Using Consensus-Shape Clustering To Identify Promiscuous Ligands and Protein Targets and To Choose the Right Query for Shape-Based Virtual Screening
Identifieur interne : 002798 ( Main/Exploration ); précédent : 002797; suivant : 002799Using Consensus-Shape Clustering To Identify Promiscuous Ligands and Protein Targets and To Choose the Right Query for Shape-Based Virtual Screening
Auteurs : Violeta I. Perez-Nueno [France] ; David W. Ritchie [France]Source :
- Journal of chemical information and modeling [ 1549-9596 ] ; 2011.
Descripteurs français
- KwdFr :
- MESH :
- métabolisme : Protéines.
- Pascal (Inist)
- Aire sous la courbe, Analyse de regroupements, Analyse donnée, Chimie informatique, Concordance forme, Interface utilisateur, Liaison aux protéines, Ligands, Reproductibilité des résultats, Sites de fixation, Spécificité du substrat, Traitement image, Système réparti, Consolidation, Ligand, Protéine, Industrie pharmaceutique, Cible multiple, Analyse amas, Médicament, Effet bord, Harmonique sphérique, Criblage virtuel, Conformation, Analyse statistique, Approche probabiliste, Forme sphérique, ., Contrôle déformation mécanique, Reconnaissance objet, Évaluation préclinique de médicament.
- Wicri :
- topic : Industrie pharmaceutique, Médicament.
English descriptors
- KwdEn :
- Area Under Curve, Binding Sites, Cluster Analysis, Cluster analysis, Computational chemistry, Conformation, Consolidation, Data analysis, Distributed system, Drug, Drug Evaluation, Preclinical (methods), Edge effect, Image processing, Ligand, Ligands, Multiple target, Object recognition, Pattern matching, Pharmaceutical industry, Probabilistic approach, Protein, Protein Binding, Proteins (metabolism), Reproducibility of Results, Spherical harmonic, Spherical shape, Statistical analysis, Strain control, Substrate Specificity, User-Computer Interface, Virtual screening.
- 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.
- mix :
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.
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Le document en format XML
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Perez-Nueno</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>INRIA Nancy, LORIA, 615 rue du Jardin Botanique</s1><s2>54600 Villers-lès-Nancy</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Villers-lès-Nancy</settlement></placeName></affiliation></author><author><name sortKey="Ritchie, David W" sort="Ritchie, David W" uniqKey="Ritchie D" first="David W." last="Ritchie">David W. Ritchie</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>INRIA Nancy, LORIA, 615 rue du Jardin Botanique</s1><s2>54600 Villers-lès-Nancy</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Villers-lès-Nancy</settlement></placeName></affiliation></author></analytic><series><title level="j" type="main">Journal of chemical information and modeling</title><title level="j" type="abbreviated">J. chem. inf. model. </title><idno type="ISSN">1549-9596</idno><imprint><date when="2011">2011</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Journal of chemical information and modeling</title><title level="j" type="abbreviated">J. chem. inf. model. </title><idno type="ISSN">1549-9596</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Area Under Curve</term><term>Binding Sites</term><term>Cluster Analysis</term><term>Cluster analysis</term><term>Computational chemistry</term><term>Conformation</term><term>Consolidation</term><term>Data analysis</term><term>Distributed system</term><term>Drug</term><term>Drug Evaluation, Preclinical (methods)</term><term>Edge effect</term><term>Image processing</term><term>Ligand</term><term>Ligands</term><term>Multiple target</term><term>Object recognition</term><term>Pattern matching</term><term>Pharmaceutical industry</term><term>Probabilistic approach</term><term>Protein</term><term>Protein Binding</term><term>Proteins (metabolism)</term><term>Reproducibility of Results</term><term>Spherical harmonic</term><term>Spherical shape</term><term>Statistical analysis</term><term>Strain control</term><term>Substrate Specificity</term><term>User-Computer Interface</term><term>Virtual screening</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Aire sous la courbe</term><term>Analyse de regroupements</term><term>Interface utilisateur</term><term>Liaison aux protéines</term><term>Ligands</term><term>Protéines (métabolisme)</term><term>Reproductibilité des résultats</term><term>Sites de fixation</term><term>Spécificité du substrat</term><term>Évaluation préclinique de médicament ()</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" qualifier="métabolisme" xml:lang="fr"><term>Protéines</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><keywords scheme="Pascal" xml:lang="fr"><term>Aire sous la courbe</term><term>Analyse de regroupements</term><term>Analyse donnée</term><term>Chimie informatique</term><term>Concordance forme</term><term>Interface utilisateur</term><term>Liaison aux protéines</term><term>Ligands</term><term>Reproductibilité des résultats</term><term>Sites de fixation</term><term>Spécificité du substrat</term><term>Traitement image</term><term>Système réparti</term><term>Consolidation</term><term>Ligand</term><term>Protéine</term><term>Industrie pharmaceutique</term><term>Cible multiple</term><term>Analyse amas</term><term>Médicament</term><term>Effet bord</term><term>Harmonique sphérique</term><term>Criblage virtuel</term><term>Conformation</term><term>Analyse statistique</term><term>Approche probabiliste</term><term>Forme sphérique</term><term>.</term><term>Contrôle déformation mécanique</term><term>Reconnaissance objet</term><term>Évaluation préclinique de médicament</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Industrie pharmaceutique</term><term>Médicament</term></keywords><keywords scheme="mix" xml:lang="en"><term>spherical harmonics</term><term>virtual drug screening</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><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Villers-lès-Nancy</li></settlement></list><tree><country name="France"><region name="Grand Est"><name sortKey="Perez Nueno, Violeta I" sort="Perez Nueno, Violeta I" uniqKey="Perez Nueno V" first="Violeta I." last="Perez-Nueno">Violeta I. Perez-Nueno</name></region><name sortKey="Ritchie, David W" sort="Ritchie, David W" uniqKey="Ritchie D" first="David W." last="Ritchie">David W. Ritchie</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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