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Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks
Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables larg...
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creator | Zhao, Suwen Sakai, Ayano Zhang, Xinshuai Vetting, Matthew W Kumar, Ritesh Hillerich, Brandan San Francisco, Brian Solbiati, Jose Steves, Adam Brown, Shoshana Akiva, Eyal Barber, Alan Seidel, Ronald D Babbitt, Patricia C Almo, Steven C Gerlt, John A Jacobson, Matthew P |
description | Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins in 12 families in the PRS that represent ∼85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes. |
doi_str_mv | 10.7554/elife.03275 |
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We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins in 12 families in the PRS that represent ∼85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.</description><identifier>ISSN: 2050-084X</identifier><identifier>EISSN: 2050-084X</identifier><identifier>DOI: 10.7554/elife.03275</identifier><identifier>PMID: 24980702</identifier><language>eng</language><publisher>England: eLife Sciences Publications Ltd</publisher><subject>Algorithms ; Amino Acid Isomerases - chemistry ; Biochemistry ; Computational Biology - methods ; Crystallography, X-Ray ; Dehydrogenases ; Enzymatic activity ; Enzymes ; functional assignment ; Gene clusters ; Gene expression ; genome neighborhood network ; Genome, Bacterial ; Genomes ; Magnetic Resonance Spectroscopy ; Mass Spectrometry ; Metabolic Networks and Pathways ; Metabolic pathways ; Metabolism ; Molecular Conformation ; Molecular Sequence Data ; Multigene Family ; Neighborhoods ; Nucleotide sequence ; Operons ; Plasmids - metabolism ; Proline racemase ; Protein families ; Proteins ; RNA - chemistry ; sequence similarity network ; Spectrometry, Mass, Electrospray Ionization ; Transcription, Genetic</subject><ispartof>eLife, 2014-06, Vol.3</ispartof><rights>Copyright © 2014, Zhao et al. This work is licensed under the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/3.0/ ) (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Copyright © 2014, Zhao et al 2014 Zhao et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c540t-e48818bdfced59bd2c1bbbc8e6696cd55ef54a6c990e2d0c9615add03b309d413</citedby><cites>FETCH-LOGICAL-c540t-e48818bdfced59bd2c1bbbc8e6696cd55ef54a6c990e2d0c9615add03b309d413</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1966565676/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1966565676?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24980702$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/biblio/1197675$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhao, Suwen</creatorcontrib><creatorcontrib>Sakai, Ayano</creatorcontrib><creatorcontrib>Zhang, Xinshuai</creatorcontrib><creatorcontrib>Vetting, Matthew W</creatorcontrib><creatorcontrib>Kumar, Ritesh</creatorcontrib><creatorcontrib>Hillerich, Brandan</creatorcontrib><creatorcontrib>San Francisco, Brian</creatorcontrib><creatorcontrib>Solbiati, Jose</creatorcontrib><creatorcontrib>Steves, Adam</creatorcontrib><creatorcontrib>Brown, Shoshana</creatorcontrib><creatorcontrib>Akiva, Eyal</creatorcontrib><creatorcontrib>Barber, Alan</creatorcontrib><creatorcontrib>Seidel, Ronald D</creatorcontrib><creatorcontrib>Babbitt, Patricia C</creatorcontrib><creatorcontrib>Almo, Steven C</creatorcontrib><creatorcontrib>Gerlt, John A</creatorcontrib><creatorcontrib>Jacobson, Matthew P</creatorcontrib><title>Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks</title><title>eLife</title><addtitle>Elife</addtitle><description>Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins in 12 families in the PRS that represent ∼85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.</description><subject>Algorithms</subject><subject>Amino Acid Isomerases - chemistry</subject><subject>Biochemistry</subject><subject>Computational Biology - methods</subject><subject>Crystallography, X-Ray</subject><subject>Dehydrogenases</subject><subject>Enzymatic activity</subject><subject>Enzymes</subject><subject>functional assignment</subject><subject>Gene clusters</subject><subject>Gene expression</subject><subject>genome neighborhood network</subject><subject>Genome, Bacterial</subject><subject>Genomes</subject><subject>Magnetic Resonance Spectroscopy</subject><subject>Mass Spectrometry</subject><subject>Metabolic Networks and Pathways</subject><subject>Metabolic pathways</subject><subject>Metabolism</subject><subject>Molecular Conformation</subject><subject>Molecular Sequence Data</subject><subject>Multigene Family</subject><subject>Neighborhoods</subject><subject>Nucleotide sequence</subject><subject>Operons</subject><subject>Plasmids - metabolism</subject><subject>Proline racemase</subject><subject>Protein families</subject><subject>Proteins</subject><subject>RNA - chemistry</subject><subject>sequence similarity network</subject><subject>Spectrometry, Mass, Electrospray Ionization</subject><subject>Transcription, Genetic</subject><issn>2050-084X</issn><issn>2050-084X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkstv1DAQhyMEolXpiTuK4IKEttiJH_EFqap4VFoJDiBxs_yYJF4Su9jOou1fj3e3VC32wZ6Zn755aKrqJUYXnFLyHibXwwVqG06fVKcNomiFOvLz6YP_SXWe0gaVw0nXYfG8OmmI6BBHzWm1_RbBOpNd8LXytjajispkiO5WHZyhr8Hf7uZimbpE3NZlB6keFmfB1npXJ_i9gDdQJze7SUWXdwfUAD7MUHtww6hDHEOwxch_QvyVXlTPejUlOL97z6ofnz5-v_qyWn_9fH11uV4ZSlBewb7gTtvegKVC28ZgrbXpgDHBjKUUekoUM0IgaCwygmGqrEWtbpGwBLdn1fWRa4PayJvoZhV3MignD44QB6li6WwCKTrec864BsYJUbgjrLWN4r1tFRfQFdaHI-tm0TNYAz5HNT2CPo54N8ohbCXBuBWCFcDrIyCk7GQyLoMZTfAeTJYYi5KcFtHbuywxlLmmLGeXDEyT8hCWJDFlrPA6Kor0zX_STViiL_OUWDBGy-X7rO-OKhNDShH6-4oxkvsdkrAuOyQPO1TUrx42ea_9tzHtXyWBxbg</recordid><startdate>20140630</startdate><enddate>20140630</enddate><creator>Zhao, Suwen</creator><creator>Sakai, Ayano</creator><creator>Zhang, Xinshuai</creator><creator>Vetting, Matthew W</creator><creator>Kumar, Ritesh</creator><creator>Hillerich, Brandan</creator><creator>San Francisco, Brian</creator><creator>Solbiati, Jose</creator><creator>Steves, Adam</creator><creator>Brown, Shoshana</creator><creator>Akiva, Eyal</creator><creator>Barber, Alan</creator><creator>Seidel, Ronald D</creator><creator>Babbitt, Patricia C</creator><creator>Almo, Steven C</creator><creator>Gerlt, John A</creator><creator>Jacobson, Matthew P</creator><general>eLife Sciences Publications Ltd</general><general>eLife Sciences Publications, Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>OTOTI</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20140630</creationdate><title>Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks</title><author>Zhao, Suwen ; Sakai, Ayano ; Zhang, Xinshuai ; Vetting, Matthew W ; Kumar, Ritesh ; Hillerich, Brandan ; San Francisco, Brian ; Solbiati, Jose ; Steves, Adam ; Brown, Shoshana ; Akiva, Eyal ; Barber, Alan ; Seidel, Ronald D ; Babbitt, Patricia C ; Almo, Steven C ; Gerlt, John A ; Jacobson, Matthew P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c540t-e48818bdfced59bd2c1bbbc8e6696cd55ef54a6c990e2d0c9615add03b309d413</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Algorithms</topic><topic>Amino Acid Isomerases - chemistry</topic><topic>Biochemistry</topic><topic>Computational Biology - methods</topic><topic>Crystallography, X-Ray</topic><topic>Dehydrogenases</topic><topic>Enzymatic activity</topic><topic>Enzymes</topic><topic>functional assignment</topic><topic>Gene clusters</topic><topic>Gene expression</topic><topic>genome neighborhood network</topic><topic>Genome, Bacterial</topic><topic>Genomes</topic><topic>Magnetic Resonance Spectroscopy</topic><topic>Mass Spectrometry</topic><topic>Metabolic Networks and Pathways</topic><topic>Metabolic pathways</topic><topic>Metabolism</topic><topic>Molecular Conformation</topic><topic>Molecular Sequence Data</topic><topic>Multigene Family</topic><topic>Neighborhoods</topic><topic>Nucleotide sequence</topic><topic>Operons</topic><topic>Plasmids - metabolism</topic><topic>Proline racemase</topic><topic>Protein families</topic><topic>Proteins</topic><topic>RNA - chemistry</topic><topic>sequence similarity network</topic><topic>Spectrometry, Mass, Electrospray Ionization</topic><topic>Transcription, Genetic</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Suwen</creatorcontrib><creatorcontrib>Sakai, Ayano</creatorcontrib><creatorcontrib>Zhang, Xinshuai</creatorcontrib><creatorcontrib>Vetting, Matthew W</creatorcontrib><creatorcontrib>Kumar, Ritesh</creatorcontrib><creatorcontrib>Hillerich, Brandan</creatorcontrib><creatorcontrib>San Francisco, Brian</creatorcontrib><creatorcontrib>Solbiati, Jose</creatorcontrib><creatorcontrib>Steves, Adam</creatorcontrib><creatorcontrib>Brown, Shoshana</creatorcontrib><creatorcontrib>Akiva, Eyal</creatorcontrib><creatorcontrib>Barber, Alan</creatorcontrib><creatorcontrib>Seidel, Ronald D</creatorcontrib><creatorcontrib>Babbitt, Patricia C</creatorcontrib><creatorcontrib>Almo, Steven C</creatorcontrib><creatorcontrib>Gerlt, John A</creatorcontrib><creatorcontrib>Jacobson, Matthew P</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>ProQuest Publicly Available Content database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>OSTI.GOV</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>eLife</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Suwen</au><au>Sakai, Ayano</au><au>Zhang, Xinshuai</au><au>Vetting, Matthew W</au><au>Kumar, Ritesh</au><au>Hillerich, Brandan</au><au>San Francisco, Brian</au><au>Solbiati, Jose</au><au>Steves, Adam</au><au>Brown, Shoshana</au><au>Akiva, Eyal</au><au>Barber, Alan</au><au>Seidel, Ronald D</au><au>Babbitt, Patricia C</au><au>Almo, Steven C</au><au>Gerlt, John A</au><au>Jacobson, Matthew P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks</atitle><jtitle>eLife</jtitle><addtitle>Elife</addtitle><date>2014-06-30</date><risdate>2014</risdate><volume>3</volume><issn>2050-084X</issn><eissn>2050-084X</eissn><abstract>Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins in 12 families in the PRS that represent ∼85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.</abstract><cop>England</cop><pub>eLife Sciences Publications Ltd</pub><pmid>24980702</pmid><doi>10.7554/elife.03275</doi><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Amino Acid Isomerases - chemistry Biochemistry Computational Biology - methods Crystallography, X-Ray Dehydrogenases Enzymatic activity Enzymes functional assignment Gene clusters Gene expression genome neighborhood network Genome, Bacterial Genomes Magnetic Resonance Spectroscopy Mass Spectrometry Metabolic Networks and Pathways Metabolic pathways Metabolism Molecular Conformation Molecular Sequence Data Multigene Family Neighborhoods Nucleotide sequence Operons Plasmids - metabolism Proline racemase Protein families Proteins RNA - chemistry sequence similarity network Spectrometry, Mass, Electrospray Ionization Transcription, Genetic |
title | Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks |
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