Using consensus-shape clustering to identify promiscuous ligands and protein targets and to choose the right query for shape-based virtual screening.
Identifieur interne : 000100 ( PubMed/Corpus ); précédent : 000099; suivant : 000101Using consensus-shape clustering to identify promiscuous ligands and protein targets and to choose the right query for shape-based virtual screening.
Auteurs : Violeta I. Pérez-Nueno ; David W. RitchieSource :
- Journal of chemical information and modeling [ 1549-960X ] ; 2011.
English descriptors
- KwdEn :
- MESH :
- chemical , metabolism : Proteins.
- chemical : Ligands.
- methods : Drug Evaluation, Preclinical.
- Area Under Curve, Binding Sites, Cluster Analysis, Protein Binding, Reproducibility of Results, Substrate Specificity, User-Computer Interface.
Abstract
Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, many authors have discussed how best to choose the initial query compounds and which of their conformations should be used. Furthermore, it is increasingly the case that pharmaceutical companies have multiple ligands for a given target and these may bind in different ways to the same pocket. Conversely, a given ligand can sometimes bind to multiple targets, and this is clearly of great importance when considering drug side-effects. We recently introduced the notion of spherical harmonic-based "consensus shapes" to help deal with these questions. Here, we apply a consensus shape clustering approach to the 40 protein-ligand targets in the DUD data set using PARASURF/PARAFIT. Results from clustering show that in some cases the ligands for a given target are split into two subgroups which could suggest they bind to different subsites of the same target. In other cases, our clustering approach sometimes groups together ligands from different targets, and this suggests that those ligands could bind to the same targets. Hence spherical harmonic-based clustering can rapidly give cross-docking information while avoiding the expense of performing all-against-all docking calculations. We also report on the effect of the query conformation on the performance of shape-based screening of the DUD data set and the potential gain in screening performance by using consensus shapes calculated in different ways. We provide details of our analysis of shape-based screening using both PARASURF/PARAFIT and ROCS, and we compare the results obtained with shape-based and conventional docking approaches using MSSH/SHEF and GOLD. The utility of each type of query is analyzed using commonly reported statistics such as enrichment factors (EF) and receiver-operator-characteristic (ROC) plots as well as other early performance metrics.
DOI: 10.1021/ci100492r
PubMed: 21604699
Links to Exploration step
pubmed:21604699Le document en format XML
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Ritchie</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2011">2011</date><idno type="doi">10.1021/ci100492r</idno><idno type="RBID">pubmed:21604699</idno><idno type="pmid">21604699</idno><idno type="wicri:Area/PubMed/Corpus">000100</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000100</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Using consensus-shape clustering to identify promiscuous ligands and protein targets and to choose the right query for shape-based virtual screening.</title><author><name sortKey="Perez Nueno, Violeta I" sort="Perez Nueno, Violeta I" uniqKey="Perez Nueno V" first="Violeta I" last="Pérez-Nueno">Violeta I. Pérez-Nueno</name><affiliation><nlm:affiliation>INRIA Nancy, LORIA, 615 rue du Jardin Botanique, 54600 Villers-lès-Nancy, France. violeta.pereznueno@inria.fr</nlm:affiliation></affiliation></author><author><name sortKey="Ritchie, David W" sort="Ritchie, David W" uniqKey="Ritchie D" first="David W" last="Ritchie">David W. Ritchie</name></author></analytic><series><title level="j">Journal of chemical information and modeling</title><idno type="eISSN">1549-960X</idno><imprint><date when="2011" type="published">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Area Under Curve</term><term>Binding Sites</term><term>Cluster Analysis</term><term>Drug Evaluation, Preclinical (methods)</term><term>Ligands</term><term>Protein Binding</term><term>Proteins (metabolism)</term><term>Reproducibility of Results</term><term>Substrate Specificity</term><term>User-Computer Interface</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Proteins</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>Ligands</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Drug Evaluation, Preclinical</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Area Under Curve</term><term>Binding Sites</term><term>Cluster Analysis</term><term>Protein Binding</term><term>Reproducibility of Results</term><term>Substrate Specificity</term><term>User-Computer Interface</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, many authors have discussed how best to choose the initial query compounds and which of their conformations should be used. Furthermore, it is increasingly the case that pharmaceutical companies have multiple ligands for a given target and these may bind in different ways to the same pocket. Conversely, a given ligand can sometimes bind to multiple targets, and this is clearly of great importance when considering drug side-effects. We recently introduced the notion of spherical harmonic-based "consensus shapes" to help deal with these questions. Here, we apply a consensus shape clustering approach to the 40 protein-ligand targets in the DUD data set using PARASURF/PARAFIT. Results from clustering show that in some cases the ligands for a given target are split into two subgroups which could suggest they bind to different subsites of the same target. In other cases, our clustering approach sometimes groups together ligands from different targets, and this suggests that those ligands could bind to the same targets. Hence spherical harmonic-based clustering can rapidly give cross-docking information while avoiding the expense of performing all-against-all docking calculations. We also report on the effect of the query conformation on the performance of shape-based screening of the DUD data set and the potential gain in screening performance by using consensus shapes calculated in different ways. We provide details of our analysis of shape-based screening using both PARASURF/PARAFIT and ROCS, and we compare the results obtained with shape-based and conventional docking approaches using MSSH/SHEF and GOLD. The utility of each type of query is analyzed using commonly reported statistics such as enrichment factors (EF) and receiver-operator-characteristic (ROC) plots as well as other early performance metrics.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">21604699</PMID><DateCreated><Year>2011</Year><Month>06</Month><Day>27</Day></DateCreated><DateCompleted><Year>2011</Year><Month>10</Month><Day>21</Day></DateCompleted><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1549-960X</ISSN><JournalIssue CitedMedium="Internet"><Volume>51</Volume><Issue>6</Issue><PubDate><Year>2011</Year><Month>Jun</Month><Day>27</Day></PubDate></JournalIssue><Title>Journal of chemical information and modeling</Title><ISOAbbreviation>J Chem Inf Model</ISOAbbreviation></Journal><ArticleTitle>Using consensus-shape clustering to identify promiscuous ligands and protein targets and to choose the right query for shape-based virtual screening.</ArticleTitle><Pagination><MedlinePgn>1233-48</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1021/ci100492r</ELocationID><Abstract><AbstractText>Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, many authors have discussed how best to choose the initial query compounds and which of their conformations should be used. Furthermore, it is increasingly the case that pharmaceutical companies have multiple ligands for a given target and these may bind in different ways to the same pocket. Conversely, a given ligand can sometimes bind to multiple targets, and this is clearly of great importance when considering drug side-effects. We recently introduced the notion of spherical harmonic-based "consensus shapes" to help deal with these questions. Here, we apply a consensus shape clustering approach to the 40 protein-ligand targets in the DUD data set using PARASURF/PARAFIT. Results from clustering show that in some cases the ligands for a given target are split into two subgroups which could suggest they bind to different subsites of the same target. In other cases, our clustering approach sometimes groups together ligands from different targets, and this suggests that those ligands could bind to the same targets. Hence spherical harmonic-based clustering can rapidly give cross-docking information while avoiding the expense of performing all-against-all docking calculations. We also report on the effect of the query conformation on the performance of shape-based screening of the DUD data set and the potential gain in screening performance by using consensus shapes calculated in different ways. We provide details of our analysis of shape-based screening using both PARASURF/PARAFIT and ROCS, and we compare the results obtained with shape-based and conventional docking approaches using MSSH/SHEF and GOLD. The utility of each type of query is analyzed using commonly reported statistics such as enrichment factors (EF) and receiver-operator-characteristic (ROC) plots as well as other early performance metrics.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Pérez-Nueno</LastName><ForeName>Violeta I</ForeName><Initials>VI</Initials><AffiliationInfo><Affiliation>INRIA Nancy, LORIA, 615 rue du Jardin Botanique, 54600 Villers-lès-Nancy, France. violeta.pereznueno@inria.fr</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ritchie</LastName><ForeName>David W</ForeName><Initials>DW</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2011</Year><Month>06</Month><Day>04</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>J Chem Inf Model</MedlineTA><NlmUniqueID>101230060</NlmUniqueID><ISSNLinking>1549-9596</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D008024">Ligands</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D011506">Proteins</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D019540">Area Under Curve</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D001665">Binding Sites</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D016000">Cluster Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D004353">Drug Evaluation, Preclinical</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D008024">Ligands</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D011485">Protein Binding</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D011506">Proteins</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000378">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D015203">Reproducibility of Results</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D013379">Substrate Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D014584">User-Computer Interface</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="aheadofprint"><Year>2011</Year><Month>6</Month><Day>4</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2011</Year><Month>5</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2011</Year><Month>5</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2011</Year><Month>10</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1021/ci100492r</ArticleId><ArticleId IdType="pubmed">21604699</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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