DOCSLIB.ORG

Prediction of RNA-Binding Proteins from Primary Sequence by a Support Vector Machine Approach

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Downloaded from rnajournal.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press BIOINFORMATICS Prediction of RNA-binding proteins from primary sequence by a support vector machine approach LIAN YI HAN,1 CONG ZHONG CAI,1,2 SIEW LIN LO,3 MAXEY C.M. CHUNG,3 and YU ZONG CHEN1 1Department of Computational Science, National University of Singapore, Singapore 117543 2Department of Applied Physics, Chongqing University, Chongqing 400044, People’s Republic of China 3Department of Biochemistry, National University of Singapore, Singapore, 117597 ABSTRACT Elucidation of the interaction of proteins with different molecules is of significance in the understanding of cellular processes. Computational methods have been developed for the prediction of protein–protein interactions. But insufficient attention has been paid to the prediction of protein–RNA interactions, which play central roles in regulating gene expression and certain RNA-mediated enzymatic processes. This work explored the use of a machine learning method, support vector machines (SVM), for the prediction of RNA-binding proteins directly from their primary sequence. Based on the knowledge of known RNA- binding and non-RNA-binding proteins, an SVM system was trained to recognize RNA-binding proteins. A total of 4011 RNA-binding and 9781 non-RNA-binding proteins was used to train and test the SVM classification system, and an independent set of 447 RNA-binding and 4881 non-RNA-binding proteins was used to evaluate the classification accuracy. Testing results using this independent evaluation set show a prediction accuracy of 94.1%, 79.3%, and 94.1% for rRNA-, mRNA-, and tRNA-binding proteins, and 98.7%, 96.5%, and 99.9% for non-rRNA-, non-mRNA-, and non-tRNA-binding proteins, respec- tively. The SVM classification system was further tested on a small class of snRNA-binding proteins with only 60 available sequences. The prediction accuracy is 40.0% and 99.9% for snRNA-binding and non-snRNA-binding proteins, indicating a need for a sufficient number of proteins to train SVM. The SVM classification systems trained in this work were added to our Web-based protein functional classification software SVMProt, at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi. Our study suggests the potential of SVM as a useful tool for facilitating the prediction of protein–RNA interactions. Keywords: RNA-binding proteins; RNA–protein interactions; rRNA; mRNA; tRNA; snRNA; support vector machine INTRODUCTION al.1999; Marcotte et al.1999), phylogenetic profile (Pelle- grini et al.1999), gene neighbor (Dandekar et al.1998; Knowledge regarding how proteins interact with each other Overbeek et al.1999), and interacting domain profile pair and with other molecules is essential in the understanding (Eisen et al.1998) methods. of cellular processes (Siomi and Dreyfuss 1997; Draper Although progress has been made in the development of 1999; Lengeler 2000; Downward 2001).With the accumu- predictive methods for protein–protein interactions, insuf- lation of sequence information, attention has been paid to ficient attention has been paid to the development of pre- the development of methods for the prediction of protein dictive methods for protein–RNA interactions.Most cellu- function (Fetrow and Skolnick 1998) and interactions lar RNAs work in concert with protein partners, and pro- (Dandekar et al.1998; Overbeek et al.1999; Bock and tein–RNA interactions are critically important in regulation Gough 2001) from sequence.Several computational meth- of different steps of gene expression (Siomi and Dreyfuss ods have been developed for the prediction of protein– 1997).Moreover, binding of proteins to some catalytic RNA protein interactions using support vector machines (SVM; molecules is known to activate or enhance the activity of Bock and Gough 2001) and for the prediction of protein– these molecules (Frank and Pace 1998).Therefore, predic- protein interaction maps by Rosetta/gene fusion (Enright et tion of protein–RNA interactions is of significance in a more comprehensive understanding of how cellular pro- Reprint requests to: Yu Zong Chen, Department of Computational cesses and networks work. Science, National University of Singapore, Blk SOC1, Level 7, 3 Science RNA recognition by proteins is primarily mediated by Drive 2, Singapore 117543; e-mail: [email protected]; fax: 65-6774-6756. Article and publication are at http://www.rnajournal.org/cgi/doi/ certain classes of RNA binding domains and motifs (Draper 10.1261/rna.5890304. 1999; Fierro-Monti and Mathews 2000; Peculis 2000; Perez- RNA (2004), 10:355–368.Published by Cold Spring Harbor Laboratory Press.Copyright © 2004 RNA Society. 355 Downloaded from rnajournal.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press Hanetal. Canadillas and Varani 2001).Hence, as in the case of pro- casting (Liong and Sivapragasam 2002), cancer diagnosis tein–protein interactions (Casari et al.1995; Pawson 1995; (Furey et al.2000; Ramaswamy et al.2001; Fritsche 2002), Elcock and McCammon 2001), correlated patterns of se- microarray gene expression data analysis (Brown et al. quence and substructure in RNA-binding proteins can be 2000), inhibitor classification (Burbidge et al.2001), pre- recognized to bind to specific RNA sequences and folds. diction of protein solvent accessibility (Yuan et al.2002), The SVM approach, successfully used for the prediction of protein fold recognition (Ding and Dubchak 2001), protein protein–protein interactions from primary sequences (Bock secondary structure prediction (Hua and Sun 2001), pre- and Gough 2001), is therefore expected to be applicable for diction of protein–protein interaction (Bock and Gough recognizing this pattern and thus predicting RNA-binding 2001) and protein functional class classification (Karchin et proteins from protein primary sequence. al.2002; Cai et al.2003a).These studies have demonstrated In the present study, we explored the use of SVM for the that SVM is consistently superior to other supervised learn- prediction of RNA-binding proteins from protein primary ing methods including classification methods (Brown et al. sequence.The SVM method was used for the prediction of 2000; Burbidge et al.2001; Cai et al.2002b).In the present individual classes of rRNA-, mRNA-, and tRNA-binding study, SVM was further tested regarding its capability to proteins, as well as all RNA-binding proteins.There are predict protein–RNA interactions. other groups of RNA-binding proteins, such as snRNA- binding and snoRNA-binding proteins, with small numbers RESULTS AND DISCUSSION of proteins and fewer available sequences (Tomasevic and Peculis 1999; Singh 2002).A search of protein family and Overall prediction accuracy sequence databases revealed a total of 60 sequences of The numbers and prediction results of specific classes of snRNA-binding proteins and 21 sequences of snoRNA- RNA-binding proteins and non-class members are given in binding proteins, which is fewer than the 80–100 sequences Table 1.In the able, TP stands for true positive (correctly typically needed to properly train an SVM protein classifi- predicted RNA-binding proteins of a specific class), FN for cation system (Cai et al.2003a).Nevertheless, to evaluate its false negative (specific class of RNA-binding proteins incor- performance on classification of a small protein class, SVM rectly predicted as non-class members), TN for true nega- was used for the prediction of snRNA-binding proteins. tive (correctly predicted non-class members), and FP for Proteins of small RNA-binding classes as well as other false positive (non-class members incorrectly predicted as a RNA-binding proteins were included in training and testing specific class of RNA-binding proteins).The predicted sen- the SVM classification of all RNA-binding proteins. sitivity (SE) for rRNA-, mRNA-, tRNA-, and snRNA-bind- SVM is a relatively new and promising algorithm for ing proteins and all RNA-binding proteins, which measures binary classification by means of supervised learning which the overall prediction accuracy for each class of RNA-bind- was originally developed by Vapnik and his coworkers (Vap- ing proteins, is 94.1%, 79.3%, 94.1%, 41.0%, and 97.8%, nik 1995; Burges 1998) and applied to a wide range of respectively.The predicted specificity ( SP) for non-rRNA-, problems including text categorization (Drucker et al.1999; non-mRNA-, non-tRNA-, and non-snRNA-binding proteins Kim et al.2001; de Vel et al.2001), hand-written digit and all non-RNA-binding proteins, which measures predic- recognition (Vapnik 1995), tone recognition (Thubthong tion accuracy for each group of non-RNA-binding proteins, is and Kijsirikul 2001), image classification and object detec- 98.7%, 96.5%, 99.9%, 99.7%, and 96.0%, respectively. tion (Ben-Yacoub et al.1999; Karlsen et al.2000; Papageor- A direct comparison with results from previous protein giou and Poggio 2000; Huang et al.2002), flood stage fore- studies is inappropriate, because of the differences in the TABLE 1. Prediction accuracies and number of positive and negative samples in the training, testing, and independent evaluation set of rRNA-, mRNA-, tRNA-, and snRNA-binding proteins and of all RNA-binding proteins Training set Testing set Independent evaluation set positive negative positive negative Protein family positive negative TP FN TN FP TP FN SE (%) TN FP SP (%) Q (%) RNA-binding 2161 2965 1844 6 6802 14 437 10 97.8 4685 196 96.0 96.1 rRNA-binding 708 972 1243 2 9031 13 95 6 94.1 4931 66 98.7 98.6 mRNA-binding 277 2106 129 0 10164 0 130 34 79.3 5833 213 96.5 96.0 tRNA-binding 94 792 114 0 9295 2 48 3 94.1 5028 5 99.9 99.8 snRNA-binding 33 1988 7 0 10373 1 9 11 41.0 6133 18 99.7 99.5 Predicted results are given in TP (true positive), FN (false negative), TN (true negative), FP (false positive), sensitivity SE = TP/(TP + FN), specificity SP = TN/(TN+FP), and Q (overall accuracy, Q=(TN+TP)/(TP+FN+TN+FP)).
Recommended publications
  • METACYC ID Description A0AR23 GO:0004842 (Ubiquitin-Protein Ligase

    METACYC ID Description A0AR23 GO:0004842 (Ubiquitin-Protein Ligase

    Electronic Supplementary Material (ESI) for Integrative Biology This journal is © The Royal Society of Chemistry 2012 Heat Stress Responsive Zostera marina Genes, Southern Population (α=0.
  • WO 2017/014762 Al 26 January 2017 (26.01.2017) P O P C T

    WO 2017/014762 Al 26 January 2017 (26.01.2017) P O P C T

    (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2017/014762 Al 26 January 2017 (26.01.2017) P O P C T (51) International Patent Classification: DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, C12Q 1/68 (2006.01) HN, HR, HU, ID, IL, IN, IR, IS, JP, KE, KG, KN, KP, KR, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, (21) International Application Number: MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PCT/US201 5/0414 15 PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, (22) International Filing Date: SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 2 1 July 20 15 (21 .07.2015) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, (71) Applicant: OMNIOME, INC. [US/US]; 4225 Executive TZ, UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, Square, Suite 440, La Jolla, California 92037 (US). TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, (72) Inventors: VIJAYAN, Kandaswamy; 4465 Vision Drive, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, Unit 6, San Diego, California 92121 (US).
  • Mapping Active Site Residues in Glutamate-5-Kinase. the Substrate Glutamate and the Feed-Back Inhibitor Proline Bind at Overlapping Sites

    Mapping Active Site Residues in Glutamate-5-Kinase. the Substrate Glutamate and the Feed-Back Inhibitor Proline Bind at Overlapping Sites

    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector FEBS Letters 580 (2006) 6247–6253 Mapping active site residues in glutamate-5-kinase. The substrate glutamate and the feed-back inhibitor proline bind at overlapping sites Isabel Pe´rez-Arellanoa, Vicente Rubiob, Javier Cerveraa,* a Centro de Investigacio´n Prı´ncipe Felipe, Avda. Autopista del Saler, 16, Valencia 46013, Spain b Instituto de Biomedicina de Valencia (IBV-CSIC), Jaime Roig 11, Valencia 46010, Spain Received 22 August 2006; revised 10 October 2006; accepted 12 October 2006 Available online 20 October 2006 Edited by Stuart Ferguson (a domain named after pseudo uridine synthases and archaeo- Abstract Glutamate-5-kinase (G5K) catalyzes the controlling first step of proline biosynthesis. Substrate binding, catalysis sine-specific transglycosylases) domain [10]. AAK domains and feed-back inhibition by proline are functions of the N-termi- bind ATP and a phosphorylatable acidic substrate, and make nal 260-residue domain of G5K. We study here the impact on a phosphoric anhydride [11]. The function of the PUA domain these functions of 14 site-directed mutations affecting 9 residues (a domain characteristically found in some RNA-modifying of Escherichia coli G5K, chosen on the basis of the structure of enzymes) [12] is not known. By deleting the PUA domain of the bisubstrate complex of the homologous enzyme acetylgluta- E. coli G5K we showed [10] that the isolated AAK domain, mate kinase (NAGK). The results support the predicted roles as the complete enzyme, forms tetramers, catalyzes the reac- of K10, K217 and T169 in catalysis and ATP binding and of tion and is feed-back inhibited by proline.
  • Supplementary Materials

    Supplementary Materials

    Supplementary Materials Figure S1. Differentially abundant spots between the mid-log phase cells grown on xylan or xylose. Red and blue circles denote spots with increased and decreased abundance respectively in the xylan growth condition. The identities of the circled spots are summarized in Table 3. Figure S2. Differentially abundant spots between the stationary phase cells grown on xylan or xylose. Red and blue circles denote spots with increased and decreased abundance respectively in the xylan growth condition. The identities of the circled spots are summarized in Table 4. S2 Table S1. Summary of the non-polysaccharide degrading proteins identified in the B. proteoclasticus cytosol by 2DE/MALDI-TOF. Protein Locus Location Score pI kDa Pep. Cov. Amino Acid Biosynthesis Acetylornithine aminotransferase, ArgD Bpr_I1809 C 1.7 × 10−4 5.1 43.9 11 34% Aspartate/tyrosine/aromatic aminotransferase Bpr_I2631 C 3.0 × 10−14 4.7 43.8 15 46% Aspartate-semialdehyde dehydrogenase, Asd Bpr_I1664 C 7.6 × 10−18 5.5 40.1 17 50% Branched-chain amino acid aminotransferase, IlvE Bpr_I1650 C 2.4 × 10−12 5.2 39.2 13 32% Cysteine synthase, CysK Bpr_I1089 C 1.9 × 10−13 5.0 32.3 18 72% Diaminopimelate dehydrogenase Bpr_I0298 C 9.6 × 10−16 5.6 35.8 16 49% Dihydrodipicolinate reductase, DapB Bpr_I2453 C 2.7 × 10−6 4.9 27.0 9 46% Glu/Leu/Phe/Val dehydrogenase Bpr_I2129 C 1.2 × 10−30 5.4 48.6 31 64% Imidazole glycerol phosphate synthase Bpr_I1240 C 8.0 × 10−3 4.7 22.5 8 44% glutamine amidotransferase subunit Ketol-acid reductoisomerase, IlvC Bpr_I1657 C 3.8 × 10−16
  • DNA Methylation Seeing the Forest for the Trees: a Wide Perspective on RNA-Directed

    DNA Methylation Seeing the Forest for the Trees: a Wide Perspective on RNA-Directed

    Downloaded from genesdev.cshlp.org on August 16, 2012 - Published by Cold Spring Harbor Laboratory Press Seeing the forest for the trees: a wide perspective on RNA-directed DNA methylation Huiming Zhang and Jian-Kang Zhu Genes Dev. 2012 26: 1769-1773 Access the most recent version at doi:10.1101/gad.200410.112 References This article cites 31 articles, 9 of which can be accessed free at: http://genesdev.cshlp.org/content/26/16/1769.full.html#ref-list-1 Related Content Spatial and functional relationships among Pol V-associated loci, Pol IV-dependent siRNAs, and cytosine methylation in the Arabidopsis epigenome Andrzej T. Wierzbicki, Ross Cocklin, Anoop Mayampurath, et al. Genes Dev. August 15, 2012 26: 1825-1836 Email alerting Receive free email alerts when new articles cite this article - sign up in the box at the service top right corner of the article or click here Topic Articles on similar topics can be found in the following collections Collections Chromatin and Gene Expression (146 articles) Plant Biology (29 articles) To subscribe to Genes & Development go to: http://genesdev.cshlp.org/subscriptions Copyright © 2012 by Cold Spring Harbor Laboratory Press Downloaded from genesdev.cshlp.org on August 16, 2012 - Published by Cold Spring Harbor Laboratory Press PERSPECTIVE Seeing the forest for the trees: a wide perspective on RNA-directed DNA methylation Huiming Zhang1 and Jian-Kang Zhu1,2,3 1Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, Indiana 47907, USA; 2Shanghai Center for Plant Stress Biology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China In this issue of Genes & Development, Wierzbicki and remaining subunits of Pol IV and/or Pol V are different but colleagues (pp.
  • Yeast Genome Gazetteer P35-65

    Yeast Genome Gazetteer P35-65

    gazetteer Metabolism 35 tRNA modification mitochondrial transport amino-acid metabolism other tRNA-transcription activities vesicular transport (Golgi network, etc.) nitrogen and sulphur metabolism mRNA synthesis peroxisomal transport nucleotide metabolism mRNA processing (splicing) vacuolar transport phosphate metabolism mRNA processing (5’-end, 3’-end processing extracellular transport carbohydrate metabolism and mRNA degradation) cellular import lipid, fatty-acid and sterol metabolism other mRNA-transcription activities other intracellular-transport activities biosynthesis of vitamins, cofactors and RNA transport prosthetic groups other transcription activities Cellular organization and biogenesis 54 ionic homeostasis organization and biogenesis of cell wall and Protein synthesis 48 plasma membrane Energy 40 ribosomal proteins organization and biogenesis of glycolysis translation (initiation,elongation and cytoskeleton gluconeogenesis termination) organization and biogenesis of endoplasmic pentose-phosphate pathway translational control reticulum and Golgi tricarboxylic-acid pathway tRNA synthetases organization and biogenesis of chromosome respiration other protein-synthesis activities structure fermentation mitochondrial organization and biogenesis metabolism of energy reserves (glycogen Protein destination 49 peroxisomal organization and biogenesis and trehalose) protein folding and stabilization endosomal organization and biogenesis other energy-generation activities protein targeting, sorting and translocation vacuolar and lysosomal
  • Fig.S1 Real-Time PCR Analysis of Select Genes from Different Metabolic Pathways S. Cerevisiae Transformants Expressing TEF-YCT1

    Fig.S1 Real-Time PCR Analysis of Select Genes from Different Metabolic Pathways S. Cerevisiae Transformants Expressing TEF-YCT1

    Fig.S1 Real-time PCR analysis of select genes from different metabolic pathways S. cerevisiae transformants expressing TEF-YCT1 were either exposed to 500µM cysteine or no cysteine for 5 hours (Control samples). Total RNA was extracted followed by cDNA synthesis. RT-PCR analysis was done using SYBR green dye-based method. The experiment was carried out twice and the figure represents expression levels of the above mentioned genes in treated samples relative to the corresponding untreated control samples. The data above are means ± SD from technical replicates (n=3) of the biological duplicates. [ARG8, ARG5,6 and RTC2 belong to the arginine biosynthetic pathway, SSU1 is a part of sulphur metabolism and TIS11 is a gene involved in maintaining iron homeostasis in the cells.] Table S1: List of strains used in this study Strain Genotype Source ABC 1738 (yct1) MATα his31 leu20 lys20 ura30 YLL055W:: kanMX4 Euroscarf (Y01543) ABC3612(arg3) MATa his31 leu20 ura30 YJL088w::kanMX4 Euroscarf (Y11335) ABC4812(arg5,6) MATa his31 leu20 ura30 YER069w::kanMX4 Euroscarf (Y10209) ABC4811(arg8) MATa his31 leu20 ura30 YOL140w::kanMX4 Euroscarf (Y17711) ABC4814(spe1) MATa his31 leu20 ura30 YKL184w::kanMX4 Euroscarf (Y15034) ABC4815(spe2) MATa his31 leu20 ura30 YOL052c::kanMX4 Euroscarf (Y11743) ABC4816(spe3) MATa his31 leu20 ura30 YPR069c::kanMX4 Euroscarf (Y15488) ABC4817(spe4) MATa his31 leu20 ura30 YLR146c::kanMX4 Euroscarf (Y16945) ABC3604(ssu1) MATa his31 leu20 ura30 YPL092w::kanMX4 Euroscarf (Y12160) ABC1486(str2) MATa his31 leu20 ura30 YJR130c::kanMX4
  • The Pennsylvania State University

    The Pennsylvania State University

    The Pennsylvania State University The Graduate School Graduate Program in Plant Biology IDENTIFICATION OF SMALL RNA PRODUCING GENES IN THE MOSS PHYSCOMITRELLA PATENS A Dissertation in Plant Biology by Ceyda Coruh 2014 Ceyda Coruh Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2014 The dissertation of Ceyda Coruh was reviewed and approved* by the following: Michael J. Axtell Associate Professor of Biology Dissertation Advisor Chair of Committee Claude dePamphilis Professor of Biology Sarah M. Assmann Waller Professor of Biology Anton Nekrutenko Associate Professor of Biochemistry and Molecular Biology Teh-hui Kao Distinguished Professor of Biochemistry and Molecular Biology Chair, Intercollege Graduate Degree Program in Plant Biology *Signatures are on file in the Graduate School iii ABSTRACT In plants, a significant fraction of the genome is responsible for making regulatory small RNAs. These ubiquitous, endogenous small RNAs are currently categorized into two groups: microRNAs (miRNAs) and small interfering RNAs (siRNAs). They are produced by Dicer-Like (DCL) proteins and utilized by Argonaute (AGO) proteins to guide repressive regulation of target mRNAs and/or chromatin selected on the basis of small RNA-target complementarity at the transcriptional or post-transcriptional levels. 21 nt miRNAs and 24 nt heterochromatic siRNAs are the two major types of small RNAs found in angiosperms (flowering plants). The small RNA populations in angiosperms are dominated by 24 nt heterochromatic siRNAs which derive from intergenic, repetitive regions and mediate DNA methylation and repressive histone modifications to targeted loci in angiosperms. However, the existence and extent of heterochromatic siRNAs in other land plant lineages has been less clear.
  • Letters to Nature

    Letters to Nature

    letters to nature Received 7 July; accepted 21 September 1998. 26. Tronrud, D. E. Conjugate-direction minimization: an improved method for the re®nement of macromolecules. Acta Crystallogr. A 48, 912±916 (1992). 1. Dalbey, R. E., Lively, M. O., Bron, S. & van Dijl, J. M. The chemistry and enzymology of the type 1 27. Wolfe, P. B., Wickner, W. & Goodman, J. M. Sequence of the leader peptidase gene of Escherichia coli signal peptidases. Protein Sci. 6, 1129±1138 (1997). and the orientation of leader peptidase in the bacterial envelope. J. Biol. Chem. 258, 12073±12080 2. Kuo, D. W. et al. Escherichia coli leader peptidase: production of an active form lacking a requirement (1983). for detergent and development of peptide substrates. Arch. Biochem. Biophys. 303, 274±280 (1993). 28. Kraulis, P.G. Molscript: a program to produce both detailed and schematic plots of protein structures. 3. Tschantz, W. R. et al. Characterization of a soluble, catalytically active form of Escherichia coli leader J. Appl. Crystallogr. 24, 946±950 (1991). peptidase: requirement of detergent or phospholipid for optimal activity. Biochemistry 34, 3935±3941 29. Nicholls, A., Sharp, K. A. & Honig, B. Protein folding and association: insights from the interfacial and (1995). the thermodynamic properties of hydrocarbons. Proteins Struct. Funct. Genet. 11, 281±296 (1991). 4. Allsop, A. E. et al.inAnti-Infectives, Recent Advances in Chemistry and Structure-Activity Relationships 30. Meritt, E. A. & Bacon, D. J. Raster3D: photorealistic molecular graphics. Methods Enzymol. 277, 505± (eds Bently, P. H. & O'Hanlon, P. J.) 61±72 (R. Soc. Chem., Cambridge, 1997).
  • Materials and Methods

    Materials and Methods

    Effects of Hybridization on Heterochromatic Small Interfering RNA and Gene Expression in Zea mays by Travis Korry Coleman A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Doctor of Philosophy in Plant Agriculture Guelph, Ontario, Canada © Travis Coleman, January, 2017 ABSTRACT EFFECTS OF HYBRIDIZATION ON HETEROCHROMATIC SMALL INTERFERING RNA AND GENE EXPRESSION IN ZEA MAYS Travis Korry Coleman Advisor: University of Guelph, 2016 Associate Professor Lewis N. Lukens Despite decades of research, the molecular nature of heterosis is not completely understood. Heterosis for quantitative traits is controlled by the cumulative effects of multiple genes and regulatory elements, each of which may exhibit differing modes of action. While dominance and over-dominance theories can account for some of the genetic control of heterosis, neither provides a complete account. There is a growing body of evidence that implicates small RNAs as non-coding elements which may play a role in the manifestation of heterosis upon hybridization. In particular, 24-nt heterochromatic small interfering RNAs (hetsiRNAs) may play a role in mediating trans-genomic interactions via DNA methylation when two genomes come into contact in an F1 nucleus. Recent research in Arabidopsis thaliana has demonstrated that hetsiRNAs show non-additive expression upon hybridization, with the majority of hetsiRNAs being downregulated. This research seeks to examine such trends in non-additive hetsiRNA expression in commercial maize germplasm. Small RNA were isolated and deep sequenced from leaf tissue samples of two inbred lines and their F1 hybrid. In order to examine the effects of reducing hetsiRNAs in hybridization, each genotype was also sampled in each of two mediator of paramutation 1 (mop1) allelic states.
  • The Microbiota-Produced N-Formyl Peptide Fmlf Promotes Obesity-Induced Glucose

    The Microbiota-Produced N-Formyl Peptide Fmlf Promotes Obesity-Induced Glucose

    Page 1 of 230 Diabetes Title: The microbiota-produced N-formyl peptide fMLF promotes obesity-induced glucose intolerance Joshua Wollam1, Matthew Riopel1, Yong-Jiang Xu1,2, Andrew M. F. Johnson1, Jachelle M. Ofrecio1, Wei Ying1, Dalila El Ouarrat1, Luisa S. Chan3, Andrew W. Han3, Nadir A. Mahmood3, Caitlin N. Ryan3, Yun Sok Lee1, Jeramie D. Watrous1,2, Mahendra D. Chordia4, Dongfeng Pan4, Mohit Jain1,2, Jerrold M. Olefsky1 * Affiliations: 1 Division of Endocrinology & Metabolism, Department of Medicine, University of California, San Diego, La Jolla, California, USA. 2 Department of Pharmacology, University of California, San Diego, La Jolla, California, USA. 3 Second Genome, Inc., South San Francisco, California, USA. 4 Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA. * Correspondence to: 858-534-2230, [email protected] Word Count: 4749 Figures: 6 Supplemental Figures: 11 Supplemental Tables: 5 1 Diabetes Publish Ahead of Print, published online April 22, 2019 Diabetes Page 2 of 230 ABSTRACT The composition of the gastrointestinal (GI) microbiota and associated metabolites changes dramatically with diet and the development of obesity. Although many correlations have been described, specific mechanistic links between these changes and glucose homeostasis remain to be defined. Here we show that blood and intestinal levels of the microbiota-produced N-formyl peptide, formyl-methionyl-leucyl-phenylalanine (fMLF), are elevated in high fat diet (HFD)- induced obese mice. Genetic or pharmacological inhibition of the N-formyl peptide receptor Fpr1 leads to increased insulin levels and improved glucose tolerance, dependent upon glucagon- like peptide-1 (GLP-1). Obese Fpr1-knockout (Fpr1-KO) mice also display an altered microbiome, exemplifying the dynamic relationship between host metabolism and microbiota.
  • Saccharomyces Cerevisiae Aspartate Kinase Mechanism and Inhibition

    Saccharomyces Cerevisiae Aspartate Kinase Mechanism and Inhibition

    In compliance with the Canadian Privacy Legislation some supporting forms may have been removed from this dissertation. While these forms may be included in the document page count, their removal does not represent any loss of content from the dissertation. Ph.D. Thesis - D. Bareich McMaster University - Department of Biochemistry FUNGAL ASPARTATE KINASE MECHANISM AND INHIBITION By DAVID C. BAREICH, B.Sc. A Thesis Submitted to the School of Graduate Studies in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy McMaster University © Copyright by David C. Bareich, June 2003 1 Ph.D. Thesis - D. Bareich McMaster University - Department of Biochemistry FUNGAL ASPARTATE KINASE MECHANISM AND INHIBITION Ph.D. Thesis - D. Bareich McMaster University - Department of Biochemistry DOCTOR OF PHILOSOPHY (2003) McMaster University (Biochemistry) Hamilton, Ontario TITLE: Saccharomyces cerevisiae aspartate kinase mechanism and inhibition AUTHOR: David Christopher Bareich B.Sc. (University of Waterloo) SUPERVISOR: Professor Gerard D. Wright NUMBER OF PAGES: xix, 181 11 Ph.D. Thesis - D. Bareich McMaster University - Department of Biochemistry ABSTRACT Aspartate kinase (AK) from Saccharomyces cerevisiae (AKsc) catalyzes the first step in the aspartate pathway responsible for biosynthesis of L-threonine, L-isoleucine, and L-methionine in fungi. Little was known about amino acids important for AKsc substrate binding and catalysis. Hypotheses about important amino acids were tested using site directed mutagenesis to substitute these amino acids with others having different properties. Steady state kinetic parameters and pH titrations of the variant enzymes showed AKsc-K18 and H292 to be important for binding and catalysis. Little was known about how the S. cerevisiae aspartate pathway kinases, AKsc and homoserine kinase (HSKsc), catalyze the transfer of the y-phosphate from adenosine triphosphate (ATP) to L-aspartate or L-homoserine, respectively.