Identification and Characterization of Two New 5-Keto-4-Deoxy-D-Glucarate Dehydratases/Decarboxylases
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Comparison of Topological Clustering Within Protein Networks Using Edge
Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity Janelle B. Leuthaeuser,1 Stacy T. Knutson,2 Kiran Kumar,2 Patricia C. Babbitt,3,4 and Jacquelyn S. Fetrow1,2* 1Department of Molecular Genetics and Genomics, Wake Forest University, Winston-Salem, North Carolina 27106 2Departments of Computer Science and Physics, Wake Forest University, Winston-Salem, North Carolina 27106 3Department of Bioengineering and Therapeutic Sciences, Institute for Quantitative Biosciences University of California San Francisco, San Francisco, California 94158 4Department of Pharmaceutical Chemistry, Institute for Quantitative Biosciences University of California San Francisco, San Francisco, California 94158 Received 10 April 2015; Accepted 10 June 2015 DOI: 10.1002/pro.2724 Published online 12 June 2015 proteinscience.org Abstract: The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annota- tion via annotation transfer, group proteins into similarity-based clusters. An underlying assump- tion is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/ function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. -
Functional Assignments in the Enolase Superfamily: Investigations of Two Divergent Groups of D-Galacturonate Dehydratases and Galactarate Dehydratase-Iii
FUNCTIONAL ASSIGNMENTS IN THE ENOLASE SUPERFAMILY: INVESTIGATIONS OF TWO DIVERGENT GROUPS OF D-GALACTURONATE DEHYDRATASES AND GALACTARATE DEHYDRATASE-III BY FIONA PATRICIA GRONINGER-POE i DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biochemistry in the Graduate College of the University of Illinois at Urbana-Champaign, 2014 Urbana, Illinois Doctoral Committee: Professor John A. Gerlt, Chair Professor John E. Cronan, Jr. Associate Professor Rutilo Fratti Associate Professor Raven H. Huang ABSTRACT More than a decade after the genomic age, full genome sequencing is cost-effective and fast, allowing for the deposit of an ever increasing number of DNA sequences. New fields have arisen from this availability of genomic information, and the way we think about biochemistry and enzymology has been transformed. Unfortunately, there is no robust method for accurately determining the functions of enzymes encoded by these sequences that matches the speed in which genomes are deposited into public databases. Functional assignment of enzymes remains of utmost importance in understanding microbial metabolism and has applications in agriculture by examining bacterial plant pathogen metabolism and additionally in human health by providing metabolic context to the human gut microbiome. To aid in the functional identification of proteins, enzymes can be grouped into superfamilies which share common structural motifs as well as mechanistic features. To this end, the enolase superfamily is an excellent model system for functional assignment because more than half of the members still lack functional identification. Structurally, these enzymes contain substrate specificity residues in the N-terminal capping domain and catalytic residues in the C-terminal barrel domain. -
Supporting Information High-Throughput Virtual Screening
Supporting Information High-Throughput Virtual Screening of Proteins using GRID Molecular Interaction Fields Simone Sciabola, Robert V. Stanton, James E. Mills, Maria M. Flocco, Massimo Baroni, Gabriele Cruciani, Francesca Perruccio and Jonathan S. Mason Contents Table S1 S2-S21 Figure S1 S22 * To whom correspondence should be addressed: Simone Sciabola, Pfizer Research Technology Center, Cambridge, 02139 MA, USA Phone: +1-617-551-3327; Fax: +1-617-551-3117; E-mail: [email protected] S1 Table S1. Description of the 990 proteins used as decoy for the Protein Virtual Screening analysis. PDB ID Protein family Molecule Res. (Å) 1n24 ISOMERASE (+)-BORNYL DIPHOSPHATE SYNTHASE 2.3 1g4h HYDROLASE 1,3,4,6-TETRACHLORO-1,4-CYCLOHEXADIENE HYDROLASE 1.8 1cel HYDROLASE(O-GLYCOSYL) 1,4-BETA-D-GLUCAN CELLOBIOHYDROLASE I 1.8 1vyf TRANSPORT PROTEIN 14 KDA FATTY ACID BINDING PROTEIN 1.85 1o9f PROTEIN-BINDING 14-3-3-LIKE PROTEIN C 2.7 1t1s OXIDOREDUCTASE 1-DEOXY-D-XYLULOSE 5-PHOSPHATE REDUCTOISOMERASE 2.4 1t1r OXIDOREDUCTASE 1-DEOXY-D-XYLULOSE 5-PHOSPHATE REDUCTOISOMERASE 2.3 1q0q OXIDOREDUCTASE 1-DEOXY-D-XYLULOSE 5-PHOSPHATE REDUCTOISOMERASE 1.9 1jcy LYASE 2-DEHYDRO-3-DEOXYPHOSPHOOCTONATE ALDOLASE 1.9 1fww LYASE 2-DEHYDRO-3-DEOXYPHOSPHOOCTONATE ALDOLASE 1.85 1uk7 HYDROLASE 2-HYDROXY-6-OXO-7-METHYLOCTA-2,4-DIENOATE 1.7 1v11 OXIDOREDUCTASE 2-OXOISOVALERATE DEHYDROGENASE ALPHA SUBUNIT 1.95 1x7w OXIDOREDUCTASE 2-OXOISOVALERATE DEHYDROGENASE ALPHA SUBUNIT 1.73 1d0l TRANSFERASE 35KD SOLUBLE LYTIC TRANSGLYCOSYLASE 1.97 2bt4 LYASE 3-DEHYDROQUINATE DEHYDRATASE -
Supplementary Information
Supplementary information (a) (b) Figure S1. Resistant (a) and sensitive (b) gene scores plotted against subsystems involved in cell regulation. The small circles represent the individual hits and the large circles represent the mean of each subsystem. Each individual score signifies the mean of 12 trials – three biological and four technical. The p-value was calculated as a two-tailed t-test and significance was determined using the Benjamini-Hochberg procedure; false discovery rate was selected to be 0.1. Plots constructed using Pathway Tools, Omics Dashboard. Figure S2. Connectivity map displaying the predicted functional associations between the silver-resistant gene hits; disconnected gene hits not shown. The thicknesses of the lines indicate the degree of confidence prediction for the given interaction, based on fusion, co-occurrence, experimental and co-expression data. Figure produced using STRING (version 10.5) and a medium confidence score (approximate probability) of 0.4. Figure S3. Connectivity map displaying the predicted functional associations between the silver-sensitive gene hits; disconnected gene hits not shown. The thicknesses of the lines indicate the degree of confidence prediction for the given interaction, based on fusion, co-occurrence, experimental and co-expression data. Figure produced using STRING (version 10.5) and a medium confidence score (approximate probability) of 0.4. Figure S4. Metabolic overview of the pathways in Escherichia coli. The pathways involved in silver-resistance are coloured according to respective normalized score. Each individual score represents the mean of 12 trials – three biological and four technical. Amino acid – upward pointing triangle, carbohydrate – square, proteins – diamond, purines – vertical ellipse, cofactor – downward pointing triangle, tRNA – tee, and other – circle. -
Supplementary Informations SI2. Supplementary Table 1
Supplementary Informations SI2. Supplementary Table 1. M9, soil, and rhizosphere media composition. LB in Compound Name Exchange Reaction LB in soil LBin M9 rhizosphere H2O EX_cpd00001_e0 -15 -15 -10 O2 EX_cpd00007_e0 -15 -15 -10 Phosphate EX_cpd00009_e0 -15 -15 -10 CO2 EX_cpd00011_e0 -15 -15 0 Ammonia EX_cpd00013_e0 -7.5 -7.5 -10 L-glutamate EX_cpd00023_e0 0 -0.0283302 0 D-glucose EX_cpd00027_e0 -0.61972444 -0.04098397 0 Mn2 EX_cpd00030_e0 -15 -15 -10 Glycine EX_cpd00033_e0 -0.0068175 -0.00693094 0 Zn2 EX_cpd00034_e0 -15 -15 -10 L-alanine EX_cpd00035_e0 -0.02780553 -0.00823049 0 Succinate EX_cpd00036_e0 -0.0056245 -0.12240603 0 L-lysine EX_cpd00039_e0 0 -10 0 L-aspartate EX_cpd00041_e0 0 -0.03205557 0 Sulfate EX_cpd00048_e0 -15 -15 -10 L-arginine EX_cpd00051_e0 -0.0068175 -0.00948672 0 L-serine EX_cpd00054_e0 0 -0.01004986 0 Cu2+ EX_cpd00058_e0 -15 -15 -10 Ca2+ EX_cpd00063_e0 -15 -100 -10 L-ornithine EX_cpd00064_e0 -0.0068175 -0.00831712 0 H+ EX_cpd00067_e0 -15 -15 -10 L-tyrosine EX_cpd00069_e0 -0.0068175 -0.00233919 0 Sucrose EX_cpd00076_e0 0 -0.02049199 0 L-cysteine EX_cpd00084_e0 -0.0068175 0 0 Cl- EX_cpd00099_e0 -15 -15 -10 Glycerol EX_cpd00100_e0 0 0 -10 Biotin EX_cpd00104_e0 -15 -15 0 D-ribose EX_cpd00105_e0 -0.01862144 0 0 L-leucine EX_cpd00107_e0 -0.03596182 -0.00303228 0 D-galactose EX_cpd00108_e0 -0.25290619 -0.18317325 0 L-histidine EX_cpd00119_e0 -0.0068175 -0.00506825 0 L-proline EX_cpd00129_e0 -0.01102953 0 0 L-malate EX_cpd00130_e0 -0.03649016 -0.79413596 0 D-mannose EX_cpd00138_e0 -0.2540567 -0.05436649 0 Co2 EX_cpd00149_e0 -
Supplemental Table 7. Every Significant Association
Supplemental Table 7. Every significant association between an individual covariate and functional group (assigned to the KO level) as determined by CPGLM regression analysis. Variable Unit RelationshipLabel See also CBCL Aggressive Behavior K05914 + CBCL Emotionally Reactive K05914 + CBCL Externalizing Behavior K05914 + K15665 K15658 CBCL Total K05914 + K15660 K16130 KO: E1.13.12.7; photinus-luciferin 4-monooxygenase (ATP-hydrolysing) [EC:1.13.12.7] :: PFAMS: AMP-binding enzyme; CBQ Inhibitory Control K05914 - K12239 K16120 Condensation domain; Methyltransferase domain; Thioesterase domain; AMP-binding enzyme C-terminal domain LEC Family Separation/Social Services K05914 + K16129 K16416 LEC Poverty Related Events K05914 + K16124 LEC Total K05914 + LEC Turmoil K05914 + CBCL Aggressive Behavior K15665 + CBCL Anxious Depressed K15665 + CBCL Emotionally Reactive K15665 + K05914 K15658 CBCL Externalizing Behavior K15665 + K15660 K16130 KO: K15665, ppsB, fenD; fengycin family lipopeptide synthetase B :: PFAMS: Condensation domain; AMP-binding enzyme; CBCL Total K15665 + K12239 K16120 Phosphopantetheine attachment site; AMP-binding enzyme C-terminal domain; Transferase family CBQ Inhibitory Control K15665 - K16129 K16416 LEC Poverty Related Events K15665 + K16124 LEC Total K15665 + LEC Turmoil K15665 + CBCL Aggressive Behavior K11903 + CBCL Anxiety Problems K11903 + CBCL Anxious Depressed K11903 + CBCL Depressive Problems K11903 + LEC Turmoil K11903 + MODS: Type VI secretion system K01220 K01058 CBCL Anxiety Problems K11906 + CBCL Depressive -
Functional and Physiological Discovery in the Mannonate Dehydratase Subgroup of the Enolase Superfamily
FUNCTIONAL AND PHYSIOLOGICAL DISCOVERY IN THE MANNONATE DEHYDRATASE SUBGROUP OF THE ENOLASE SUPERFAMILY BY DANIEL JOSEPH WICHELECKI DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biochemistry in the Graduate College of the University of Illinois at Urbana-Champaign, 2014 Urbana, Illinois Doctoral Committee: Professor John Gerlt, Chair Professor John Cronan Professor Scott Silverman Professor Wilfred van der Donk ABSTRACT In the current post-genomic world, the exponential amassing of protein sequences is overwhelming the scientific community’s ability to experimentally assign each protein’s function. The use of automated, homology-based annotations has allowed a reprieve from this efflux of data, but has led to widespread misannotation and nonannotation in protein sequence databases. This dissertation details the functional and physiological characterization of the mannonate dehydratase subgroup (ManD) of the enolase superfamily (ENS). The outcome affirms the dangers of homology-based annotations while discovering novel metabolic pathways. Furthermore, the experimental verification of these pathways ( in vitro and in vivo ) has provided a platform to test the general strategies for improved functional and metabolic characterization being developed by the Enzyme Function Initiative (EFI). Prior to this study, one member of the ManD subgroup had been characterized and was shown to dehydrate D-mannonate to 2-keto-3-deoxy-D-gluconate. Forty-two additional members of the ManD, selected from across the sequence space of the subgroup, were screened for activity and kinetic constants were determined. The members of the once isofunctional subgroup were found to differ in both catalytic efficiency and substrate specificity: 1) high 3 4 -1 -1 efficiency (k cat /K M = 10 to 10 M s ) dehydration of D-mannonate, 2) low efficiency (k cat /K M = 10 1 to 10 2 M-1s-1) dehydration of D-mannonate and/or D-gluconate, and 3) no-activity with either D-mannonate or D-gluconate (or any other acid sugar tested). -
Supplementary Table S1. the Key Enzymes for Autotrophic Growth in C
Electronic Supplementary Material (ESI) for Metallomics. This journal is © The Royal Society of Chemistry 2015 Supplementary Table S1. The key enzymes for autotrophic growth in C. metallidurans Name Rmet-number Spec. Predicted Mass, (kDa) Determined Mass, (kDa) Activity HOS Rmet_1522 to Rmet_1525 28.7 65.2, 25.4, 23.4, 52.8 = 166.8 62.4±1.8, 26.4±1.3, 24.0±0.6, 54.1±2.5, =235±20a HOP Rmet_1297, Rmet_1298, 67.0 69.0 + 38.6 = 108 148±24a CAX Rmet_1500, Rmet_1501 2.82 8 * (13.6 + 52.5) = 529 475±36a PRK Rmet_1512 0.994 8 * 32.4 = 259 256±11a aMean value of masses determined by S300 size exclusion chromatography and sucrose gradient centrifugation. HOS, NAD-reducing soluble hydrogenase; HOP, membrane-bound hydrogenase; CAX, ribulose-bis-phosphate carboxylase/oxygenase; PRK, phosphoribulokinase. Specific activity in U/mg protein. Supplementary Table S2. Genes expressed differently in AE104 compared to CH34 wild typea Operon Region Name Gene Q D Description UP Op1321r Rmet_4594 zntA 1.72 2.88 Q1LEH0 Heavy metal translocating P -type ATPase Op1322f Rmet_4595 czcI2 2.03 2.92 Q1LEG9 Putative uncharacterized protein Op1322f Rmet_4596 czcC2 23.34 5.36 Q1LEG8 Outer membrane efflux protein Op1322f Rmet_4597 czcB2' 15.36 9.68 Q1LEG7 Secretion protein HlyD Op0075f Rmet_0260 - 2.31 2.48 Q1LRT0 Putative transmembrane protein Op0075f Rmet_0261 coxB 2.08 2.49 Q1LRS9 Cytochrome c oxidase subunit 2 Adjacent to CMGI-7 Op0335f Rmet_1171 tnpA 7.03 21.74 Q9F8S6 Transposase (Transposase, IS4 family) CMGI-2 Op0362r Rmet_1251 tnp 4.08 0.61 Q1LNY9 Putative uncharacterized -
Novel Non-Phosphorylative Pathway of Pentose Metabolism from Bacteria
www.nature.com/scientificreports OPEN Novel non-phosphorylative pathway of pentose metabolism from bacteria Received: 22 March 2018 Seiya Watanabe1,2,3, Fumiyasu Fukumori4, Hisashi Nishiwaki1,2, Yasuhiro Sakurai5, Accepted: 30 September 2018 Kunihiko Tajima5 & Yasuo Watanabe1,2 Published: xx xx xxxx Pentoses, including D-xylose, L-arabinose, and D-arabinose, are generally phosphorylated to D-xylulose 5-phosphate in bacteria and fungi. However, in non-phosphorylative pathways analogous to the Entner-Dodorof pathway in bacteria and archaea, such pentoses can be converted to pyruvate and glycolaldehyde (Route I) or α-ketoglutarate (Route II) via a 2-keto-3-deoxypentonate (KDP) intermediate. Putative gene clusters related to these metabolic pathways were identifed on the genome of Herbaspirillum huttiense IAM 15032 using a bioinformatic analysis. The biochemical characterization of C785_RS13685, one of the components encoded to D-arabinonate dehydratase, difered from the known acid-sugar dehydratases. The biochemical characterization of the remaining components and a genetic expression analysis revealed that D- and L-KDP were converted not only to α-ketoglutarate, but also pyruvate and glycolate through the participation of dehydrogenase and hydrolase (Route III). Further analyses revealed that the Route II pathway of D-arabinose metabolism was not evolutionally related to the analogous pathway from archaea. Te breakdown of D-glucose is central for energy and biosynthetic metabolism throughout all domains of life. Te most common glycolytic routes in bacteria are the Embden-Meyerhof-Parnas, the Entner-Doudorof (ED), and the oxidative pentose phosphate pathways. Te distinguishing diference between the two former glyco- lytic pathways lies in the nature of the 6-carbon metabolic intermediates that serve as substrates for aldol cleav- age. -