcine muscle. Meat Science, 80, 761-764.

26

532 Pownall, T. L., Udenigwe, C. C., & Aluko, R. E. (2010). Amino acid composition and antioxidant

533 properties of pea seed (Pisum sativum L.) enzymatic protein hydrolysate fractions. Journal of

534 Agricultural and Food Chemistry, 58, 4712-4718.

535 Purslow, P. P. (2014). New developments on the role of intramuscular connective tissue in meat

536 toughness. Annual Review of Food Science and Technology, 5, 133-153.

537 Reville, W. J., Harrington, M. G., & Joseph, R. L. (1971). Post-mortem autolysis in bovine muscle.

538 Biochemical Journal, 125, 104P.

539 Rowe, R. W. D. (1986). Elastin in bovine Semitendinosus and Longissimus dorsi muscles. Meat

540 Science, 17(4), 293-312.

541 Ryan, J. T., Ross, R. P., Bolton, D., Fitzgerald, G. F., & Stanton, C. (2011). Bioactive peptides from

542 muscle sources: Meat and fish. Nutrients, 3, 765–791.

543 Saiga, A. I., Tanabe, S., & Nishimura, T. (2003). Antioxidant activity of peptides obtained from

544 porcine myofibrillar proteins by protease treatment. Journal of Agricultural and Food Chemistry, 51,

545 3661-3667.

546 Samaranayaka, A.G.P. and Li-Chan, E.C.Y. (2011). Food-derived peptidic antioxidants: A review of

547 their production, assessment, and potential applications. Journal of Functional Foods, 3, 229–254.

548 Seppo, L., Jauhiainen, T., Poussa, T., & Korpela, R. (2003). A fermented milk high in bioactive

549 peptides has a blood pressure–lowering effect in hypertensive subjects. The American journal of

550 Clinical Nutrition, 77, 326-330.

551 Sylvestre, M. N., Balcerzak, D., Feidt, C., Baracos, V. E., & Bellut, J. B. (2002). Elevated rate of

552 collagen solubilization and postmortem degradation inmuscles of lambs with high growth rates:

27

553 Possible relationship with activity of matrix metalloproteinases. Journal of Animal Science, 80(7),

554 1871-1878.

555 Tuomilehto, J., Lindstrom, J., Hyyrynen, J., Korpela, R., Karhunen, M. L., Mikkola, L., Jauhiainen, T.,

556 Seppo, L., & Nissinen, A. (2004). Effect of ingesting sour milk fermented using Lactobacillus

557 helveticus bacteria producing tripeptides on blood pressure in subjects with mild hypertension.

558 Journal of Human Hypertension, 18, 795-802.

559 Udenigwe, C. C., & Howard, A. (2013). Meat proteome as source of functional biopeptides. Food

560 Research International, 54, 1021-1032.

561 Udenigwe, C. C., Lin, Y. S., Hou, W. C., & Aluko, R. E. (2009). Kinetics of the inhibition of renin and

562 angiotensin I-converting enzyme by flaxseed protein hydrolysate fractions. Journal of Functional

563 Foods, 1, 199-207.

564 Vercruysse, L., Van Camp, J., & Smagghe, G. (2005). ACE inhibitory peptides derived from enzymatic

565 hydrolysates of animal muscle protein: A review. Journal of Agricultural and Food Chemistry, 53,

566 8106–8115

567 Vitale, M., Pérez-Juan, M., Lloret, E., Arnau, J., & Realini, C. E. (2014). Effect of aging time in

568 vacuum on tenderness, and color and lipid stability of beef from mature cows during display in high

569 oxygen atmosphere package. Meat Science, 96, 270-277.

570 Wang, L. S., Huang, J. C., Chen, Y. L., Huang, M., & Zhou, G. H. (2015). Identification and

571 characterization of antioxidant peptides from enzymatic hydrolysates of duck meat. Journal of

572 Agricultural and Food Chemistry, 63, 3437-3444.

28

573 Weston, A. R., Rogers, R. W., & Althen, T. G. (2002). Review: The role of collagen in meat tenderness.

574 The Professional Animal Scientist, 18, 107-111.

575 World Health Organization (2015). http://www.who.int/mediacentre/factsheets/fs317/en/

576 Wu, J., Aluko, R. E., & Nakai, S. (2006). Structural requirements of angiotensin I-converting enzyme

577 inhibitory peptides: quantitative structure-activity relationship study of di-and tripeptides. Journal of

578 Agricultural and Food Chemistry, 54, 732-738.

579 Wu, W., Fu, Y., Therkildsen, M., Li, X. M., & Dai, R. T. (2015). Molecular understanding of meat

580 quality through application of proteomics. Food Reviews International, 31, 13-28.

581 Young, J. F., Therkildsen, M., Ekstrand, B., Che, B. N., Larsen, M. K., Oksbjerg, N., & Stagsted, J.

582 (2013). Novel aspects of health promoting compounds in meat. Meat Science, 95, 904-911.

583 Figure captions

584 Fig. 1 Effects of postmortem aging on WBSF of LT and ST samples after 1, 10 and 20 days aging

585 postmortem. Different letters indicate significantly different values (P <0.05) by one-way ANOVA

586 analysis.

587 Fig. 2 DPPH radical scavenging ability of the extracted peptides from LT samples (A) and ST samples

588 (B). The samples were either raw or cooked (62°C) and aged for 1, 10 or 20 days postmortem.

589 Different letters indicate significantly different values (P <0.05) by one-way ANOVA analysis.

590 Fig. 3 ACE-inhibitory activity of the extracted peptides from LT samples (A) and ST samples (B). The

591 samples were either raw or cooked (62°C) and aged for 1, 10 or 20 days postmortem. Different letters

592 indicate significantly different values (P <0.05) by one-way ANOVA analysis.

29

Figure

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table

Table 1 The potential of bioactive peptide sequences released from LD muscle predicted by PeptideRanker

Day Raw Cooked 1

Peptide No. Parent protein Score Peptide sequence Parent protein Score sequence

1 Sodium/myo-inositol Actin filament-associated WPGIL 0.96 ARICAF 0.82 cotransporter 2 protein 1-like 2 2 CPSGPGTF Docking protein 1 0.91 IPGAPGAIPGIG Elastin 0.77 3 ISPCAMMLAL Protein CNPPD1 0.89 GPAGDGDAGGR Envelope glycoprotein B 0.7 4 PGPGPAPGPGPAS MafF 0.88 FICPVVGL Protein RTF2 homolog 0.66 5 Microtubule-associated AGDPMCS Histone deacetylase 8 0.75 SPPRPS 0.65 protein 10 6 Serine/threonine-protein AQSVGGGCC Ras-related protein Rab-21 0.65 FGCFQTPE 0.62 kinase N1 7 WD repeat-containing protein KLFAL 0.64 APGTAGLP Collagen alpha-1(I) chain 0.61 91 9 ACPALGTKSC Reticulon-3 0.6 ACASGP Semaphorin-4A 0.53 10 Rho-associated protein kinase CACSGDCN Replicase polyprotein 1ab 0.59 PPPTGK 0.59 2 Immunoglobulin superfamily 11 LLLLLLP LLPLPCSAP containing leucine-rich repeat 0.51 Protein shisa-5 0.52 protein 12 FRSGK Membrane protein FAM174B 0.51 U1 small nuclear 13 GGGGGGGDM 0.5 ribonucleoprotein 70 kDa

Day Raw Cooked 10

No. Peptide sequence Parent protein Score Peptide sequence Parent protein Score

1 Prosaposin receptor RFPGI Elastin 0.86 CCCCGEGCGEGCGG 0.97 GPR37L1 2 PSFIP Cadherin-3 0.84 SGAPGPAGSRGPPGP Collagen alpha-1(III) chain 0.84 3 Vacuolar fusion protein IQGFP Conglutinin 0.82 GAGGPPPLAT 0.78 MON1 homolog A 4 MCGGGLVCC Claudin-5 0.74 GVKPAKPGVGGLVGPG Elastin 0.78 5 LIM domain-containing protein LPILPP 0.71 YCAPY Keratin-associated protein 3-1 0.76 1 6 AGPEPEPPL Palmitoyltransferase ZDHHC5 0.7 GFRVL 11-cis retinol dehydrogenase 0.76 7 Mitogen-activated protein LPLGG Actin-related protein 10 0.64 SEPGCP 0.69 kinase 7 8 AHKILPVLCGLT N-terminal kinase-like protein 0.63 WSAAGG SCO-spondin 0.69 9 SKMPKPKPPP Cyclin-C 0.6 KGIGKMGLGALVLT Genome polyprotein 0.67 10 UPF0686 protein C11orf1 IKPCCN Putative helicase MOV-10 0.6 QVLACF 0.66 homolog 11 TLD domain-containing SGKPP Cadherin-13 0.59 SQLSLHLPPR 0.63 protein 2 12 Serine/threonine-protein RLSPL Serpin A3-3 0.51 LGFSY 0.63 kinase Sgk1 13 MADPR Tubulin-specific chaperone A 0.57 Day Raw Cooked 20

No. Peptide sequence Parent protein Score Peptide sequence Parent protein Score

1 Putative sodium-coupled neutral KQAGFPLGILLL 0.92 RACCPGWGG SCO-spondin 0.95 amino acid transporter 11 2 Calcium-activated potassium DNA (cytosine-5)- GGGGGGGGGGGSSLRMSSN 0.87 APGEPLP 0.73 channel subunit alpha-1 methyltransferase 1 3 U1 small nuclear GGHMP 0.83 FSSGTM Replicase polyprotein 1a 0.69 ribonucleoprotein C 4 CCAAT/enhancer-binding 78 kDa glucose-regulated GAPGGGAAGMAAGFPY 0.81 LLGTF 0.67 protein beta protein 5 Microsomal glutathione S- CCCCGE Prosaposin receptor GPR37L1 0.78 GFVIL 0.65 3 6 LYLPDCC Ficolin-2 0.76 FAGGRGG Alpha-actinin-3 0.63 7 Krev interaction trapped protein GDADSCF 0.73 PEGGCCN ETS translocation variant 1 0.57 1 8 Ubiquitin carboxyl-terminal E3 ubiquitin-protein EACSRPMMN 0.7 KPLPQP 0.55 42 SH3RF1 GTPase-activating protein 9 Putative methyltransferase AAPGGKSLALLQCAYP 0.69 KLFLA and VPS9 domain-containing 0.53 NSUN3 protein 1 10 FCMSS HMG box-containing protein 1 0.68 11 GPAGPPGVAGEDGDKG Collagen alpha-2(XI) chain 0.66 12 FFASV Nitric oxide synthase, inducible 0.63 13 LVDGGGPCGGRV Antigen WC1.1 0.6 14 VIGGLLLVVALGPG Surfeit locus protein 4 0.58 15 MAPPAA E3 ubiquitin-protein ligase ICP0 0.56 16 IIFLLVIGTLL Transmembrane protein 245 0.55 17 Putative uncharacterized protein FQKVLM 0.53 PXBL-III

Note: The scores were obtained based on the prediction by PeptideRanker and peptides (scores > 0.5) were chosen due to the high probability of bioactivity.

Table 2 The potential of bioactive peptide sequences released from ST muscle predicted by PeptideRanker and BIOPEP

Day Raw Cooked 1

Peptide Peptide No. Parent protein Score Parent protein Score sequence sequence

1 RPPKGF AFG3-like protein 2 0.89 RACCPGWGG SCO-spondin 0.95 2 FEGMC Rhodopsin kinase 0.88 GAQGPMGPAG Collagen alpha-1(II) chain 0.76 3 F-box/WD repeat-containing Alpha-aminoadipic semialdehyde PPLPAP 0.84 LSYGPGPL 0.73 protein 9 synthase, mitochondrial 4 PWWP domain-containing LLGMP Zinc finger protein 668 0.79 AGPAALCSPP 0.71 protein MUM1 5 PVPSW Metalloproteinase inhibitor 4 0.75 GPGSGG Myozenin-1 (Calsarcin-2) 0.63 6 Transmembrane protein 79 FAALR 0.73 SSGPLVP Cathepsin Z 0.6 (Mattrin) 7 Neuropeptide-like protein PRLLLLL 0.7 SRVAGVLGF N-terminal kinase-like protein 0.54 C4orf48 homolog 9 Sine oculis-binding protein AGPEPEPPL Palmitoyltransferase ZDHHC5 0.7 LVPPPTLLVP 0.54 homolog 10 PPPTGK Rho-associated protein kinase 2 0.59 CFCQVSGY ERO1-like protein alpha 0.54 A disintegrin and 11 Cell division control protein 42 KAPPGK metalloproteinase with 0.53 VVGDGAVGKTCLL 0.52 homolog thrombospondin motifs 4 12 Putative histone-lysine N- EPLPPK 0.53 methyltransferase PRDM6 Day Raw Cooked 10

Peptide Peptide No. Parent protein Score Parent protein Score sequence sequence

1 Secretory carrier-associated WPPLP 0.98 PTGAPPGGGAL D site-binding protein 0.81 membrane protein 3 2 SGAGGGGGGGGGGGGGGGG Calpain small subunit 1 0.94 SPLPPPE Collagen alpha-2(XI) chain 0.77 3 PGPMGPPGLAGP Collagen alpha-1(I) chain 0.88 EGPQGPPGPVG Collagen alpha-1(XI) chain 0.74 4 LLGLIILLLW alpha-3 0.81 PGLIGARGPPGP Collagen alpha-1(III) chain 0.63 5 GFRVL 11-cis retinol dehydrogenase 0.76 EDPGSML Limbin 0.52 6 DPPFQIT FAP 0.74 VGAVLPGPLLQ Glycerate kinase 0.5 7 KAPAQLCEGC TBC1 domain family member 1 0.73 8 PGGGGGGAGGRLA Neurexin-1-beta 0.7 9 PDQDCC Transmembrane protein 183 0.7 10 APPPPAEVP Troponin T, fast skeletal muscle 0.68 11 Kelch domain-containing protein VFGGCA 0.62 2 12 RALPPAAPL Capsid scaffolding protein 0.55 13 KDTPRLSLLLVIL Melanoma-associated antigen D4 0.55 14 Insulin-like growth factor- CCSVCA 0.54 binding protein 2 Day Raw Cooked 20

Peptide No. Parent protein Score Peptide sequence Parent protein Score sequence

Phosphatidylserine 1 Lutropin-choriogonadotropic PCCAPCPF decarboxylase proenzyme, 0.99 CSAAGFF 0.96 hormone receptor mitochondrial 2 GRFKRFRKKFKKLFKKLSP Cathelicidin-6 0.85 EPAFM Natural killer cells antigen CD94 0.81 3 QPPLLL Cyclin-dependent kinase 13 0.84 PGAAGGAEDGFF Coatomer subunit alpha 0.79 4 Mitotic-spindle organizing GGGALGGGPAL 0.81 ALAPGHLGGLVL Homeobox protein PKNOX1 0.79 protein 2 5 O(6)-methylguanine-induced PLPVPPPVG Retinal guanylyl cyclase 2 0.77 GPGYYNPNGH 0.74 apoptosis 2 6 AT-rich interactive domain- KPLPPSKPRK 0.74 PADGSMC Spondin-1 0.73 containing protein 5A 7 GGAPSPSSLSLPP INO80 complex subunit E 0.73 CFGGAGG 5-oxoprolinase 0.72 8 Mitochondrial enolase ENPFAC Zinc finger protein OZF 0.72 LMAPGP 0.68 superfamily member 1 9 NACHT, LRR and PYD LPECALLL 0.69 MGPCPGE SCO-spondin 0.67 domains-containing protein 5 10 TRPM8 channel-associated VINDCCRGAM 0.68 ILLPL Collagen alpha-1(XVII) chain 0.62 factor 1 Methylmalonic aciduria and 11 DNA-(apurinic or apyrimidinic EEPSSCSAMAMGR 0.66 LPLPGPTLA homocystinuria type C protein 0.6 site) 2 homolog 12 Lymphocyte antigen 6 complex PPAPK 0.6 HGSGM DNA helicase MCM8 0.59 locus protein G6f 13 KLLSLGKHGRL Telomerase reverse transcriptase 0.58 PEGGCCN ETS translocation variant 1 0.57 14 Dihydropyrimidine Ubiquitin carboxyl-terminal PLGLTCGMVCPT 0.56 ENSGFDGM 0.57 dehydrogenase [NADP(+)] hydrolase 37 15 Brefeldin A-inhibited guanine Mitochondrial inner membrane PPHGEAKAGSSTLPP 0.53 KIPCIKFSK 0.56 nucleotide-exchange protein 1 protein OXA1L 16 KDTPRLSLLLVIL Melanoma-associated antigen D4 0.55 17 Homeobox protein aristaless-like DLPSPME 0.55 4 18 AICDDGATYC 0.54 19 U1 small nuclear GGGGGGGDM 0.5 ribonucleoprotein 70 kDa

Note: The scores were obtained based on the prediction by PeptideRanker and peptides (scores > 0.5) were chosen due to the high probability of bioactivity.

Supplementary Interactive Plot Data (CSV) Click here to download Supplementary Interactive Plot Data (CSV): Supplementary data.xlsx

Peptide ACE inhibitors Renin inhibitorsAntioxidant PeptideRankerMass scorem/z RT Protein Accession PTM WPGIL GI , PG 0.96 584.3322 293.1747 72.41 Sodium/myo-inositol cotransporter 2 Q3ZC26.1|SC5AB_BOVIN CPSGPGTF GP, GT, SG, PG , TF 0.91 764.3163 383.1655 57.44 Docking protein 1 Q5EA84|DOK1_BOVIN ISPCAM(+15.99)MLAL LA 0.89 1064.507 533.2625 60.07 Protein CNPPD1 Q5E9J2.1|CNPD1_BOVIN Oxidation (M) PGPGPAPGPGPAS GPA , GP, AP, PG , PAP 0.88 1057.519 529.7712 19.48 Transcription factor MafF A7YY73.1|MAFF_BOVIN AGDPMCS AG , GD 0.75 679.2305 340.6247 77.7 Histone deacetylase 8 Q0VCB2|HDAC8_BOVIN AQSVGGGCC VG, GG 0.65 780.2894 391.1491 28.71 Ras-related protein Rab-21 Q17R06|RAB21_BOVIN KLFAL LF , KL , FAL 0.64 590.3792 296.1986 42.5 WD repeat-containing protein 91 Q2HJE1.2|WDR91_BOVIN ACPALGTKSC GT, LG 0.6 949.4361 475.7275 69.94 Reticulon-3 Q08D83.1|RTN3_BOVIN CACSGDCN SG, GD 0.59 771.1986 386.608 42.91 Replicase polyprotein 1ab P0C6V8|R1AB_BRV1 LLPLPCSAP PLP , LPLP, LLP, PL , AP 0.51 909.4993 455.7585 24.98 Immunoglobulin superfamily containing leucine-rich repeat protein A4IFA6.1|ISLR_BOVIN FRSGK FR, GK, SG 0.51 593.3285 297.6716 11.79 Membrane protein FAM174B Q1RMK9.1|F174B_BOVIN GGGGGGGDM(+15.99) GG, GD 0.5 679.2231 340.6184 68.71 U1 small nuclear ribonucleoprotein 70 kDa Q1RMR2|RU17_BOVIN Oxidation (M) WGVLGAAEF AA, GA, GV, WG, LG EF GAA 0.42 948.4705 475.2381 63.16 Glutaryl-CoA dehydrogenase, mitochondrial Q2KHZ9.1|GCDH_BOVIN IALLKILL IA LK 0.42 895.647 448.8286 12.16 Striatin-interacting protein 1 Q0P5J8.1|STRP1_BOVIN FGM(+15.99)SGEAP AP, GM, FG, GE, SG, EA 0.4 810.3218 406.1656 13.55 Cleft lip and palate transmembrane protein 1 homolog Q2NL17.1|CLPT1_BOVIN Oxidation (M) KNKMKL KL , NK 0.36 760.4629 381.2421 48.42 Chromatin assembly factor 1 subunit A A6QLA6|CAF1A_BOVIN RITKIF IF 0.33 776.4908 389.2544 52.28 Zinc phosphodiesterase ELAC protein 1 Q29RY4.1|RNZ1_BOVIN MLGGGGYT GGY, GY, GG , LG 0.31 754.332 378.1761 57.8 Histone deacetylase 1 Q32PJ8.1|HDAC1_BOVIN KPKAIL KA, KP, AI KAI, KP 0.31 668.4584 335.238 44.12 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase delta-4 P21671.2|PLCD4_BOVIN RVGALILAHLH LA , VG , GA, HL, AH 0.3 1198.73 600.3738 22.73 Nucleoporin GLE1 Q3ZBK7.1|GLE1_BOVIN RVPLI PL , VP 0.29 596.4009 299.2055 52.75 Fructose-1,6-bisphosphatase isozyme 2 Q2KJJ9.1|F16P2_BOVIN RPDAGPVLAD GP, GPV, RP, LA, AG,DA 0.29 1009.519 337.5161 44.97 TATA-box-binding protein Q2HJ52.1|TBP_BOVIN FMKRVT KR 0.29 780.4316 391.2197 100.94 Replicase polyprotein 1ab P0C6V8|R1AB_BRV1 AILGGGGT GT , GG, LG , AI 0.29 644.3493 323.1839 44.16 Vesicle transport through interaction with t-SNAREs homolog 1B Q2KIU0.1|VTI1B_BOVIN MEGYN GY, EG, ME 0.25 612.2214 307.119 42.52 Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform P67774.1|PP2AA_BOVIN DAGGEG AG, DA , GE , GG , EG 0.23 504.1816 253.0985 36.78 U3 small nucleolar ribonucleoprotein protein IMP4 Q0VD01.1|IMP4_BOVIN PVGRAT RA, VG, GR 0.19 599.3391 300.6766 48.76 Small nuclear ribonucleoprotein-associated protein N Q17QN3.1|RSMN_BOVIN DEADM(+15.99) EA 0.19 595.1796 298.5995 54.87 Translation initiation factor eIF-2B subunit gamma A5PJI7.1|EI2BG_BOVIN Oxidation (M) LTAKVLRL RL 0.17 912.612 305.2138 33.08 Protein RRP5 homolog A7MB10.1|RRP5_BOVIN TKVKMLMV VK 0.15 948.55 475.2803 80.54 Genome polyprotein Q01499|POLG_BVDVS:P19711|POLG_BVDVN TCEAPT AP, AP, PT 0.15 620.2476 311.1291 28.05 E-selectin P98107.1|LYAM2_BOVIN RRAAK RA, AA, RR 0.14 600.382 301.1958 91.78 Zinc finger protein 512 A4FV61.1|ZN512_BOVIN PALSVV AI 0.14 584.3533 293.186 48.88 Trans-L-3-hydroxyproline dehydratase Q3SX04|T3HPD_BOVIN LIKLV KL , IKL 0.13 584.4261 293.222 51.24 L-2-hydroxyglutarate dehydrogenase, mitochondrial A7MBI3|L2HDH_BOVIN KVDGVY VY , GV, DG 0.11 679.3541 340.683 93.36 Replicase polyprotein 1ab P0C6X0|R1AB_CVBQ:P0C6W7|R1AB_CVBEN:P0C6W8|R1AB_CVBLU:P0C6W9|R1AB_CVBM:P0C6T9|R1A_CVBLU:P0C6U1|R1A_CVBQ:P0C6U0|R1A_CVBM:P0C6T8|R1A_CVBEN VSVAPV VAP, AP 0.09 570.3377 571.3441 30.35 Docking protein 1 Q5EA84|DOK1_BOVIN AELLVV EL 0.09 642.3952 322.2069 28.14 Mitochondrial dimethyladenosine transferase 1 Q2TBQ0.2|TFB1M_BOVIN TGAKVRA RA, GA, TG, VR 0.08 701.4184 702.4282 94.66 TATA-box-binding protein Q2HJ52 (TBP_BOVIN) TSKKK 0.05 590.3751 591.3784 37.16 Peroxisome proliferator-activated receptor gamma coactivator 1-alpha Q865B7|PRGC1_BOVIN:Q1JPG1|RS10B_BOVIN

Supplementary Excel sheet 1 (Day1_LD_Raw)

Peptide ACE inhibitors Renin inhibitorsAntioxidant PeptideRankerMass Score m/z RT Protein Accession PTM ARICAF AF , AR 0.82 679.3475 340.6796 93.99 Actin filament-associated protein 1-like 2 Q17R10.1|AF1L2_BOVIN IPGAPGAIPGIG AIP, IP, AP, IG, GI, GA, PG, AI 0.77 1018.581 340.5319 66.53 Elastin P04985.1|ELN_BOVIN GPAGDGDAGGR GPA, GP, AG, GR, DA, GG, GD, DG 0.7 928.3998 465.2076 65.97 Envelope glycoprotein B P12640|GB_BHV1C FICPVVGL VG, GL 0.66 846.4673 424.2435 13.41 Protein RTF2 homolog Q0VCR1|RTF2_BOVIN SPPRPS PR, RP, PP 0.65 639.334 640.3454 15.91 Microtubule-associated protein 10 A3KMW7|MAP10_BOVIN FGCFQTPE FG, CF 0.62 927.3796 464.7003 62.34 Serine/threonine-protein kinase N1 A1A4I4.1|PKN1_BOVIN APGTAGLP GLP, AP, GL, AG, GT, PG 0.61 682.365 342.1892 14.82 Collagen alpha-1(I) chain P02453.3|CO1A1_BOVIN ACASGP GP, SG 0.53 504.2002 505.2087 40.24 Semaphorin-4A Q5EA85.1|SEM4A_BOVIN PPPTGK GK, TG, PT, PP 0.59 595.3329 596.3454 13.39 Rho-associated protein kinase 2 Q28021.1|ROCK2_BOVIN LLLLLLP LLP 0.52 793.5677 397.7939 64.39 Protein shisa-5 Q3T0A9|SHSA5_BOVIN IAKVPLCI PL, IA, VP, IAK 0.46 855.5251 428.7662 57.53 synthase Q4JIJ3.1|METH_BOVIN IPGGKG IP, KG , GK, GG, PG 0.44 527.3067 528.3091 22.01 Collagen alpha-1(III) chain P04258.1|CO3A1_BOVIN VSVAGAFR AF, FR, GA, AG 0.39 805.4446 403.7316 19.87 Atlastin-1 Q58D72|ATLA1_BOVIN RKGKSFL KG , GK, SF 0.39 834.5075 418.2634 40.86 Atlastin-1 Q58D72|ATLA1_BOVIN FEGVDC GV, EG 0.39 668.2476 335.1288 52.44 Sarcoplasmic/ calcium ATPase 1 Q0VCY0.1|AT2A1_BOVIN KGPRK PR, GP, KG 0.38 584.3758 293.1955 44.97 Histone H1oo Q3HNG7.1|H1FOO_BOVIN DVDMACN 0.38 766.2626 384.1369 42.98 Lysosomal protective protein Q3MI05.1|PPGB_BOVIN VVPPG VPP, VP, PG, PP, VVPP 0.35 467.2744 468.2778 51.16 Cystinosin A7MB63.1|CTNS_BOVIN LLLFSS LF, LLF 0.34 678.3952 340.2075 52.68 Serum albumin P02769.4|ALBU_BOVIN DLPTSFVK DLP, VK, SF, PT 0.3 905.4858 453.7529 66.33 Phosphatidylinositol 4-kinase alpha O02811.2|PI4KA_BOVIN LVILL 0.28 569.4152 570.4261 31.79 Heat shock protein HSP 90-alpha Q76LV2.3|HS90A_BOVIN VIQMSC 0.25 679.3033 340.6617 67.7 Sodium-driven chloride bicarbonate exchanger Q32LP4.1|S4A10_BOVIN VFPLVGSAEKR VF, FP, PL, KR, VG, GS, EK 0.24 1201.682 401.5691 30.38 Fatty acid synthase Q71SP7.1|FAS_BOVIN TPLPGAVPT PLP , AVP, LPG , PL , VP , GA, PG, PT, AV 0.23 851.4752 426.7449 17.4 Dysferlin A6QQP7.1|DYSF_BOVIN GPKKK GP 0.22 556.3696 279.1906 99.41 Amiloride-sensitive sodium channel subunit beta A5D7U4|SCNNB_BOVIN:Q2NKY8|DHX30_BOVIN RPPLSSV PL, RP, PP 0.21 754.4337 252.4828 40.53 E3 ubiquitin-protein ligase UHRF1 A7E320.1|UHRF1_BOVIN LPLSSAI PL , AI 0.21 699.4167 700.4257 38.7 P3 protein Q0V8N6|P3_BOVIN QGGGGGGSSSSSV GS, GG, QG 0.19 1022.427 512.2179 36.54 Zinc finger protein AEBP2 A4FV57|AEBP2_BOVIN EDSPASG SG 0.18 661.2555 331.633 90.08 Lanosterol 14-alpha demethylase Q4PJW3.1|CP51A_BOVIN QPAASDSDD AA 0.12 904.341 453.1795 85.28 Membrane-associated progesterone receptor component 1 Q17QC0.3|PGRC1_BOVIN VIKLIGQGTIE IG, GQ, GT, QG, KL, IE IKL 0.08 1169.702 585.8575 37.33 SWI/SNF-related matrix-associated actin-dependent regulator ofE1B7X9.2|SMRCD_BOVIN chromatin subfamily A containing DEAD/H box 1

Supplementary Excel sheet 2 (Day1_LD_Cooked)

Peptide ACE inhibitors Renin inhibitorsAntioxidantScore Mass m/z RT Protein Accession PTM RFPGI RF, FP, GI, PG 0.86 588.3384 295.1754 35.33 Elastin P04985.1|ELN_BOVIN PSFIP IP, SF 0.84 559.3005 560.3102 42.14 Cadherin-3 P19535.1|CADH3_BOVIN IQGFP FP, GF, QG 0.82 560.2958 561.3001 14.46 Conglutinin P23805.2|CONG_BOVIN M(+15.99)CGGGLVCCGL, GG 0.74 857.2904 429.6507 76.11 Claudin-5 Q2HJ22.1|CLD5_BOVINOxidation (M) LPILPP ILP , LPP, PP 0.71 648.421 325.2152 0.64 LIM domain-containing protein 1 G5E5X0.1|LIMD1_BOVIN AGPEPEPPL GP, PL, AG, PP 0.7 905.4494 453.7289 84.13 Palmitoyltransferase ZDHHC5 E1BLT8.1|ZDHC5_BOVIN LPLGG PLG, PL, GG, LG 0.64 455.2744 456.2859 54.96 Actin-related protein 10 Q3ZBD2.1|ARP10_BOVIN AHKILPVLCGLT ILP, GL , AH , HK AHK, AH 0.63 1263.737 632.8743 48.45 N-terminal kinase-like protein A6QLH6.1|NTKL_BOVIN SKMPKPKPPP KP, PP KP 0.6 1105.632 553.8259 49.72 Cyclin-C Q3ZCK5.1|CCNC_BOVIN IKPCCN IKP , KP KP 0.6 676.3036 339.1567 106.47 Putative helicase MOV-10 Q0V8H6.1|MOV10_BOVIN SGKPP GKP , GK, SG, KP, PP KP 0.59 484.2645 485.2724 91.36 Cadherin-13 Q3B7N0.1|CAD13_BOVIN RLSPL RL , LSP, PL 0.51 584.3646 293.1885 64.63 Serpin A3-3 Q3ZEJ6.2|SPA33_BOVIN KARLLL RL, AR, KA 0.48 712.4959 357.2579 1.43 Pre-mRNA-processing factor 6 Q2KJJ0.1|PRP6_BOVIN GEGHPGPVR GP, GPV, GH, GE, EG, PG, VR, HP 0.48 904.4515 453.2321 81.16 Angiopoietin-1 receptor Q06807.1|TIE2_BOVIN PKVAPAPAR VAP, AP, PAP, AR 0.46 905.5446 453.7828 74.99 Coatomer subunit delta P53619.1|COPD_BOVIN SIPGYNK GY, IP, PG, YNK, NK 0.45 777.4021 260.1437 22.19 A disintegrin and metalloproteinase with thrombospondinP79331.1|ATS2_BOVIN motifs 2 EPMLCS 0.41 678.2717 340.1455 53.09 F-box only protein 7 Q2T9S7|FBX7_BOVIN PLLPNALVAP VAP, LLP, PL, AP 0.38 1003.607 502.8123 14.34 Condensin-2 complex subunit H2 Q3SZL8.1|CNDH2_BOVIN QPGHK GH, PG, HK 0.36 565.2972 283.6535 105.05 Collagen alpha-2(I) chain P02465.2|CO1A2_BOVIN AHHPGAMDA GA, DA, PG, AH, HP HHP, HH, AH, AHH0.35 905.3813 453.6988 73.46 UV-stimulated scaffold protein A F1MX48.2|UVSSA_BOVIN AFVHA AF 0.31 543.2805 544.2859 18.49 Zinc transporter 4 Q9TTF3.1|ZNT4_BOVIN DLPTSFVK DLP, VK, SF, PT 0.3 905.4858 453.7524 64.42 Phosphatidylinositol 4-kinase alpha O02811|PI4KA_BOVIN LRSNPK 0.23 713.4184 357.7191 103.93 Eukaryotic translation initiation factor 3 subunit LQ3ZCK1.1|EIF3L_BOVIN RLHKKPK RL, KP, HK LH, LHK, KP 0.22 905.5923 453.8027 78.44 Protein SDA1 homolog A5D7C2.1|SDA1_BOVIN EACPE EA 0.19 547.1948 548.1993 11.94 Thromboxane-A synthase Q2KIG5.1|THAS_BOVIN VIGSY IG, GS, SY 0.16 537.2798 269.649 99.12 Choline transporter-like protein 2 A5D7H3|CTL2_BOVIN PKQESDCV 0.15 904.396 453.2022 78.38 Protein FAM63A Q2KJ22|FA63A_BOVIN IIILDD 0.14 700.4007 351.2046 83.11 Putative malate dehydrogenase 1B A3KMX7|MDH1B_BOVIN SSSTA 0.13 451.1914 452.1992 12.8 Cbp/p300-interacting transactivator 1 Q9BDI3|CITE1_BOVIN:P04515|NSP5_ROTBU:P07295|VNCN_PAVBP:A0MTF4|FGF5_BOVIN:Q0V898|NELFE_BOVIN:P13909|PAI1_BOVIN:Q29RK4|RD23B_BOVIN:Q2T9P0|EFCB3_BOVIN:Q148H8|K2C72_BOVIN:Q3B7N0|CAD13_BOVIN:A1A4K3|DDB1_BOVIN:Q2NKY8|DHX30_BOVIN TVTLALGVM LA , GV, LG 0.13 903.5099 302.177 18.77 Calcium-transporting ATPase type 2C member 1 P57709.1|AT2C1_BOVIN LSKTH 0.08 584.3282 293.1706 64.75 Sodium/myo-inositol cotransporter 2 Q3ZC26.1|SC5AB_BOVIN HVVVRA RA, VR 0.07 679.4129 680.4195 96.17 N(G),N(G)-dimethylarginine dimethylaminohydrolaseP56965.3|DDAH1_BOVIN 1 ILKTT LK 0.06 574.369 288.1909 84.5 Inositol 1,4,5-trisphosphate receptor type 1 Q9TU34|ITPR1_BOVIN:Q8WN95|ITPR3_BOVIN:Q8WN96|ITPR2_BOVIN:P13615|L_VSNJH LKKTT LK 0.04 589.3799 295.6971 104.2 Junction plakoglobin Q8SPJ1.1|PLAK_BOVIN

Supplementary Excel sheet 3 (Day10_LD_Raw) Peptide ACE inhibitors Renin inhibitorsAntioxidant PeptideRankerMass Score m/z RT Protein Accession PTM CCCCGEGCGEGCGG GE, GG, EG 0.97 1236.28 619.1474 25.2 Prosaposin receptor GPR37L1 Q17QD8.1|ETBR2_BOVIN SGAPGPAGSRGPPGP GPA, GP, AP, GA, AG, GS, SG, PG, GPP, PP 0.84 1260.621 631.3134 44.62 Collagen alpha-1(III) chain P04258.1|CO3A1_BOVIN GAGGPPPLAT GP, PL, LA, GA, AG, GG, GPP, PPGPP 0.78 836.4392 279.8224 12.55 Vacuolar fusion protein MON1 homolog A Q17QV2|MON1A_BOVIN GVKPAKPGVGGLVGPG GP, VK, VG, GL, GV, GG, PG, KP, VKP 0.78 1388.814 463.9485 34.64 Elastin P04985|ELN_BOVIN YCAPY AP 0.76 615.2363 616.2474 91.35 Keratin-associated protein 3-1 Q24JX8.1|KRA31_BOVIN GFRVL GF, FR 0.76 590.354 591.3598 87.27 11-cis retinol dehydrogenase Q27979.1|RDH1_BOVIN SEPGCP PG 0.69 588.2213 295.1166 68.22 Mitogen-activated protein kinase 7 A5PKJ4.1|MK07_BOVIN WSAAGG AA, AG, GG 0.69 547.239 274.6266 74.02 SCO-spondin P98167.2|SSPO_BOVIN KGIGKM(+15.99)GLGALVLT LVL, IG, GI, GA, GL, KG, MG, GK, LG 0.67 1372.811 687.4072 93.11 Genome polyprotein P27395|POLG_JAEV1:P32886|POLG_JAEVJ:P19110|POLG_JAEV5Oxidation (M) QVLACF LA, CF 0.66 679.3363 340.6745 59.45 UPF0686 protein C11orf1 homolog Q2T9Q3|CK001_BOVIN SQLSLHLPPR LHLP, LPP, PR, HL, PP 0.63 1146.651 574.3329 56.47 TLD domain-containing protein 2 Q0IID2.1|TLDC2_BOVIN LGFSY GF, LG, SY 0.63 585.2798 293.6493 73.36 Serine/threonine-protein kinase Sgk1 A7MB74.1|SGK1_BOVIN MADPR PR 0.57 588.269 295.1443 81.32 Tubulin-specific chaperone A P48427|TBCA_BOVIN:Q2KIW6|PRS10_BOVIN RACCNN RA 0.44 679.2531 340.6319 71.05 Transmembrane 4 L6 family member 20 Q3T0Z4|T4S20_BOVIN LVAPSAGHKL VAP, AP, AG, GH, KL, HK 0.43 991.5814 496.7938 26.52 Junctional protein associated with coronary artery disease homolog A2VE02|JCAD_BOVIN KTLAPPP LAP, AP, LA, PP 0.43 722.4326 362.2264 47.58 Calicin Q28068.1|CALI_BOVIN LVAGLL GL, AG 0.37 584.3897 293.1995 22.01 Claudin-12 Q0IIL2.1|CLD12_BOVIN ILPKKPPVK ILP, VK, KP, PP KP 0.33 1018.69 340.571 94.25 Targeting protein for Xklp2 A6H6Z7.1|TPX2_BOVIN DLPTSFVK DLP, VK, SF, PT 0.3 905.4858 453.7541 70.02 Phosphatidylinositol 4-kinase alpha O02811.2|PI4KA_BOVIN SLVSTVAPGPGLAPPA VAP, PGL, GP, LAP, AP, LA, GL, PG, PP 0.29 1432.793 717.4103 65.61 Mediator of RNA polymerase II transcription subunit 25 A2VE44|MED25_BOVIN LDILTLLK LK 0.29 927.6005 464.8084 63.11 Uveal autoantigen with coiled-coil domains and ankyrin repeats protein Q8HYY4.1|UACA_BOVIN HLGGGAVVLGA GA, HL, GG, LG, AV HL 0.29 949.5345 475.7705 63.97 Syntaxin-5 Q08DB5.1|STX5_BOVIN GDDNR GD 0.25 575.2299 288.6217 46.61 Glutamate-rich protein 5 Q08DY0.1|ERIC5_BOVIN RGSNSC GS 0.23 622.2493 623.2532 34.33 Phosphoprotein Q03335|PHOSP_RINDR KGFITIVDVQR GF, KG 0.14 1274.735 425.9161 26.29 Glycerol-3-phosphate dehydrogenase A6QLU1.1|GPDM_BOVIN SRAGTPDSD RA, AG, GT 0.11 904.3886 453.1972 88.92 SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamilyE1B7X9|SMRCD_BOVIN A containing DEAD/H box 1 LVKII VK 0.09 584.4261 293.2201 50.16 Myosin light chain kinase 2, skeletal muscle A4IFM7.1|MYLK2_BOVIN SKVKAK VK, KA 0.07 659.433 660.4434 17.35 Tubulin polymerization-promoting protein family member 3 Q3ZCC8.1|TPPP3_BOVIN EYGSE YG, GS, EY 0.07 583.2125 292.6143 50.9 Arf-GAP with SH3 domain, ANK repeat and PH domain-containing protein 1 O97902.1|ASAP1_BOVIN VTKIT 0.04 560.3533 561.36 14.54 Zinc finger CCCH domain-containing protein 15 Q1RMM1.1|ZC3HF_BOVIN KTKVTSASK 0.03 948.5604 475.2894 88.17 Histone H1.1 G3N131|H11_BOVIN

Supplementary Excel sheet 4 (Day10_LD_Cooked)

Peptide ACE inhibitors Renin inhibitorsAntioxidant PeptideRankerMass score m/z RT Protein Accession PTM KQAGFPLGILLL FP, PLG, PL, GF, GI, AG, LG 0.92 1268.786 423.9327 16.7 Putative sodium-coupled neutral amino acid transporter 11 Q5EA97.1|S38AB_BOVIN GGGGGGGGGGGSSLRMSSNGS, GG 0.87 1507.643 503.5541 26.61 Calcium-activated potassium channel subunit alpha-1 Q28204|KCMA1_BOVIN GGHM(+15.99)P GH, GG 0.83 513.2006 257.6061 12.75 U1 small nuclear ribonucleoprotein C Q32PA0.2|RU1C_BOVINOxidation (M) GAPGGGAAGM(+15.99)AAGFPY FP, AP, AA, GF, GM, GA, AG, GG, PG GAA 0.81 1366.598 456.541 16.95 CCAAT/enhancer-binding protein beta O02755.1|CEBPB_BOVINOxidation (M) CCCCGE GE 0.78 616.1114 309.0645 92.42 Prosaposin receptor GPR37L1 Q17QD8.1|ETBR2_BOVIN LYLPDCC LY, YL LY 0.76 825.3401 413.6744 105.67 Ficolin-2 Q5I2E5.1|FCN2_BOVIN GDADSCF DA, GD, CF 0.73 713.2327 357.6258 101.89 Krev interaction trapped protein 1 Q6TNJ1.1|KRIT1_BOVIN EACSRPMMN RP, EA 0.7 1037.409 519.7139 20.37 Ubiquitin carboxyl-terminal hydrolase 42 E1B9W9.1|UBP42_BOVIN AAPGGKSLALLQCAYPAAP, AY, YP, AP, LA, AA , GK, GG,AY PG, LQ 0.69 1558.818 520.6155 41.1 Putative methyltransferase NSUN3 Q0P5D8.1|NSUN3_BOVIN FCMSS 0.68 573.1927 287.6016 90.57 HMG box-containing protein 1 Q2KJ34|HBP1_BOVIN GPAGPPGVAGEDGDKG GPA, GP, AG, KG, GV, GE, GD, PG, GPP DG, GPP, PP 0.66 1379.632 460.8804 15.91 Collagen alpha-2(XI) chain Q32S24|COBA2_BOVIN FFASV 0.63 569.2849 285.6513 14.34 Nitric oxide synthase, inducible Q27995|NOS2_BOVIN:Q9INJ1|RDRP_BAVJK LVDGGGPCGGRV GP, GR, GG, DG 0.6 1085.529 362.8526 22.82 Antigen WC1.1 P30205|WC11_BOVIN VIGGLLLVVALGPG LGP , GP, IG, GL, GG, LG, PG 0.58 1276.812 426.6152 19.88 Surfeit locus protein 4 A7YY49|SURF4_BOVIN MAPPAA AP , AA, PP 0.56 556.2679 279.1423 103.97 E3 ubiquitin-protein ligase ICP0 P29128|ICP0_BHV1J:P29836|ICP0_BHV1K IIFLLVIGTLL IF, IG, GT 0.55 1213.805 405.6103 19.99 Transmembrane protein 245 E1BD52.1|TM245_BOVIN FQKVLM(+15.99) QK 0.53 780.4204 391.2207 94.89 Putative uncharacterized protein PXBL-III P03414|YPX3_BLVJOxidation (M) KINASM(+15.99)NGHLPFGH, HL, NG HL 0.49 1343.666 672.8367 19.48 Septin-10 Q2KJB1.1|SEP10_BOVINOxidation (M) QPSSEGLPGTARPP GLP, LPG, RP, GL, GT, EG, PG, AR, PP 0.48 1392.7 465.2359 33.72 PH and SEC7 domain-containing protein 1 F1MUS9.1|PSD1_BOVIN DRALPDFKGIQ RA, GI, KG, 0.47 1258.667 420.5598 33.8 Haloacid dehalogenase-like hydrolase domain-containing protein 2Q3ZCH9.1|HDHD2_BOVIN CCGGEDYR GE, GG GGE 0.47 901.3058 301.4408 91.63 Tetraspanin-15 Q1JQA4.1|TSN15_BOVIN GRNQNGKKKNKLVKKKKKTGPPPQKTGP, VK, GR, GK, TG, NG, NKL, QK,GPP KL, NK, GPP, PP, PQ 0.45 2929.778 1465.891 72.17 Methionine aminopeptidase 2 Q3ZC89.1|MAP2_BOVIN CSVSCGGGE GE, GG GGE 0.45 797.2684 399.638 13.12 SCO-spondin P98167|SSPO_BOVIN LQDPGTTLPGPPSAAATSPM(+15.99) LPG, GP, AA, GT, PG, GPP, LQ, PP 0.43 1923.925 642.3094 30.06 Probable UDP-sugar transporter protein SLC35A4 Q05B73|S35A4_BOVINOxidation (M) GLGGQGPQL GP, GL, GQ, GG, QG, LG, PQ 0.4 825.4344 413.725 106.35 Tensin-4 Q32PJ7|TENS4_BOVIN WGLGHEAG GL, AG, GH, WG, LG, EA 0.38 825.377 413.6947 101.62 Very-long-chain 3-oxoacyl-CoA reductase Q5E9H7.1|DHB12_BOVIN PAGVCAGDAISGP GP, AG, DA, GV, SG, GD, AI 0.38 1113.513 372.1772 23.8 Protein kintoun Q0VC73.2|KTU_BOVIN AFSAAAPAAAAQPAAP, AF, AP, AA 0.37 1142.572 572.2921 12.95 Envelope glycoprotein I Q08102|GI_BHV1S QAAQGPGAPENGAT GP, AP, AA, GA, QG, NG, PG 0.36 1267.579 634.7918 92.37 BTB/POZ domain-containing protein KCTD18 Q29RJ0|KCD18_BOVIN LGTGGFGNVIR IR, GF, FG, GT, GG, LG, IRTG IR 0.36 1089.593 545.8031 94.66 Inhibitor of nuclear factor kappa-B kinase subunit beta Q95KV0|IKKB_BOVIN QQSKILKVIRK IR IR KVI, IR, LK 0.34 1339.866 447.6331 16.84 Heat shock protein HSP 90-alpha Q76LV2.3|HS90A_BOVIN PMQGPEAP GP, AP, QG, EA 0.34 825.3691 413.6926 103.78 Calcium-binding protein 4 Q8HZJ4.1|CABP4_BOVIN SPRKVPSSAEKLV PR, VP, KL, EK 0.33 1396.804 466.6055 36.34 Uncharacterized protein CXorf66 homolog Q2YDJ5.2|CX066_BOVIN LIIVILPG ILP, LPG, PG 0.32 836.5735 419.2961 14.47 Protein argonaute-3 Q6T5B7.2|AGO3_BOVIN KVLFLGNLKK LF, LG LK 0.32 1158.749 387.2594 21.92 Coronin-2A Q32LP9.1|COR2A_BOVIN KLYALGLV LY, YA, GL, LG, KL LY 0.32 875.548 292.8545 18.26 U3 small nucleolar ribonucleoprotein protein IMP3 Q3T0M3.1|IMP3_BOVIN KAYRKL AY, KL, KA AY 0.32 777.4861 260.1698 18.56 DnaJ homolog subfamily B member 6 Q0III6|DNJB6_BOVIN:A5D7F5|DJC27_BOVIN:Q5EA26|DJC18_BOVIN:Q3ZBA6|DJB11_BOVIN:Q58DR2|DJB12_BOVIN:Q0IIE8|DJB14_BOVIN:Q0II91|DJC21_BOVIN:Q5E954|DNJA1_BOVIN:Q2HJ94|DNJA2_BOVIN:Q27968|DNJC3_BOVIN PIRVRVLASLTVM IR, LA, VR, ASL, IR IR 0.3 1453.88 485.6355 31.14 Equilibrative nucleoside transporter 3 A1A4N1.1|S29A3_BOVIN EPEGPGGSP GP, GS, GG, EG, PG 0.3 825.3504 413.6853 105.35 Putative sodium-coupled neutral amino acid transporter 7 A7E3U5.1|S38A7_BOVIN LLKGTAFKLN AF, KG, GT, KL, LN LK 0.29 1103.67 368.8943 16.13 UPF0602 protein C4orf47 homolog Q2T9M0.1|CD047_BOVIN ETAASSCGQPPR PR, AA, GQ, PP 0.28 1202.535 401.8517 25.51 SWI/SNF-related matrix-associated actin-dependent regulator of chromatinQ9TTA5|SMAL1_BOVIN subfamily A-like protein 1 DRSKFKTK KF KF 0.28 1008.572 337.1979 19.38 Transcription factor E2F8 E1BKK0|E2F8_BOVIN LDLAGGSGH LA, AG, GH, GS, GG, SG 0.26 825.3981 413.7059 106.09 Probable inactive glycosyltransferase 25 family member 3 A7MB73.1|GT253_BOVIN LDCANKVTGKTPAPAP, GK, TG, PAP, NK 0.26 1413.729 472.2538 15.53 Mediator of RNA polymerase II transcription subunit 29 A1A4Q8.1|MED29_BOVIN VTPDPSPPS PP 0.25 895.4287 299.4812 17.72 WD repeat-containing protein 6 A7Z052.1|WDR6_BOVIN LAKIIPIINGQ IP, LA, GQ, NG 0.25 1178.739 393.9219 20.16 Peroxisomal biogenesis factor 3 A6H7C2.1|PEX3_BOVIN FLSQPAGSAAS AA, AG, GS 0.24 1034.503 518.255 12.4 Antigen WC1.1 P30205|WC11_BOVIN LMLLYSS LY LY 0.23 825.4306 413.7261 106.2 Mediator of RNA polymerase II transcription subunit 25 A2VE44.1|MED25_BOVIN LNILHA LN LH, LHA 0.18 679.4017 340.7099 92.4 Replication origin-binding protein P52377|OBP_BHV1C LIKKSFS SF IKK 0.17 821.501 411.7547 13.64 Inter-alpha-trypsin inhibitor heavy chain H1 Q0VCM5|ITIH1_BOVIN EPHM(+15.99)ANCQVQVAPAVAP, AP, PH PHM 0.17 1509.67 504.2309 33.67 Lysyl oxidase homolog 4 Q8MJ24.1|LOXL4_BOVINOxidation (M) QTPLTALYV LY, PL LY 0.16 1004.554 335.8564 25.61 Aldehyde dehydrogenase, mitochondrial P20000.2|ALDH2_BOVIN AVEGGGGSGGSQ GS, GG, SG, EG, VE, AV 0.15 961.4101 321.4786 13.73 Potassium voltage-gated channel subfamily A member 4 Q05037|KCNA4_BOVIN VPEGM(+15.99)QS VP, GM, EG 0.14 762.3218 382.1686 16.11 Inhibitor of nuclear factor kappa-B kinase subunit beta Q95KV0|IKKB_BOVINOxidation (M) ACGEGVAD GV, GE, EG 0.14 720.2748 361.1433 11.94 Alpha-fetoprotein Q3SZ57.1|FETA_BOVIN KTPARPVGTSEPK RP, VG, GT, AR 0.13 1366.757 456.5938 19.36 Uncharacterized protein C8orf46 homolog Q0VCV7.1|CH046_BOVIN KKRAPKV AP, KR, RA 0.12 825.5548 413.7836 103.69 Ganglioside-induced differentiation-associated protein 1 A6QQZ0.1|GDAP1_BOVIN KKKPA KP, KP 0.12 570.3853 286.1981 19.75 Myosin light chain kinase, smooth muscle Q28824.1|MYLK_BOVIN KVDGVY VY, GV, DG VY 0.11 679.3541 340.6826 98.59 Replicase polyprotein 1a P0C6T9|R1A_CVBLU:P0C6U1|R1A_CVBQ:P0C6U0|R1A_CVBM:P0C6T8|R1A_CVBEN:P0C6W8|R1AB_CVBLU:P0C6X0|R1AB_CVBQ:P0C6W7|R1AB_CVBEN:P0C6W9|R1AB_CVBM YSLGESA GE, LG 0.1 725.3232 363.6661 92.24 Prenylcysteine oxidase-like Q0P5H1|PCYXL_BOVIN EVKGKRINVEL VK, KR, KG, GK, EV, VE EL 0.09 1283.756 642.8804 14.03 RNA-binding protein 14 Q5EA36.1|RBM14_BOVIN EVEFEDGENG GE, NG, DG, EV, VE EF 0.09 1123.431 375.4847 92.44 Inositol 1,4,5-trisphosphate receptor type 1 Q9TU34.1|ITPR1_BOVIN YDTSGED GE, SG 0.08 785.2715 393.6421 28.02 Tudor domain-containing protein 7 A6QLE1|TDRD7_BOVIN LSEELDQVV EL 0.06 1030.518 344.5135 12.05 Transmembrane protein 206 Q2KHV2|TM206_BOVIN DQPKTQAETS TQ 0.06 1103.51 368.8445 16.17 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 2 P26285|F262_BOVIN DEPSTKQSD 0.06 1005.425 336.1475 15.38 Intraflagellar transport protein 46 homolog Q1LZB4.1|IFT46_BOVIN

Supplementary Excel sheet 5 (Day20_LD_Raw)

Peptide ACE inhibitors Renin inhibitorsAntioxidantPeptideRankerMass scorem/z RT Protein Accession PTM RACCPGWGG GW, RA, WG, GG, PG 0.95 905.3636 453.6925 87.89 SCO-spondin P98167.2|SSPO_BOVIN APGEPLP PLP, PL, GEP, AP, GE, PG, GEP 0.73 679.3541 340.6823 60.41 DNA (cytosine-5)-methyltransferase 1 Q24K09.1|DNMT1_BOVIN FSSGTM(+15.99) GT, SG 0.69 644.2476 323.1313 33.93 Replicase polyprotein 1a P0C6F4|R1A_BRV1:P0C6V8|R1AB_BRV1Oxidation (M) LLGTF GT, LG, TF 0.67 549.3162 275.6667 45.81 78 kDa glucose-regulated protein Q0VCX2.1|GRP78_BOVIN GFVIL GF 0.65 547.337 274.6773 92.36 Microsomal glutathione S-transferase 3 Q3T100|MGST3_BOVIN:P04882|GLYCO_VSNJO:P56701|PSMD2_BOVIN FAGGRGG AG, GR,GG 0.63 620.303 311.1587 51.96 Alpha-actinin-3 Q0III9.1|ACTN3_BOVIN PEGGCCN GG, EG 0.57 678.2101 340.1101 81.98 ETS translocation variant 1 Q2KIC2|ETV1_BOVIN KPLPQP PLP, PL, KP, PQ KP 0.55 678.4064 340.2082 53.53 E3 ubiquitin-protein ligase SH3RF1 A5D7F8.1|SH3R1_BOVIN KLFLA LF, LA, KL 0.53 590.3792 591.3818 87.19 GTPase-activating protein and VPS9 domain-containing protein 1 A5D794.1|GAPD1_BOVIN WGTCIATSC IA, GT, WG 0.48 940.3783 471.1948 73.57 Proteasome activator complex subunit 4 F1MKX4.1|PSME4_BOVIN GGAHLSSPLASIA PL, IA, LA, GA, HL, GG, AH HL, AH, GAH 0.46 1179.625 394.2132 29.57 Cytochrome b-c1 complex subunit 1, mitochondrial P31800.2|QCR1_BOVIN TPFGGFEKAGF, FG, GG, KA, EK 0.43 952.4654 477.2436 35.4 Myozenin-2 Q5E9V3|MYOZ2_BOVIN LPQMPDV PQ 0.41 798.3945 400.2018 34.18 Mediator of RNA polymerase II transcription subunit 27 Q2TBN7.1|MED27_BOVIN KKRPGKL RP, KR, GK, PG, KL 0.33 825.5548 413.7856 99.58 Zinc phosphodiesterase ELAC protein 1 Q29RY4.1|RNZ1_BOVIN KVISCM KVI 0.32 679.3397 340.6751 94.59 Replicase polyprotein 1a P0C6F4|R1A_BRV1:P0C6V8|R1AB_BRV1 LPPVGDA LPP, VG, DA, GD, PP 0.31 667.3541 334.6863 18.52 Rab9 effector protein with kelch motifs Q5EA50.1|RABEK_BOVIN DSGSLLLEYVGS, SG, EY 0.3 1094.55 365.8605 11.91 Cation-independent -6-phosphate receptor P08169|MPRI_BOVIN SLLVL LVL 0.29 543.3632 544.3736 28.19 Transmembrane 4 L6 family member 20 Q3T0Z4.1|T4S20_BOVIN TESASLILLPLLP, TE, ASL 0.28 1042.591 522.3032 37.46 tRNA (guanine(37)-N1)-methyltransferase Q3MHN8.1|TRM5_BOVIN RKGMDI GM, KG 0.28 718.3796 360.1939 52.22 Angiopoietin-2 O77802.3|ANGP2_BOVIN PGALGLEGQAAAAA, GA, GL, GQ, LG, EG, PG 0.27 1053.545 527.7822 49.9 Periaxin E1BM58.3|PRAX_BOVIN GLPKGVVRGLP, GL, KG, GV, VR 0.26 824.5232 413.2721 17.7 Acyl-CoA synthetase short-chain family member 3, mitochondrial A7MB45|ACSS3_BOVIN IIAKL IA, IAK, KL 0.24 556.3948 279.206 103.03 Myosin heavy chain 7 Q9BE39.1|MYH7_BOVIN KPNEEVTWK KPNEEVTWK KP, EV, KP, TW 0.22 1129.577 565.7963 23.38 NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 1 Q02378|NDUB1_BOVIN HAKPKLVI YAVLGSQGGYA, GS, GG, QG, LG, AV 0.23 850.4185 426.2173 51.89 Fatty acid synthase Q71SP7.1|FAS_BOVIN LNPEGGKSGKSPLNP, GK, GG, SG, EG, LN 0.23 1169.604 585.8085 46.27 Forkhead box protein O1 E1BPQ1.2|FOXO1_BOVIN ARLIAH RL, IA, AR, AH AH 0.23 679.4129 340.716 92.82 Nucleolar protein 56 Q3SZ63.1|NOP56_BOVIN LSEPVL 0.22 656.3745 329.1925 61.26 CST complex subunit STN1 Q08DB2.1|STN1_BOVIN KGLIAVVVIGVIIIA, IG, GL, KG, GV, AV 0.21 1292.88 431.9703 17.81 Platelet endothelial cell adhesion molecule P51866.1|PECA1_BOVIN IETLLP LLP, IE 0.21 684.4058 343.2106 29.39 Glycogen phosphorylase, muscle form P79334|PYGM_BOVIN HLSAHSSLV HL, AH HL, AH 0.21 949.4981 475.7568 82.26 Protein misato homolog 1 A5D9D4.2|MSTO1_BOVIN EPAGSI AG, GS 0.18 572.2806 287.1478 36.61 AP-3 complex subunit beta-1 Q32PG1|AP3B1_BOVIN REAGACLSGA, AG, EA 0.17 805.3752 403.6935 24.14 Methionine synthase reductase Q4JIJ2.1|MTRR_BOVIN NFSVSDGEEGGE, EG, DG, NF 0.17 1039.409 520.7112 48.68 Mitogen-activated protein kinase kinase kinase 13 A7MBB4|M3K13_BOVIN AVRAIGRLSS RL, RA, IG, GR, VR, AI, AV 0.15 1028.609 343.8781 23.95 Myosin light chain kinase, smooth muscle Q28824.1|MYLK_BOVIN ISEPVI 0.14 656.3745 329.1925 61.26 Non-structural maintenance of element 4 homolog A Q2TBI1.1|NSE4A_BOVIN EPGGGESETGHSGH, GE, GG, TG, PG GGE 0.14 1142.448 572.2324 26.36 Protein SHQ1 homolog Q3MHH1.1|SHQ1_BOVIN KVLLI 0.14 584.4261 293.2184 86.36 Annexin A4 P13214.2 VISVLAISLAS MEGAGGANDGA, AG, GG, EG, ME 0.13 820.3021 411.1554 61.2 Hypoxia-inducible factor 1-alpha Q9XTA5.1|HIF1A_BOVIN IINM(+15.99)SS 0.13 679.3211 340.666 97.18 3-hydroxybutyrate dehydrogenase type 2 Q3T046.1|BDH2_BOVINOxidation (M) EDDKDPNDKD 0.12 946.3516 474.1818 51.18 AP-3 complex subunit delta-1 Q865S1.2|AP3D1_BOVIN KKPKIKVVNVKP KP 0.11 1151.775 384.9331 23.65 Heat shock protein 105 kDa Q0IIM3.1|HS105_BOVIN RVTPAIVAAI 0.09 825.5072 413.7633 99.92 Heat shock protein 70 kDa protein 14 Q2YDD0.1|HSP7E_BOVIN KMVVK VK 0.08 603.3778 302.6963 85.94 Succinyl-CoA ligase [ADP-forming] subunit beta, mitochondrial Q148D5.1|SUCB1_BOVIN VVLVV 0.04 527.3683 528.3721 17.95 Replicase polyprotein 1a P0C6F4|R1A_BRV1:P0C6V8|R1AB_BRV1

Supplementary Excel sheet 6 (Day20_LD_Cooked) Peptide ACE inhibitors Renin inhibitorsAntioxidantPeptideRankerMass score m/z RT Prortein Accession PTM RPPKGF RP, GF, KG, PPK, PP 0.89 700.402 351.2101 80.64 AFG3-like protein 2 Q2KJI7.1|AFG32_BOVIN FEGMC GM, EG 0.88 585.1927 293.6014 63.63 Rhodopsin kinase P28327.1|RK_BOVIN PPLPAP PLP, PL, AP, PAP, PP 0.84 590.3428 591.3472 24.25 F-box/WD repeat-containing protein 9 Q2T9T9.1|FBXW9_BOVIN LLGMP GM, LG 0.79 529.2934 265.6536 25.98 Zinc finger protein 668 Q2TA17.2|ZN668_BOVIN PVPSW VP 0.75 584.2958 293.1569 59.75 Metalloproteinase inhibitor 4 O97563 |TIMP4_BOVIN FAALR AA 0.73 576.3384 289.1789 16.48 Transmembrane protein 79 (Mattrin) Q5E9U3.1|TMM79_BOVIN PRLLLLL RL, PR 0.7 836.5847 419.2999 62.71 Neuropeptide-like protein C4orf48 homolog A0JNN8.2|CD048_BOVIN AGPEPEPPL GP, PL, AG, PP 0.7 905.4494 453.7328 59.15 Palmitoyltransferase ZDHHC5 E1BLT8|ZDHC5_BOVIN PPPTGK GK, TG, PT, PP 0.59 595.3329 596.3454 13.39 Rho-associated protein kinase 2 Q28021.1|ROCK2_BOVIN KAPPGK AP, GK, PG, KA, PP 0.53 596.3646 299.1867 45.88 A disintegrin and metalloproteinase with thrombospondin motifs 4 Q9TT93|ATS4_BOVIN EPLPPK PLP, LPP, PL, PPK, PP 0.53 679.3904 340.7012 72.05 Putative histone-lysine N-methyltransferase PRDM6 A6QPM3.1|PRDM6_BOVIN HRIAVQHFW IA, AV 0.51 1192.625 597.3201 89.35 Methionine--tRNA ligase, mitochondrial A6H7E1|SYMM_BOVIN PFIPQPDD IP, PQ 0.49 927.4338 464.7207 59.2 Serine/threonine-protein kinase greatwall E1BFR5.1|GWL_BOVIN YRGGGGGGGG GG 0.45 793.3467 397.6812 91.12 Transformer-2 protein homolog beta Q3ZBT6.1|TRA2B_BOVIN RACCNN RA 0.44 679.2531 340.6341 91.99 Transmembrane 4 L6 family member 20 Q3T0Z4.1|T4S20_BOVIN RACCNN RA 0.44 679.2531 340.6341 91.99 Transmembrane 4 L6 family member 20 Q3T0Z4.1|T4S20_BOVIN ALLLR LLR 0.43 584.4009 293.2084 24.2 Metalloproteinase inhibitor 4 O97563 |TIMP4_BOVIN GSHSATAM(+15.99) GS 0.39 776.3123 389.1666 68.18 RNA-directed RNA polymerase VP1 Q9INJ1|RDRP_BAVJKOxidation (M) LEEFDCSC EF 0.35 944.3256 473.1743 24.06 Leucine-rich repeat-containing protein F1MCA7.3|LRRC7_BOVIN PECASLPSSI ASL 0.33 1002.469 502.2401 15.88 SPRY domain-containing SOCS box protein 3 Q3MHZ2|SPSB3_BOVIN AGAVASGFSA GF, GA, AG, SG, AV 0.33 836.4028 419.2094 15.25 Atrial natriuretic peptide receptor 2 P46197.1|ANPRB_BOVIN PPTAGA GA, AG, PT, PP 0.32 512.2595 513.2714 13.66 Unconventional myosin-Ia P10568.1|MYO1A_BOVIN IIVLGVFI VF , GV , LG 0.29 872.5735 873.5858 29.02 Sphingosine 1-phosphate receptor 1 Q5E9P3|S1PR1_BOVIN PEILVQPL PL, EI 0.27 907.5378 454.7758 70.46 MAGUK p55 subfamily member 7 A6QQZ7.1|MPP7_BOVIN IPVAGLT IP, GL, AG 0.27 669.4061 670.408 57.36 Palmitoyltransferase ZDHHC5 E1BLT8|ZDHC5_BOVIN KKLLLY LY, KL LY 0.26 776.516 389.2621 21.51 Proteasome activator complex subunit 4 (Proteasome activator PA200) F1MKX4.1|PSME4_BOVIN ITIGRLG RL, IG, GR, LG 0.25 728.4545 365.2344 46.65 Transportin-1 Q3SYU7.2|TNPO1_BOVIN GHQLLVALV GH 0.25 948.5756 475.2953 62.41 Protein-methionine sulfoxide oxidase MICAL3 G3MWR8.1|MICA3_BOVIN VRGLTGPI GP, GL, TG, VR 0.22 811.4916 406.7517 61.09 Collagen alpha-1(I) chain P02453.3|CO1A1_BOVIN QQEPAAPA AAP, AP, AA 0.21 810.3871 406.2023 13.25 Putative -protein phosphatase auxilin Q27974.1|AUXI_BOVIN M(+15.99)VLASGGQDG LA, GQ, GG, SG, DG 0.18 949.4175 475.7179 65.91 Sterol regulatory element-binding protein cleavage-activating protein (SCAP)A6QM06.1|SCAP_BOVINOxidation (M) DHSSYA YA , SY 0.18 678.2609 340.1344 93.79 Telomerase reverse transcriptase Q27ID4.2|TERT_BOVIN IAPAVVH IA, AP, IAP, AV 0.16 705.4174 353.715 65.59 HTRA1 F1N152.1|HTRA1_BOVIN KKIPKK IP 0.14 740.5272 371.272 29.39 Rho-associated protein kinase 2 Q28021.1|ROCK2_BOVIN VHQLVAH AH AH 0.1 802.4449 402.2299 25.69 Zinc finger protein 574 Q29RK0.1|ZN574_BOVIN KTKKIK 0.05 744.5221 745.5303 47.94 Heat shock protein HSP 90-beta Q76LV1.3|HS90B_BOVIN

Supplementary Excel sheet 7 (Day1_ST_Raw) Peptide ACE inhibitors Renin inhibitorsAntioxidantPeptideRankerMass scorem/z RT Protein Accession PTM RACCPGWGG GW, RA, WG, GG, PG 0.95 905.3636 453.6885 78.23 SCO-spondin P98167|SSPO_BOVIN GAQGPM(+15.99)GPAGGPA, GP, GA, AG, MG, QG 0.76 857.3701 429.6912 18.5 Collagen alpha-1(II) chain P02459.4|CO2A1_BOVINOxidation (M) LSYGPGPL YG, GPL, GP, PL, PG, SY 0.73 802.4225 402.2145 14.88 Alpha-aminoadipic semialdehyde synthase, mitochondrial A8E657.1|AASS_BOVIN AGPAALCSPP GPA, GP, AA, AG, PP 0.71 882.4269 442.2166 17.4 PWWP domain-containing protein MUM1 Q08DK9|MUM1_BOVIN GPGSGG GP , GS , GG, SG , PG 0.63 430.1812 431.1845 90.04 Myozenin-1 (Calsarcin-2) Q8SQ24.1|MYOZ1_BOVIN SSGPLVP GPL, GP, PL, VP, SG 0.6 655.3541 328.6831 75.01 Cathepsin Z P05689|CATZ_BOVIN SRVAGVLGF GF, AG, GV, LG 0.54 904.513 453.2611 92.85 N-terminal kinase-like protein A6QLH6|NTKL_BOVIN LVPPPTLLVP VPP, VP, PT, PP 0.54 1044.658 349.225 26 Sine oculis-binding protein homolog A7XYH9.1|SOBP_BOVIN CFCQVSGY GY, SG, CF 0.54 905.3412 453.6741 65.37 ERO1-like protein alpha A5PJN2.1|ERO1A_BOVIN VVGDGAVGKTCLL VG, GA, GK, GD, DG, AV 0.52 1230.664 616.3414 31.14 Cell division control protein 42 homolog Q2KJ93|CDC42_BOVIN:P62998|RAC1_BOVIN:Q9TU25|RAC2_BOVIN LMLGLPA GLP , GL, LG 0.49 713.4146 357.7137 100.46 Radical S-adenosyl methionine domain-containing protein 1, mitochondrialA5D7B1.1|RSAD1_BOVIN CGSSCN GS 0.49 569.1574 285.5875 48.99 Adseverin Q28046.1|ADSV_BOVIN IALIPPTP IPP, IA, IP, PT, PP 0.48 820.5058 821.5206 24.42 Thymidine kinase P22649|KITH_BHV2H KCTM(+15.99)AP AP 0.46 665.2877 333.6543 27.95 Protein arginine N-methyltransferase 7 A6QQV6.1|ANM7_BOVINOxidation (M) AYTPFHAV AY, AV AY 0.41 904.4443 453.2275 87.28 von Willebrand factor A domain-containing protein 9 Q5EA76|VWA9_BOVIN RMAVLIFT IF, AV 0.39 949.5419 475.7768 86.43 Thyrotropin receptor Q27987.1|TSHR_BOVIN ILLSSF SF 0.39 678.3952 340.202 89.27 Protein YIPF5 Q5E9E8.1|YIPF5_BOVIN EGVQGLLELGL GL, GV, QG, LG, EG 0.36 1126.623 564.3161 31.5 Fatty acid synthase Q71SP7.1|FAS_BOVIN PEDPKDGACS GA, DG 0.34 1017.407 340.1458 97.61 Zinc transporter ZIP4 Q1KZG0.1|S39A4_BOVIN RSAPGGPAGSPS GPA , GP, AP, AG , GS, GG, PG, AGSP 0.32 1039.505 520.7551 44.74 E3 ubiquitin-protein ligase TRIM71 E1BJS7|LIN41_BOVIN TARPLPV PLP, PL, RP, AR 0.31 752.4545 753.468 89.97 Potassium voltage-gated channel subfamily B member 2 Q4ZHA6.1|KCNB2_BOVIN LVPIR IR, VP IR IR 0.31 596.4009 597.403 89.09 Alpha-actinin-2 Q3ZC55.1|ACTN2_BOVIN DEASGGAAAAAGEAGAA, GA, AG, GE, GG, SG, EA GAA 0.3 1203.5 602.754 47.79 Myristoylated alanine-rich C-kinase substrate P12624|MARCS_BOVIN KVGAGVGAFT AF, VG, GA, AG, GV 0.29 905.497 453.7526 63.8 UPF0577 protein KIAA1324-like homolog A7E2Z9.1|K132L_BOVIN TYAGACSSF YA, GA, AG, SF TY 0.28 905.3589 453.6858 67.09 Envelopment polyprotein P21401|GP_RVFVZ:P03518|GP_RVFV RLVAALLAR VAA, LA, AA, AR 0.27 981.6447 491.8296 89.29 RNA polymerase II-associated protein 1 A0JN53.1|RPAP1_BOVIN EPLFSS LF, PL 0.27 678.3224 340.1686 82.53 Torsin-2A A4FUH1.1|TOR2A_BOVIN DGRHCECS GR, DG 0.27 905.312 453.6591 78.18 Uromodulin P48733.1|UROM_BOVIN STSSGGGGGGPGAA GP, AA, GA, GG, SG, PG 0.26 1018.432 510.219 48.03 E3 ubiquitin-protein ligase TRIM71 E1BJS7|LIN41_BOVIN REDLCSGDSGGP GP, GG, SG, GD 0.25 1191.483 596.7524 50.5 Serine protease 45 A2VE36.1|PRS45_BOVIN MPECNS MP 0.24 679.2305 340.6253 94.44 Myogenic factor 5 P17667.1|MYF5_BOVIN QAFVKA VK, AF, KA 0.22 662.3751 332.1924 20.15 Transmembrane protein 251 Q2HJ69|TM251_BOVIN VVLQLGADTIAGDPM IA, GA, AG, LG, GD, LQ 0.21 1498.77 500.593 30.7 Histone deacetylase 8 Q0VCB2|HDAC8_BOVIN HTLCLPVVSTLN LN 0.21 1295.691 648.8537 47.59 Protein transport protein Sec24A A6QNT8.1|SC24A_BOVIN ELVLAGVSLL LVL , LA, AG, GV 0.19 1012.617 507.3138 23.11 Endothelin-converting enzyme 2 Q10711|ECE2_BOVIN LATGLAH LA, GL, TG, AH 0.16 681.381 341.7002 59.71 TRPM8 channel-associated factor 1 A5PJN5.1|TCAF1_BOVIN YGASNM(+15.99)NASS YG, GA, 0.14 1016.387 339.8004 82.29 Cbp/p300-interacting transactivator 2 Q0VCT9.1|CITE2_BOVINOxidation (M) VPITK VP 0.12 556.3584 557.3696 16.41 Krev interaction trapped protein 1 Q6TNJ1.1|KRIT1_BOVIN AEVDELGKVLTPTQVGK, LG, EV, PT, TQ EL 0.12 1597.856 799.9401 50.17 Phosphatidylethanolamine-binding protein 1 P13696|PEBP1_BOVIN RIGLTGTILQNN IG, GL, GT, TG, LQ 0.09 1298.731 650.3686 56.68 DNA excision repair protein ERCC-6-like 2 A3KMX0.3|ER6L2_BOVIN KTPKKAKK AKK, KA 0.08 927.6229 464.8228 62.5 Histone H1.2 P02253.2|H12_BOVIN VKDESC VK 0.07 679.2847 340.6523 55.11 Aldehyde dehydrogenase family 8 member A1 Q0P5F9|AL8A1_BOVIN LVVVR VR 0.06 584.4009 293.2097 64.68 Sodium/myo-inositol cotransporter P53793.1|SC5A3_BOVIN HTKEVGR VG, GR, EV, KE 0.06 825.4457 413.7325 91.91 Supervillin O46385.2|SVIL_BOVIN

Supplementary Excel sheet 8 (Day1_ST_Cooked) Peptide ACE inhibitors Renin inhibitorsAntioxidantPeptideRankerMass Scorem/z RT Protein Accession PTM WPPLP PLP, PL, PP 0.98 608.3322 609.3363 25.93 Secretory carrier-associated membrane protein 3 Q58DR5|SCAM3_BOVIN SGAGGGGGGGGGGGGGGGG GA, AG, GG, SG 0.94 1145.445 573.7248 13.21 Calpain small subunit 1 P13135|CPNS1_BOVIN PGPM(+15.99)GPPGLAGP PGL, GP, LA, GL, AG, MG, PG, GPP, PP GPP 0.88 1062.517 355.179 23.51 Collagen alpha-1(I) chain P02453.3|CO1A1_BOVINOxidation (M) LLGLIILLLW LW, GL, LG LW 0.81 1165.784 389.6008 25.58 Integrin alpha-3 F1MMS9.1|ITA3_BOVIN GFRVL GF, FR 0.76 590.354 591.3599 87.38 11-cis retinol dehydrogenase Q27979.1|RDH1_BOVIN DPPFQIT PP 0.74 816.4017 409.2088 81.5 Prolyl endopeptidase FAP A5D7B7.1|SEPR_BOVIN KAPAQLCEGC AP, EG , KA 0.73 1018.458 510.238 14.62 TBC1 domain family member 1 O97790|TBCD1_BOVIN PGGGGGGAGGRLA RL, LA, GA, AG, GR, GG, PG 0.7 982.4944 492.2514 90.31 Neurexin-1-beta Q28142.2|NRX1B_BOVIN PDQDCC 0.7 679.1942 340.6061 93.06 Transmembrane protein 183 Q5EA86.1|TM183_BOVIN APPPPAEVP AP, VP, EV, PP 0.68 873.4596 437.7358 31.97 Troponin T, fast skeletal muscle Q8MKI3|TNNT3_BOVIN VFGGCA VF, FG, GG 0.62 552.2366 553.2438 89.93 Kelch domain-containing protein 2 Q5E9A7.1|KLDC2_BOVIN RALPPAAPL AAP, LPP, ALPP, PL, AP, RA, AA, PP 0.55 904.5494 453.2783 88 Capsid scaffolding protein P54817|SCAF_BHV1C KDTPRLSLLLVILRL, PR KD 0.55 1479.939 740.9821 31.93 Melanoma-associated antigen D4 A6QLI5.1|MAGD4_BOVIN CCSVCA 0.54 584.1757 293.0963 63.53 Insulin-like growth factor-binding protein 2 P13384.2|IBP2_BOVIN RSPARRGGY GGY, GY, GG, RR, AR 0.47 1018.542 340.5193 72.95 CLK4-associating serine/arginine rich protein A0JNI5.1|CLASR_BOVIN PGSCETSFT GS, PG, SF 0.44 927.3644 464.6863 64.18 Synapse differentiation-inducing protein 1-like A4IFJ1.1|SYN1L_BOVIN GLRGKP GKP, GL, GK, KP KP 0.43 626.3864 627.3958 18.86 Transmembrane protein 215 A7MB05.1|TM215_BOVIN MVPLVP KP, VKP 0.41 654.3774 328.193 19.94 Receptor-type tyrosine-protein phosphatase F A7MBJ4.1|PTPRF_BOVIN GGEAGEGAPGGAGDYAP, GA, AG, GE, GG, GD, EG, EA, PG GGE 0.41 1263.5 422.1744 26.1 Polyadenylate-binding protein 2 Q28165.3|PABP2_BOVIN PEWSSGY GY, SG, EW 0.4 824.334 825.3362 16.83 Collagen alpha-2(I) chain P02465|CO1A2_BOVIN QVQCPPSNANP PP 0.38 1153.519 385.5168 19.31 Inositol hexakisphosphate and diphosphoinositol-pentakisphosphate kinase 1A7Z050.1|VIP1_BOVIN PPSAASAPAAVH AP, AA, PP, AV 0.37 1074.546 359.191 22.61 Poly [ADP-ribose] polymerase 1 P18493.2|PARP1_BOVIN LLAIRR IR, LA, RR, AI IR IR 0.36 740.5021 371.2566 66.86 Pyruvate carboxylase, mitochondrial Q29RK2.2|PYC_BOVIN AHHPGAMDA GA, DA, PG, AH, HP HHP, HH, AH, AHH0.35 905.3813 453.699 68.99 UV-stimulated scaffold protein A F1MX48.2|UVSSA_BOVIN KVISCM KVI 0.32 679.3397 340.6774 55.43 Replicase polyprotein 1a P0C6F4|R1A_BRV1:P0C6V8|R1AB_BRV1 LPTPEAAHAPGPT GP, AP, AA, EA, PG, PT, AH AH 0.31 1257.635 420.2189 17.36 Transcription factor E3 Q05B92|TFE3_BOVIN QLQQM LQQ, LQ 0.3 646.3109 324.1642 23.91 Dynein regulatory complex protein 1 Q32KY1.1|DRC1_BOVIN IGDGQSPEC IG, GQ, GD, DG 0.3 904.3596 453.1871 83.64 YrdC domain-containing protein, mitochondrial Q0VC80.1|YRDC_BOVIN TSM(+15.99)ACETF TF 0.29 904.3307 453.1689 61.57 Protein AMBP P00978.2|AMBP_BOVINOxidation (M) YAKAAGKLKYA, AA, AG, GK, KL, KA LK 0.27 948.5756 475.2992 65.69 Protein - P05307.1|PDIA1_BOVIN PSLSAPALSSSSSAP 0.27 1189.583 595.7963 29.88 CREB-regulated transcription coactivator 2 Q08E26.1|CRTC2_BOVIN VLLVALLP LLP 0.24 836.5735 279.8668 12.13 Corticoliberin Q95MI6.2|CRF_BOVIN STLGAR GA, LG, AR 0.24 603.334 302.6748 53.64 28S ribosomal protein S26, mitochondrial Q3SZ86|RT26_BOVIN KPTGSPCTDGS, TG, KP, PT 0.24 904.396 453.2066 88.11 Transcription factor E2F8 E1BKK0|E2F8_BOVIN LDDGGSDLD GS, GG, DG 0.23 905.3614 453.6866 67.67 Replicase polyprotein 1a P0C6F4|R1A_BRV1:P0C6V8|R1AB_BRV1 EYISVALR EY 0.23 949.5233 475.7671 67.79 Spastin A2VDN5|SPAST_BOVIN VQGAGSSDW GA, AG, GS, QG, AGSS 0.22 905.3879 453.699 79.62 SCO-spondin P98167.2|SSPO_BOVIN LGVSVSAPGGAIASV IA, AP, GA, GV, GG, LG, PG, AI 0.2 1283.709 642.8602 13.73 Tripeptidyl-peptidase 2 A5PK39.1|TPP2_BOVIN KGGGGGTGTAPEKTAP, AP, KG, GT, GG, TG, EK 0.19 1115.557 372.8633 13.64 Forkhead box protein L2 Q6VFT7|FOXL2_BOVIN KTKIIFV IF 0.18 847.5531 424.7821 13 Adenylate kinase isoenzyme 1 P00570.2|KAD1_BOVIN RQGTGAVQMRI GA, GT, QG, TG, AV 0.17 1215.651 608.8348 11.93 Rho GTPase-activating protein 15 A4IF90.1|RHG15_BOVIN GPAAAQEA GPA, GP, AA, EA 0.17 713.3344 357.6724 12.04 Insulin-like 3 O77801.1|INSL3_BOVIN KSEIVDVVKKRV VK, KR, EI 0.15 1398.856 467.2966 29.44 Phospholipid-transporting ATPase IB C7EXK4.4|AT8A2_BOVIN QVIGGAGLDV IG, GA, GL, AG, GG 0.14 927.5025 464.7555 62.87 Transmembrane emp24 domain-containing protein 1 Q2TBK5.1|TMED1_BOVIN KVDGVY VY, GV, DG VY 0.11 679.3541 340.685 95.94 Replicase polyprotein 1a P0C6T9|R1A_CVBLU:P0C6U1|R1A_CVBQ:P0C6U0|R1A_CVBM:P0C6T8|R1A_CVBEN:P0C6X0|R1AB_CVBQ:P0C6W7|R1AB_CVBEN:P0C6W8|R1AB_CVBLU:P0C6W9|R1AB_CVBM VIVRN IVR, VR 0.08 599.3755 300.6923 19.98 Interleukin-3 P49875.1|IL3_BOVIN LTKEDIDL KE 0.07 945.5018 473.7592 57.5 Nuclear factor erythroid 2-related factor 1 A5D7E9.1|NF2L1_BOVIN Supplementary Excel sheet 9 (Day10_ST_Raw) Peptide ACE inhibitors Renin inhibitorsAntioxidantPeptideRankerMass scorem/z RT Protein Accession PTM PTGAPPGGGAL AP, GA, GG, TG, PG, PT, PP 0.81 893.4606 447.7361 17 D site-binding protein Q32PF6.2|DBP_BOVIN SPLPPPE LPP, PL, PP 0.77 735.3802 736.3906 48.25 Collagen alpha-2(XI) chain Q32S24|COBA2_BOVIN EGPQGPPGPVG GP, GPV, VG, QG, EG, PG, GPP, PP, PQ GPP 0.74 990.477 496.2471 47.62 Collagen alpha-1(XI) chain Q28083.1|COBA1_BOVIN PGLIGARGPPGP PGL, GP, IG, GA, GL, PG, AR, GPP, PP GPP 0.63 1087.614 544.8172 48.89 Collagen alpha-1(III) chain P04258.1|CO3A1_BOVIN EDPGSML GS, PG 0.52 747.3109 374.6662 57.61 Limbin Q8MI28.1|LBN_BOVIN VGAVLPGPLLQ VLP, GPL, LPG, GP, PL, VG, GA, PG, LQ, AV 0.5 1062.644 355.222 13.55 Glycerate kinase Q2KJF7.1|GLCTK_BOVIN LILCDC 0.48 678.308 340.161 91.19 Small nuclear ribonucleoprotein-associated protein N Q17QN3|RSMN_BOVIN:Q58DW4|RSMB_BOVIN KEGAGGPPP GP, GA, AG, GG, EG, GPP, PP, KE GPP 0.46 808.4078 405.2137 24.1 Vacuolar fusion protein MON1 homolog A Q17QV2|MON1A_BOVIN HPPAGD AG, GD, PP, HP 0.46 592.2605 297.1387 41.88 Protein argonaute-3 Q6T5B7.2|AGO3_BOVIN SVAIDAGHTSFQF AG, GH, DA, SF, AI 0.43 1378.652 690.3381 55.21 Q5E998.1|CATL2_BOVIN PELPGAP LPG, AP, GA, PG EL, PEL 0.43 679.3541 340.6848 73.67 Urotensin-2 receptor P49220|UR2R_BOVIN GPASCAELSAGPA, GP EL 0.42 904.396 453.204 79.72 SCO-spondin P98167.2|SSPO_BOVIN AYTPFHAV AY, AV AY 0.41 904.4443 453.2251 80.83 von Willebrand factor A domain-containing protein 9 Q5EA76.1|VWA9_BOVIN VAGMGMD GM, AG, MG 0.39 679.267 340.6406 63.34 E3 ubiquitin-protein ligase ZNRF1 F1MM41.2|ZNRF1_BOVIN PGKAR GK, PG, AR, KA 0.34 527.3179 528.3244 61.62 Ras-related protein Rap-1b P61223|RAP1B_BOVIN:Q5E9U4|TYW3_BOVIN:Q2TBI7|IQCC_BOVIN VAAAAAGAGGEMA VAA, AA, GA, AG, GE, GG GGE 0.33 1045.486 349.5042 25.57 Ras GTPase-activating protein 1 P09851.1|RASA1_BOVIN RTVAPLVASGAIQLI VAP, PL, AP, GA, SG, AI 0.32 1507.909 503.6458 32.55 DNA replication complex GINS protein SLD5 A2VE40.1|SLD5_BOVIN KPDGTY GT, DG, KP KP, TY 0.27 679.3177 340.6672 90 Peroxisomal acyl-coenzyme A oxidase 1 Q3SZP5.1|ACOX1_BOVIN ARLLAH RL, IA, AR, AH AH 0.27 679.4129 340.7121 97.09 Envelope glycoprotein H P27599|GH_BHV1C KIAKPLSSLTP PL, IA, IAK, KP KP 0.26 1153.707 385.5748 19.46 Serine/threonine-protein kinase PAK 1 Q08E52.1|PAK1_BOVIN EGAGHPK GA, AG, GH, EG, HP 0.25 694.3398 348.1752 90.49 Rab-like protein 6 Q08DA0|RABL6_BOVIN VMGDIPAAV IPA, IP, AA, MG, GD, AV 0.24 871.4473 436.7348 11.78 Nuclear autoantigenic sperm protein Q2T9P4.2|NASP_BOVIN VAVGAGGAPAP, VG, GA, AG, GG, VAV, AV 0.23 697.3759 349.6966 17.58 RNA-binding protein 42 Q0P5L0.1|RBM42_BOVIN TCGGDLKQ GG, GD LK 0.19 820.3749 274.4671 73.83 Elongation factor Ts, mitochondrial P43896.1|EFTS_BOVIN EPTANGVSM GV, NG, PT 0.18 904.396 453.2045 85.97 Neutral amino acid transporter B(0) Q95JC7|AAAT_BOVIN PGVKK VK, GV, PG 0.16 527.3431 528.3503 55.92 Elongation factor 1-beta Q5E983|EF1B_BOVIN:Q9GMS5|IMPG1_BOVIN TMAGGAADCS AA, GA, AG, GG GAA 0.15 882.3212 442.1715 95.43 Proteasome subunit beta type-5 Q32KL2.1|PSB5_BOVIN GATEISC GA, EI, TE 0.15 679.2847 340.6482 70.63 Secretion-regulating guanine nucleotide exchange factor Q3MHW0.1|SRGEF_BOVIN KGFITIVDVQRGF, KG 0.14 1274.735 425.9161 26.29 Glycerol-3-phosphate dehydrogenase A6QLU1.1|GPDM_BOVIN YVGDVAG VG, AG, GD YVGD 0.13 679.3177 340.6675 56.55 A disintegrin and metalloproteinase with thrombospondin motifs 2P79331.1|ATS2_BOVIN YGDDSD YG, GD 0.13 670.2082 336.1097 82.46 ADP-ribosylation factor GTPase-activating protein 3 Q17R07.1|ARFG3_BOVIN LPLSEDAH PL, DA, AH AH 0.13 880.429 441.2205 34.85 BAG family molecular chaperone regulator 5 Q2TA08.1|BAG5_BOVIN VHQLVAH AH AH 0.1 802.4449 402.2318 25.51 Zinc finger protein 574 Q29RK0.1|ZN574_BOVIN TGVLK GV, TG LK 0.1 516.3271 259.1718 35.89 Elongation factor 1-alpha 1 P68103.1|EF1A1_BOVIN GIGQSQDDSIG, GI, GQ 0.1 905.3726 453.6896 82.51 Rap guanine nucleotide exchange factor 2 F1MSG6.2|RPGF2_BOVIN PIVVT 0.09 527.3319 528.3376 57.69 Glutamyl aminopeptidase Q32LQ0.1|AMPE_BOVIN HRSSV 0.09 584.303 293.1613 64.48 Aldehyde dehydrogenase family 16 member A1 A6QR56.1|A16A1_BOVIN DDDDED 0.09 722.1879 362.1004 47.58 Methionine aminopeptidase 2 Q3ZC89|MAP2_BOVIN:Q3T160|NPM_BOVIN:Q9TT96|ADRB1_BOVIN:A5D796|SPE39_BOVIN:Q3MHH9|ECM2_BOVIN:Q2KJJ0|PRP6_BOVIN VVVAAKK AKK, VAA, AA 0.05 713.4799 357.7481 25.38 Uncharacterized protein KIAA2013 homolog Q2KHV9|K2013_BOVIN PKKGTVETKG, GT, VE 0.05 858.4811 430.2455 65.78 Collagen alpha-1(XVII) chain A6QPB3.1|COHA1_BOVIN

Supplementary Excel sheet 10 (Day10_ST_Cooked)

Peptide ACE inhibitors Renin inhibitorsAntioxidantPeptideRankerMass score m/z RT Protein Accession PTM PCCAPCPF AP 0.99 836.3019 419.1586 13.46 Phosphatidylserine decarboxylase proenzyme, mitochondrial Q58DH2|PISD_BOVIN GRFKRFRKKFKKLFKKLSP RF, LSP, LF, KR, FR, GR, KF, KL KF FKK 0.85 2438.527 813.8572 50.62 Cathelicidin-6 P54228.1|CTHL6_BOVIN QPPLLL PL, PP 0.84 679.4268 340.7224 95.15 Cyclin-dependent kinase 13 E1BB52|CDK13_BOVIN GGGALGGGPAL GPA, GP, GA, GG, LG 0.81 825.4344 413.7277 101.3 Mitotic-spindle organizing protein 2 A5PJV8.1|MZT2_BOVIN PLPVPPPVG PLP, VPP, PL, VP,VG, PP 0.77 871.5167 291.5123 12.63 Retinal guanylyl cyclase 2 O02740.1|GUC2F_BOVIN KPLPPSKPRK PLP, LPP, PR, PL, KP, PP KP 0.74 1146.724 383.2518 13.63 AT-rich interactive domain-containing protein 5A Q3SWY1.1|ARI5A_BOVIN GGAPSPSSLSLPP LPP, AP, GA, GG, PP 0.73 1165.598 389.5432 16.42 INO80 complex subunit E Q29RS4|IN80E_BOVIN ENPFAC 0.72 679.2635 340.6374 92.52 Zinc finger protein OZF Q28151|OZF_BOVIN LPECALLL 0.69 870.4885 291.1688 14.3 NACHT, LRR and PYD domains-containing protein 5 Q647I9.1|NALP5_BOVIN VINDCCRGAM(+15.99) GA 0.68 1096.446 366.4897 21.42 TRPM8 channel-associated factor 1 A5PJN5|TCAF1_BOVINOxidation (M) EEPSSCSAM(+15.99)AMGR GR, MG 0.66 1370.527 457.8513 32.15 DNA-(apurinic or apyrimidinic site) lyase 2 Q5E9N9|APEX2_BOVINOxidation (M) PPAPK AP, PAP, PP 0.6 508.3009 509.3117 89.46 Lymphocyte antigen 6 complex locus protein G6f Q0V881|LY66F_BOVIN:Q2T9S4|PGP_BOVIN:Q2KJE5|G3PT_BOVIN:Q5BIR3|THSD1_BOVIN:A5PKL1|OXR1_BOVIN KLLSLGKHGRL RL, GR, GK, HG, LG, KL 0.58 1220.772 407.9312 13.85 Telomerase reverse transcriptase Q27ID4.2|TERT_BOVIN PLGLTCGM(+15.99)VCPT PLG, PL, GM, GL, LG, PT 0.56 1206.545 403.186 24.64 Dihydropyrimidine dehydrogenase [NADP(+)] Q28007.1|DPYD_BOVINOxidation (M) PPHGEAKAGSSTLPP LPP, AG, GS, HG, GE, EA, KA, PP, PH, AGSS PHG, EAK 0.53 1444.731 482.5863 33.59 Brefeldin A-inhibited guanine nucleotide-exchange protein 1 O46382.1|BIG1_BOVIN DALLGGFVTGASFT GF, GA, DA, GG, LG, TG, SF 0.46 1354.677 452.566 34.6 Sulfate transporter Q9BEG8|S26A2_BOVIN PPPVRALTPQK RA, VR, QK, PP, PQ 0.45 1202.714 401.9124 16.57 Protein transport protein Sec24A A6QNT8.1|SC24A_BOVIN HSLGGGTGSGM(+15.99)GT GM, GS, MG, GT, GG, SG, LG, TG 0.44 1133.477 378.8293 16.79 Tubulin beta-4A chain Q3ZBU7|TBB4A_BOVIN:Q2KJD0|TBB5_BOVIN:Q3MHM5|TBB4B_BOVIN:Q6B856|TBB2B_BOVIN:Q2HJ81|TBB6_BOVIN:Q2T9S0|TBB3_BOVINOxidation (M) VVPPPGAK VPP, VP, GA, PG, PP, VVPP 0.43 763.4592 382.7384 14.58 DNA excision repair protein ERCC-1 Q1LZ75|ERCC1_BOVIN M(+15.99)GPADPASESTG GPA, GP, MG, TG 0.4 1134.45 568.2292 94.67 dCTP pyrophosphatase 1 Q32KY6.1|DCTP1_BOVINOxidation (M) VLPGVGVGGPGI VLP, LPG, GP, VG, GI, GV, GG, PG 0.39 1020.597 511.304 14.85 Elastin P04985|ELN_BOVIN FVTPKVIKLL KL IKL, KVI 0.39 1156.758 386.5951 18.39 Endoribonuclease Dicer Q6TUI4|DICER_BOVIN AISVALLLLVV AI 0.36 1109.742 370.9189 30.82 Basal cell adhesion molecule Q9MZ08.2|BCAM_BOVIN LPPAAAVAAAAATA LPP, VAA, AA, PP, AV 0.35 1164.65 389.2246 16.61 Poly(U)-binding-splicing factor PUF60 Q2HJG2|PUF60_BOVIN KKAGTLMR AG, GT, KA 0.34 903.5324 302.1828 18.61 Repressor of yield of DENV protein homolog Q32L09|CS066_BOVIN KAVLKW KW, KA, AV 0.32 743.4694 372.7441 17.02 RNA-directed RNA polymerase L P27316|L_RVFVZ QLAHDPRQ PR, LA, AH AH 0.31 963.4886 322.1732 12.55 Retinol dehydrogenase 8 Q9N126|RDH8_BOVIN LLLLLLLVVL 0.31 1120.82 374.6133 14.62 Beta-defensin 10 P46168.3|DFB10_BOVIN EAVIGHFNG IG, GH, EA, NG, GHF, AV 0.31 942.4559 315.1577 15.38 RNA-binding motif, single-stranded-interacting protein 1 Q3ZBP3.1|RBMS1_BOVIN FCLLGTEGTP GT, LG, EG, TE 0.3 1036.49 346.5033 13.54 N-acyltransferase Q2KIR7.2|GLYAT_BOVIN PLSAYLTAT YL, AY, PL AY 0.29 935.4964 312.8384 14.55 Replicase polyprotein 1ab P0C6V8|R1AB_BRV1:P0C6F4|R1A_BRV1 LGVSREVFDFS VF, GV, LG, EV 0.27 1254.624 419.2145 18.55 Nuclear factor erythroid 2-related factor 2 Q5NUA6.2|NF2L2_BOVIN ALRCACPEDA DA 0.27 1047.448 524.7283 19.01 F-box/LRR-repeat protein 8 Q08DG4.1|FBXL8_BOVIN LPTPKVIGI IG, GI, PT KVI 0.26 936.6008 313.2074 13.27 Heat shock 70 kDa protein 13 Q2TBX4.1|HSP13_BOVIN KIMTVLTVGIF IF, VG, GI 0.26 1220.72 407.9135 13.59 Short transient receptor potential channel 1 O18784|TRPC1_BOVIN IVDANLSVLNLVIV DA, LN 0.26 1480.887 494.6387 38.42 40S ribosomal protein S6 Q5E995.1|RS6_BOVIN FGTIILNKIV FG, GT, NK, LN 0.26 1116.691 373.2342 16.89 Phospholipase-D-like protein K4 P18377|K4_VACCW TVHM(+15.99)SALGLPG GLP, LPG, GL, LG, PG 0.25 1097.554 366.8603 17.12 Mitochondrial import receptor subunit TOM40B A6QR22|TM40L_BOVINOxidation (M) M(+15.99)MGNAEL MG EL 0.23 780.3146 391.1668 100.91 Exocyst complex component 3 Q0V8C2.1|EXOC3_BOVINOxidation (M) LLGIIAGLSLSA IA, GI, GL, AG, LG 0.23 1126.696 376.5745 12.75 Urea transporter 1 Q5QF96|UT1_BOVIN KTGVKLPGGLEPK LPG, VK, GL, GV, GG, TG, PG, KL VKL 0.23 1322.792 441.9384 29.13 Insulin-like growth factor-binding protein 4 Q05716.1|IBP4_BOVIN EGPVITALTP GP, GPV, EG 0.23 996.5491 333.1914 21.4 Lon protease homolog, mitochondrial Q59HJ6|LONM_BOVIN ITSPVRLIH RL, VR 0.21 1034.624 345.8829 13.33 Hepatocyte cell adhesion molecule A4FUY1.1|HECAM_BOVIN LLKGVLAL LA, KG, GV LK 0.19 825.5687 413.7904 99.56 Replicase polyprotein 1ab P0C6V8|R1AB_BRV1:P0C6F4|R1A_BRV1 GTSTMSGHYVCH HY, GH, GT, SG 0.19 1278.512 427.1744 23.45 Ubiquitin carboxyl-terminal hydrolase 13 E1BMF7.2|UBP13_BOVIN SHTSCKSQSC 0.17 1066.417 356.4766 26.33 Protein FAM193B A7MB40.1|F193B_BOVIN KKPAN KP KP 0.16 556.3333 279.1729 100.76 Transmembrane protein 108 A6QLF8|TM108_BOVIN VHIEAGAFTN AF, GA, AG, EA, IE 0.15 1057.519 529.7708 21.44 Lutropin-choriogonadotropic hormone receptor Q28005.1|LSHR_BOVIN KAPKGKSV AP, KG, GK, KA 0.15 813.5072 407.7587 13.73 Translation machinery-associated protein 16 Q3T071|TMA16_BOVIN VRHSSSPY VR RHS 0.14 931.4512 311.4889 16.4 Centrosomal protein of 57 kDa Q865V0.1|CEP57_BOVIN QVLLAH LA, AH 0.14 679.4017 340.7092 91.95 Transmembrane protein 245 E1BD52|TM245_BOVIN KVLQKKAILK QK, KA, LQ, AI KAI, LK 0.14 1167.807 390.2792 19.53 Serine/threonine-protein kinase Sgk1 A7MB74.1|SGK1_BOVIN KLSIGNITTK IG, ITT, KL 0.13 1073.644 358.8878 28.95 Adaptin ear-binding coat-associated protein 1 Q3T093.1|NECP1_BOVIN VESRPRGGARAE PR, RP, RA, GA, GG, AR, VE 0.12 1283.669 642.8397 12.58 NF-kappa-B inhibitor zeta Q9BE45|IKBZ_BOVIN GQDSDSISSSSS GQ 0.12 1155.453 578.7338 91.43 Zinc finger SWIM domain-containing protein 8 A7E305.1|ZSWM8_BOVIN IVTGAPRHQ PR, AP, GA, TG 0.11 977.5406 489.7802 17.51 Collagen alpha-1(I) chain P02453.3|CO1A1_BOVIN TQQGDGKGD KG, GK, QG, GD, DG, TQ 0.09 904.3886 905.3932 15.76 Cyclic nucleotide-gated olfactory channel Q03041|CNGA2_BOVIN LATKH LA 0.09 568.3333 285.1731 99.59 Dihydropteridine reductase Q3T0Z7|DHPR_BOVIN:Q0P5J4|K1C25_BOVIN:Q0P5J6|K1C27_BOVIN:Q148H6|K1C28_BOVIN NGDDEAAE AA, GD, EA, NG 0.08 819.2882 274.1027 28.65 Microtubule-associated protein RP/EB family member 1 Q3ZBD9|MARE1_BOVIN KVTHLSTLQ HL, LQ HL 0.08 1025.587 342.8694 16.09 Hydroxyacylglutathione hydrolase, mitochondrial Q3B7M2|GLO2_BOVIN STIVHAVQR AV 0.07 1009.567 505.7943 18.1 Ribose-5-phosphate isomerase Q3T186.2|RPIA_BOVIN EVAGVQRAVVEAG RA, AG, GV, EA, EV, VE, AV 0.07 1283.683 642.855 12.81 Short transient receptor potential channel 2 homolog O62826.1|TRPC2_BOVIN QVHAVQ AV 0.06 680.3606 681.3617 91.35 Mediator of RNA polymerase II transcription subunit 17 Q5BIR6|MED17_BOVIN EVSATEL EV, TE EL 0.06 747.3651 374.6894 42.13 Calpain small subunit 1 P13135 |CPNS1_BOVIN TTLKVV LK 0.03 659.4218 330.7178 17.24 Transmembrane protein 120B A6QPF8|T120B_BOVIN

Supplementary Excel sheet 11 (Day20_ST_Raw)

Peptide ACE inhibitors Renin inhibitors Antioxidant PeptideRankerMass scorem/z RT Protein Accession PTM CSAAGFF AA, GF, AG 0.96 701.2843 351.6472 59.57 Lutropin-choriogonadotropic hormone receptor Q28005|LSHR_BOVIN EPAFM AF 0.81 593.2519 297.6359 92.73 Natural killer cells antigen CD94 Q863H3.2|KLRD1_BOVIN PGAAGGAEDGFF AA, GF, GA, AG, GG, PG, DG 0.79 1094.467 365.8293 27.38 Coatomer subunit alpha Q27954|COPA_BOVIN ALAPGHLGGLVL LVL, LAP, AP, LA, GL, GH, HL, GG, LG, PG HL 0.79 1116.666 559.3378 49.86 Homeobox protein PKNOX1 Q2HJ84.1|PKNX1_BOVIN GPGYYNPNGH GY, GP, GH, NG, PG, GYY, YYN 0.74 1074.452 359.1554 22.66 O(6)-methylguanine-induced apoptosis 2 A6QQ60.1|STPG1_BOVIN PADGSMC GS, DG 0.73 679.2305 340.6201 87.64 Spondin-1 Q9GLX9|SPON1_BOVIN CFGGAGG GA, AG, FG, GG, CF 0.72 567.2111 568.2162 54.57 5-oxoprolinase Q75WB5|OPLA_BOVIN LMAPGP GP, AP, PG, 0.68 584.2992 293.1567 69.83 Mitochondrial enolase superfamily member 1 Q2KIA9.1|ENOF1_BOVIN MGPCPGE GP, MG, GE, PG 0.67 689.2513 345.6329 44.51 SCO-spondin P98167|SSPO_BOVIN ILLPL LLP, PL 0.62 567.3995 284.7073 56.68 Collagen alpha-1(XVII) chain A6QPB3.1|COHA1_BOVIN LPLPGPTLA PLP, LPLP, LPG, GP, PL, LA, PG, PT 0.6 877.5273 439.7697 35.86 Methylmalonic aciduria and homocystinuria type C protein homologQ5E9C8|MMAC_BOVIN HGSGM(+15.99) GM, GS, HG, SG 0.59 503.1798 504.1836 14.35 DNA helicase MCM8 E1BPX4.2|MCM8_BOVINOxidation (M) PEGGCCN GG, EG 0.57 678.2101 340.1153 77.65 ETS translocation variant 1 Q2KIC2|ETV1_BOVIN ENSGFDGM(+15.99) GF, GM, SG, DG 0.57 871.3018 291.4419 11.93 Ubiquitin carboxyl-terminal hydrolase 37 F1N5V1.1|UBP37_BOVINOxidation (M) KIPCIKFSK IP, KF KF 0.56 1062.626 355.2167 14.17 Mitochondrial inner membrane protein OXA1L Q3SYV3.1|OXA1L_BOVIN KDTPRLSLLLVIL RL, PR KD 0.55 1479.939 494.318 31.86 Melanoma-associated antigen D4 A6QLI5|MAGD4_BOVIN DLPSPM(+15.99)EDLP, ME 0.55 803.3371 804.3397 44.35 Homeobox protein aristaless-like 4 Q4LAL6.1|ALX4_BOVIN Oxidation (M) AICDDGATYC GA, DG, AI TY 0.54 1030.374 344.467 23.59 Complement factor B P81187.2|CFAB_BOVIN GGGGGGGDM(+15.99)GG, GD 0.5 679.2231 340.6222 54.97 U1 small nuclear ribonucleoprotein 70 kDa Q1RMR2.1|RU17_BOVINOxidation (M) PEGGGGP GP, GG, EG 0.45 569.2445 570.2463 63.31 Electron transfer flavoprotein-ubiquinone , mitochondrialQ2KIG0.1|ETFD_BOVIN RACCNN RA 0.44 679.2531 340.6349 62 Transmembrane 4 L6 family member 20 Q3T0Z4.1|T4S20_BOVIN PLSDP PL 0.44 527.2591 528.2634 57.67 Zinc finger protein 227 A0JNB1.1|ZN227_BOVIN LALPPASPG LPP, ALPP, LALPP, LA, PG, PP 0.43 821.4647 411.7369 14.41 Poly(rC)-binding protein 4 Q0VCU0.1|PCBP4_BOVIN DPSEDPGA GA, PG 0.43 786.3032 394.1561 12.32 Pyridoxal phosphate phosphatase Q3ZBF9.1|PLPP_BOVIN SGAVTPSPVG VG, GA, SG, AV 0.4 870.4447 436.228 13.66 XIAP-associated factor 1 Q58DH1.2|XAF1_BOVIN FPPDVGGNVDYKN FP, VG, GG, YK, PP 0.4 1420.662 711.3442 39.21 Myosin regulatory light chain 2, skeletal muscle isoform Q0P571.1|MLRS_BOVIN QDGGGGASSGGGGA,GG, SG, DG 0.39 905.3475 453.6847 89.99 Cyclin-dependent kinase 13 E1BB52.1|CDK13_BOVIN DLTLVLRGCQPHLLVL, HL, PH HL, PHL 0.39 1463.792 732.8975 33.64 Proline-rich protein 19 Q0P5M0|PRR19_BOVIN PVLLR LLR 0.38 596.4009 597.4055 89.08 Spondin-1 Q9GLX9|SPON1_BOVIN LCPKKGGA GA, KG, GG 0.38 772.4265 387.2232 58.22 Protein Jade-1 Q5E9T7.1|JADE1_BOVIN RGLFTNGSCAD LF, GL, GS, NG 0.36 1139.503 570.762 93.65 Extracellular calcium-sensing receptor P35384.1|CASR_BOVIN QPDFSS 0.35 679.2813 340.648 77.15 Acyl-CoA synthetase family member 2, mitochondrial Q17QJ1.1|ACSF2_BOVIN RQKDPDALLAGV LA, AG, DA, GV, QK KD 0.34 1281.704 428.2433 15.96 Integrin alpha-L P61625.1|ITAL_BOVIN PAKPGV GV, PG, KP KP 0.33 567.338 568.3484 55.29 Elastin P04985.1|ELN_BOVIN GAASGG AA, GA, GG, SG GAA 0.32 418.1812 419.1863 75.46 Transmembrane protein 245 E1BD52.1|TM245_BOVIN VVLFGTSAFIP LF, IP, AF, FG, GT 0.31 1149.643 575.8274 17.63 Sideroflexin-4 Q3T0M2.1|SFXN4_BOVIN TYAGACSSF YA, GA, AG, SF TY 0.28 905.3589 453.6826 88.03 Envelopment polyprotein P21401|GP_RVFVZ:P03518|GP_RVFV PVLIR IR IR IR 0.27 596.4009 597.4055 89.08 Spondin-1 Q9GLX9.1|SPON1_BOVIN VQGAGSSDW GA, AG, GS, QG, AGSS 0.22 905.3879 453.6978 79.2 SCO-spondin P98167|SSPO_BOVIN HAKPKLVI KL, KP KP 0.22 904.5858 453.3005 85.73 Acyl-CoA synthetase short-chain family member 3, mitochondrialA7MB45.1|ACSS3_BOVIN HSLGGGTGSG GS, GT, GG, SG, LG, TG 0.21 828.3726 415.1938 51.71 Tubulin beta-5 chain Q2KJD0.1|TBB5_BOVIN RNIPAV IPA, IP, AV 0.19 668.3969 335.2071 69.35 Tetratricopeptide repeat protein 30A A2VE45|TT30A_BOVIN QPSSDGSDD GS, DG, SDGS 0.18 906.3203 454.1715 42.86 Retrotransposon-like protein 1 Q52QI2.2|RTL1_BOVIN SSCSSS 0.17 556.1799 279.0995 91.95 Myocyte-specific enhancer factor 2C Q2KIA0.1|MEF2C_BOVIN KGKKAP AP, KG, GK, KA 0.16 627.4067 628.4138 46.71 Signal recognition particle receptor subunit alpha Q3MHE8.1|SRPR_BOVIN RSKVL 0.15 601.3911 301.7047 17.07 Pyruvate carboxylase, mitochondrial Q29RK2.2|PYC_BOVIN QRISVSSPG PG 0.15 929.493 465.7549 38.38 Protein phosphatase 1 regulatory subunit 37 A7Z026.1|PPR37_BOVIN MAAVGSGGSTATAA, VG, GS, GG, SG, AV 0.14 1008.455 505.2314 19.02 UDP-galactose translocator Q58DA6.1|S35A2_BOVIN ILLVK VK 0.13 584.4261 293.2231 68.36 E3 ubiquitin-protein ligase LAP Q91T40|LAP_LSDV:P05807|NTP1_VACCW LVIIK 0.1 584.4261 293.2185 72.02 ATG4B Q6PZ03.1|ATG4B_BOVIN TLDSSYVV SY 0.07 882.4335 442.222 26.55 Spindle and centriole-associated protein 1 Q2T9X8.1|SPICE_BOVIN

Supplementary Excel sheet 12 (Day20_ST_Cooked)