VoteDock: Consensus docking method for prediction of protein–ligand interactions
Identifieur interne : 002387 ( Istex/Corpus ); précédent : 002386; suivant : 002388VoteDock: Consensus docking method for prediction of protein–ligand interactions
Auteurs : Dariusz Plewczynski ; Michał Ła Niewski ; Marcin Von Grotthuss ; Leszek Rychlewski ; Krzysztof GinalskiSource :
- Journal of Computational Chemistry [ 0192-8651 ] ; 2011-03.
English descriptors
- Teeft :
- 3dscore, Active site, Algorithm, Autodock, Benchmark, Benchmarking, Best docking program, Best program, Binding prediction, Chem, Comput, Comput chem, Computational, Computational chemistry, Conformation, Conformers, Consensus, Consensus algorithms, Consensus approach, Consensus docking method, Consensus method, Corina, Database, Dataset, Datasets, Docking, Docking accuracy, Docking program, Docking programs, Docking results, Ehits, Flexx, Individual docking programs, Ligand, Ligand size, Ligandfit, Metapose, Metascore, Nally, Omega2, Optimization, Pdbbind, Pearson correlation, Plewczynski, Protein target, Receptor, Rmsd, Rmsd value, Rmsd values, Rotatable bonds, Score conformation, Software, Spearman, Spearman correlations, Subset, Vote2, Vote7, Votedock, Wang, Whole benchmarking dataset.
Abstract
Molecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein–ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time‐consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein–ligand docking programs can be used. The aim of docking procedure is to predict correct poses of ligand in the binding site of the protein as well as to score them according to the strength of interaction in a reasonable time frame. The purpose of our studies was to present the novel consensus approach to predict both protein–ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein–ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5. © 2010 Wiley Periodicals, Inc. J Comput Chem 32: 568–581, 2011
Url:
DOI: 10.1002/jcc.21642
Links to Exploration step
ISTEX:1621E659B6DFEDAA19A47A9A199BA0FE5626530ALe document en format XML
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recognition</value></json:item></subject><articleId><json:string>JCC21642</json:string></articleId><arkIstex>ark:/67375/WNG-TGTW0PBS-8</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>Molecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein–ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time‐consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein–ligand docking programs can be used. The aim of docking procedure is to predict correct poses of ligand in the binding site of the protein as well as to score them according to the strength of interaction in a reasonable time frame. The purpose of our studies was to present the novel consensus approach to predict both protein–ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein–ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5. © 2010 Wiley Periodicals, Inc. 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type="first">Dariusz</forename><surname>Plewczynski</surname></persName><email>darman@icm.edu.pl</email><affiliation><orgName type="division">Interdisciplinary Centre for Mathematical and Computational Modelling</orgName><orgName type="institution">University of Warsaw</orgName><address><addrLine>Pawinskiego 5a Street</addrLine><addrLine>02‐106 Warsaw, Poland</addrLine><country key="PL" xml:lang="en">POLAND</country></address></affiliation></author><author xml:id="author-0001"><persName><forename type="first">Michał</forename><surname>Łażniewski</surname></persName><affiliation><orgName type="division">Interdisciplinary Centre for Mathematical and Computational Modelling</orgName><orgName type="institution">University of Warsaw</orgName><address><addrLine>Pawinskiego 5a Street</addrLine><addrLine>02‐106 Warsaw, Poland</addrLine><country key="PL" xml:lang="en">POLAND</country></address></affiliation><affiliation><orgName type="division">Department of Physical Chemistry</orgName><orgName type="department">Faculty of Pharmacy</orgName><orgName type="institution">Medical University of Warsaw</orgName><address><addrLine>Banacha 1 Street</addrLine><addrLine>Warsaw, Poland</addrLine><country key="PL" xml:lang="en">POLAND</country></address></affiliation></author><author xml:id="author-0002"><persName><forename type="first">Marcin Von</forename><surname>Grotthuss</surname></persName><affiliation><orgName type="laboratory">Department of Chemistry and Chemical Biology</orgName><orgName type="institution">Harvard University</orgName><address><addrLine>Cambridge</addrLine><addrLine>Massachusetts 02138</addrLine><country key="US" xml:lang="en">UNITED STATES</country></address></affiliation></author><author xml:id="author-0003"><persName><forename type="first">Leszek</forename><surname>Rychlewski</surname></persName><affiliation><orgName type="institution">BioInfoBank Institute</orgName><address><addrLine>Poznan</addrLine><addrLine>Poland</addrLine><country key="PL" 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The purpose of our studies was to present the novel consensus approach to predict both protein–ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein–ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5. © 2010 Wiley Periodicals, Inc. J Comput Chem 32: 568–581, 2011</p></abstract><abstract xml:lang="en" style="graphical"><p><figure type="box"><media mimeType="image" url="urn:x-wiley:01928651:media:JCC21642:gra001"></media><media mimeType="image/gif" url="" rendition="webLoRes"></media><media mimeType="image/jpeg" url="" rendition="webOriginal"></media><media mimeType="image/jpeg" url="" rendition="webHiRes"></media></figure></p></abstract><textClass><keywords xml:lang="en"><term xml:id="kwd1">drug discovery</term><term xml:id="kwd2">PDBbind database</term><term xml:id="kwd3">docking</term><term xml:id="kwd4">consensus</term><term xml:id="kwd5">molecular recognition</term></keywords></textClass><langUsage><language ident="en"></language></langUsage></profileDesc><revisionDesc><change when="2019-12-20" who="#istex" xml:id="pub2tei">formatting</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/ark:/67375/WNG-TGTW0PBS-8/fulltext.txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>Wiley Subscription Services, Inc., A Wiley Company</publisherName><publisherLoc>Hoboken</publisherLoc></publisherInfo><doi registered="yes">10.1002/(ISSN)1096-987X</doi><issn type="print">0192-8651</issn><issn type="electronic">1096-987X</issn><idGroup><id type="product" value="JCC"></id></idGroup><titleGroup><title type="main" xml:lang="en" sort="JOURNAL OF COMPUTATIONAL CHEMISTRY">Journal of Computational Chemistry</title><title type="short">J. 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Additional supporting information may be found in the online version of this article.
</p><supportingInfoItem><mediaResource alt="supporting information" href="urn-x:wiley:01928651:media:jcc21642:JCC_21642_sm_SuppTable1"></mediaResource><caption>Supporting Information Table 1.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting information" href="urn-x:wiley:01928651:media:jcc21642:JCC_21642_sm_SuppTable2"></mediaResource><caption>Supporting Information Table 2.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting information" href="urn-x:wiley:01928651:media:jcc21642:JCC_21642_sm_SuppTable3"></mediaResource><caption>Supporting Information Table 3.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting information" href="urn-x:wiley:01928651:media:jcc21642:JCC_21642_sm_SuppTable4"></mediaResource><caption>Supporting Information Table 4.</caption></supportingInfoItem></supportingInformation><abstractGroup><abstract type="main" xml:lang="en"><title type="main">Abstract</title><p>Molecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein–ligand interactions. 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We evaluated it on the extensive benchmark dataset of 1300 protein–ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5. © 2010 Wiley Periodicals, Inc. J Comput Chem 32: 568–581, 2011</p></abstract><abstract type="graphical" xml:lang="en"><p><blockFixed type="graphic"><mediaResourceGroup><mediaResource alt="image" eRights="yes" copyright="Wiley Periodicals, Inc." href="urn:x-wiley:01928651:media:JCC21642:gra001"></mediaResource><mediaResource alt="thumbnail image" rendition="webLoRes" mimeType="image/gif" href=""></mediaResource><mediaResource alt="original image" rendition="webOriginal" mimeType="image/jpeg" href=""></mediaResource><mediaResource alt="magnified image" rendition="webHiRes" mimeType="image/jpeg" href=""></mediaResource></mediaResourceGroup></blockFixed></p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>VoteDock: Consensus docking method for prediction of protein–ligand interactions</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>VoteDock: Consensus Docking Method</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>VoteDock: Consensus docking method for prediction of protein–ligand interactions</title></titleInfo><name type="personal"><namePart type="given">Dariusz</namePart><namePart type="family">Plewczynski</namePart><affiliation>Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Pawinskiego 5a Street, 02‐106 Warsaw, Poland</affiliation><affiliation>E-mail: darman@icm.edu.pl</affiliation><affiliation>Correspondence address: Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Pawinskiego 5a Street, 02‐106 Warsaw, Poland</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Michał</namePart><namePart type="family">Łażniewski</namePart><affiliation>Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Pawinskiego 5a Street, 02‐106 Warsaw, Poland</affiliation><affiliation>Department of Physical Chemistry, Faculty of Pharmacy, Medical University of Warsaw, Banacha 1 Street, Warsaw, Poland</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Marcin Von</namePart><namePart type="family">Grotthuss</namePart><affiliation>Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Leszek</namePart><namePart type="family">Rychlewski</namePart><affiliation>BioInfoBank Institute, Poznan, Poland</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Krzysztof</namePart><namePart type="family">Ginalski</namePart><affiliation>Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Pawinskiego 5a Street, 02‐106 Warsaw, Poland</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-6N5SZHKN-D">article</genre><originInfo><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><place><placeTerm type="text">Hoboken</placeTerm></place><dateIssued encoding="w3cdtf">2011-03</dateIssued><dateCaptured encoding="w3cdtf">2010-03-15</dateCaptured><dateValid encoding="w3cdtf">2010-06-30</dateValid><copyrightDate encoding="w3cdtf">2011</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><extent unit="figures">1</extent><extent unit="tables">3</extent><extent unit="references">60</extent><extent unit="words">14788</extent></physicalDescription><abstract lang="en">Molecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein–ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time‐consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein–ligand docking programs can be used. The aim of docking procedure is to predict correct poses of ligand in the binding site of the protein as well as to score them according to the strength of interaction in a reasonable time frame. The purpose of our studies was to present the novel consensus approach to predict both protein–ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein–ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5. © 2010 Wiley Periodicals, Inc. J Comput Chem 32: 568–581, 2011</abstract><abstract type="graphical" lang="en"></abstract><note type="funding">OxyGreen (6FP project) - No. KBBE‐2007‐212281; </note><note type="funding">EMBO Installation</note><note type="funding">National Institute of Health - No. 1R01GM081680‐01; </note><note type="funding">Foundation for Polish Science (Focus)</note><note type="funding">Polish Ministry of Science and Higher Education - No. N301 159735; No. N301 159435; No. N301 246436; </note><note type="funding">PepLaser - No. HEALTH‐2007‐223242; </note><subject lang="en"><genre>keywords</genre><topic>drug discovery</topic><topic>PDBbind database</topic><topic>docking</topic><topic>consensus</topic><topic>molecular recognition</topic></subject><relatedItem type="host"><titleInfo><title>Journal of Computational Chemistry</title></titleInfo><titleInfo type="abbreviated"><title>J. Comput. 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