A Collaborative Filtering Approach for Protein-Protein Docking Scoring Functions
Identifieur interne : 002602 ( Main/Merge ); précédent : 002601; suivant : 002603A Collaborative Filtering Approach for Protein-Protein Docking Scoring Functions
Auteurs : Thomas Bourquard [France] ; Julie Bernauer [France] ; Jérôme Azé [France] ; Anne Poupon [France]Source :
- PLoS ONE [ 1932-6203 ] ; 2011.
Abstract
A protein-protein docking procedure traditionally consists in two successive
tasks: a search algorithm generates a large number of candidate conformations
mimicking the complex existing
In this work, we introduce a new procedure for conformation ranking. We previously developed a set of scoring functions where learning was performed using a genetic algorithm. These functions were used to assign a rank to each possible conformation. We now have a refined rank using different classifiers (decision trees, rules and support vector machines) in a collaborative filtering scheme. The scoring function newly obtained is evaluated using 10 fold cross-validation, and compared to the functions obtained using either genetic algorithms or collaborative filtering taken separately.
This new approach was successfully applied to the CAPRI scoring ensembles. We show that for 10 targets out of 12, we are able to find a near-native conformation in the 10 best ranked solutions. Moreover, for 6 of them, the near-native conformation selected is of high accuracy. Finally, we show that this function dramatically enriches the 100 best-ranking conformations in near-native structures.
Url:
DOI: 10.1371/journal.pone.0018541
PubMed: 21526112
PubMed Central: 3081294
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PMC:3081294Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>A protein-protein docking procedure traditionally consists in two successive
tasks: a search algorithm generates a large number of candidate conformations
mimicking the complex existing <italic>in vivo</italic>
between two proteins,
and a scoring function is used to rank them in order to extract a native-like
one. We have already shown that using Voronoi constructions and a well chosen
set of parameters, an accurate scoring function could be designed and optimized.
However to be able to perform large-scale <italic>in silico</italic>
exploration
of the interactome, a near-native solution has to be found in the ten
best-ranked solutions. This cannot yet be guaranteed by any of the existing
scoring functions.</p>
<p>In this work, we introduce a new procedure for conformation ranking. We
previously developed a set of scoring functions where learning was performed
using a genetic algorithm. These functions were used to assign a rank to each
possible conformation. We now have a refined rank using different classifiers
(decision trees, rules and support vector machines) in a collaborative filtering
scheme. The scoring function newly obtained is evaluated using 10 fold
cross-validation, and compared to the functions obtained using either genetic
algorithms or collaborative filtering taken separately.</p>
<p>This new approach was successfully applied to the CAPRI scoring ensembles. We
show that for 10 targets out of 12, we are able to find a near-native
conformation in the 10 best ranked solutions. Moreover, for 6 of them, the
near-native conformation selected is of high accuracy. Finally, we show that
this function dramatically enriches the 100 best-ranking conformations in
near-native structures.</p>
</div>
</front>
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<idno type="DOI">10.1371/journal.pone.0018541</idno>
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<front><div type="abstract" xml:lang="en">A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions. In this work, we introduce a new procedure for conformation ranking. We previously developed a set of scoring functions where learning was performed using a genetic algorithm. These functions were used to assign a rank to each possible conformation. We now have a refined rank using different classifiers (decision trees, rules and support vector machines) in a collaborative filtering scheme. The scoring function newly obtained is evaluated using 10 fold cross-validation, and compared to the functions obtained using either genetic algorithms or collaborative filtering taken separately. This new approach was successfully applied to the CAPRI scoring ensembles. We show that for 10 targets out of 12, we are able to find a near-native conformation in the 10 best ranked solutions. Moreover, for 6 of them, the near-native conformation selected is of high accuracy. Finally, we show that this function dramatically enriches the 100 best-ranking conformations in near-native structures.</div>
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<PMC><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A Collaborative Filtering Approach for Protein-Protein Docking
Scoring Functions</title>
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<author><name sortKey="Aze, Jerome" sort="Aze, Jerome" uniqKey="Aze J" first="Jérôme" last="Azé">Jérôme Azé</name>
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<placeName><settlement type="city">Orsay</settlement>
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<wicri:regionArea>Université François Rabelais, Tours</wicri:regionArea>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A Collaborative Filtering Approach for Protein-Protein Docking
Scoring Functions</title>
<author><name sortKey="Bourquard, Thomas" sort="Bourquard, Thomas" uniqKey="Bourquard T" first="Thomas" last="Bourquard">Thomas Bourquard</name>
<affiliation wicri:level="4"><nlm:aff id="aff1"><addr-line>Bioinformatics Group, INRIA AMIB, Laboratoire de Recherche en Informatique, Université Paris-Sud, Orsay, France</addr-line>
</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Bioinformatics Group, INRIA AMIB, Laboratoire de Recherche en Informatique, Université Paris-Sud, Orsay</wicri:regionArea>
<placeName><settlement type="city">Orsay</settlement>
</placeName>
<orgName type="university">Université Paris-Sud</orgName>
</affiliation>
<affiliation wicri:level="3"><nlm:aff id="aff2"><addr-line>Bioinformatics Group, INRIA AMIB, Laboratoire d'Informatique (LIX), École Polytechnique, Palaiseau, France</addr-line>
</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Bioinformatics Group, INRIA AMIB, Laboratoire d'Informatique (LIX), École Polytechnique, Palaiseau</wicri:regionArea>
<placeName><region type="region">Île-de-France</region>
<region type="old region">Île-de-France</region>
<settlement type="city">Palaiseau</settlement>
</placeName>
</affiliation>
<affiliation wicri:level="3"><nlm:aff id="aff3"><addr-line>INRIA Nancy Grand Est, LORIA, Vandoeuvre-lès-Nancy, France</addr-line>
</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>INRIA Nancy Grand Est, LORIA, Vandoeuvre-lès-Nancy</wicri:regionArea>
<placeName><region type="region" nuts="2">Grand Est</region>
<region type="old region" nuts="2">Lorraine (région)</region>
<settlement type="city">Vandœuvre-lès-Nancy</settlement>
<settlement type="city" wicri:auto="agglo">Nancy</settlement>
</placeName>
</affiliation>
</author>
<author><name sortKey="Bernauer, Julie" sort="Bernauer, Julie" uniqKey="Bernauer J" first="Julie" last="Bernauer">Julie Bernauer</name>
<affiliation wicri:level="3"><nlm:aff id="aff2"><addr-line>Bioinformatics Group, INRIA AMIB, Laboratoire d'Informatique (LIX), École Polytechnique, Palaiseau, France</addr-line>
</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Bioinformatics Group, INRIA AMIB, Laboratoire d'Informatique (LIX), École Polytechnique, Palaiseau</wicri:regionArea>
<placeName><region type="region">Île-de-France</region>
<region type="old region">Île-de-France</region>
<settlement type="city">Palaiseau</settlement>
</placeName>
</affiliation>
</author>
<author><name sortKey="Aze, Jerome" sort="Aze, Jerome" uniqKey="Aze J" first="Jérôme" last="Azé">Jérôme Azé</name>
<affiliation wicri:level="4"><nlm:aff id="aff1"><addr-line>Bioinformatics Group, INRIA AMIB, Laboratoire de Recherche en Informatique, Université Paris-Sud, Orsay, France</addr-line>
</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Bioinformatics Group, INRIA AMIB, Laboratoire de Recherche en Informatique, Université Paris-Sud, Orsay</wicri:regionArea>
<placeName><settlement type="city">Orsay</settlement>
</placeName>
<orgName type="university">Université Paris-Sud</orgName>
</affiliation>
</author>
<author><name sortKey="Poupon, Anne" sort="Poupon, Anne" uniqKey="Poupon A" first="Anne" last="Poupon">Anne Poupon</name>
<affiliation wicri:level="1"><nlm:aff id="aff4"><addr-line>BIOS Group, INRA, UMR85, Unité Physiologie de la Reproduction et des Comportements, Nouzilly, France</addr-line>
</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>BIOS Group, INRA, UMR85, Unité Physiologie de la Reproduction et des Comportements, Nouzilly</wicri:regionArea>
<wicri:noRegion>Nouzilly</wicri:noRegion>
<wicri:noRegion>Nouzilly</wicri:noRegion>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff5"><addr-line>CNRS, UMR6175, Nouzilly, France</addr-line>
</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>CNRS, UMR6175, Nouzilly</wicri:regionArea>
<wicri:noRegion>Nouzilly</wicri:noRegion>
<wicri:noRegion>Nouzilly</wicri:noRegion>
</affiliation>
<affiliation wicri:level="3"><nlm:aff id="aff6"><addr-line>Université François Rabelais, Tours, France</addr-line>
</nlm:aff>
<country xml:lang="fr">France</country>
<wicri:regionArea>Université François Rabelais, Tours</wicri:regionArea>
<placeName><region type="region">Centre-Val de Loire</region>
<region type="old region">Région Centre</region>
<settlement type="city">Tours</settlement>
</placeName>
</affiliation>
</author>
</analytic>
<series><title level="j">PLoS ONE</title>
<idno type="eISSN">1932-6203</idno>
<imprint><date when="2011">2011</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en"><p>A protein-protein docking procedure traditionally consists in two successive
tasks: a search algorithm generates a large number of candidate conformations
mimicking the complex existing <italic>in vivo</italic>
between two proteins,
and a scoring function is used to rank them in order to extract a native-like
one. We have already shown that using Voronoi constructions and a well chosen
set of parameters, an accurate scoring function could be designed and optimized.
However to be able to perform large-scale <italic>in silico</italic>
exploration
of the interactome, a near-native solution has to be found in the ten
best-ranked solutions. This cannot yet be guaranteed by any of the existing
scoring functions.</p>
<p>In this work, we introduce a new procedure for conformation ranking. We
previously developed a set of scoring functions where learning was performed
using a genetic algorithm. These functions were used to assign a rank to each
possible conformation. We now have a refined rank using different classifiers
(decision trees, rules and support vector machines) in a collaborative filtering
scheme. The scoring function newly obtained is evaluated using 10 fold
cross-validation, and compared to the functions obtained using either genetic
algorithms or collaborative filtering taken separately.</p>
<p>This new approach was successfully applied to the CAPRI scoring ensembles. We
show that for 10 targets out of 12, we are able to find a near-native
conformation in the 10 best ranked solutions. Moreover, for 6 of them, the
near-native conformation selected is of high accuracy. Finally, we show that
this function dramatically enriches the 100 best-ranking conformations in
near-native structures.</p>
</div>
</front>
<back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Wodak, Sj" uniqKey="Wodak S">SJ Wodak</name>
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</analytic>
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<biblStruct><analytic><author><name sortKey="Sanderson, Cm" uniqKey="Sanderson C">CM Sanderson</name>
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</analytic>
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</analytic>
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<biblStruct><analytic><author><name sortKey="Kastritis, Pl" uniqKey="Kastritis P">PL Kastritis</name>
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</analytic>
</biblStruct>
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</biblStruct>
<biblStruct><analytic><author><name sortKey="Andrusier, N" uniqKey="Andrusier N">N Andrusier</name>
</author>
<author><name sortKey="Mashiach, E" uniqKey="Mashiach E">E Mashiach</name>
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<author><name sortKey="Wolfson, Hj" uniqKey="Wolfson H">HJ Wolfson</name>
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</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Bernauer, J" uniqKey="Bernauer J">J Bernauer</name>
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<author><name sortKey="Rodier, F" uniqKey="Rodier F">F Rodier</name>
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</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Bernauer, J" uniqKey="Bernauer J">J Bernauer</name>
</author>
<author><name sortKey="Poupon, A" uniqKey="Poupon A">A Poupon</name>
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<author><name sortKey="Aze, J" uniqKey="Aze J">J Azé</name>
</author>
<author><name sortKey="Janin, J" uniqKey="Janin J">J Janin</name>
</author>
</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Bernauer, J" uniqKey="Bernauer J">J Bernauer</name>
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</author>
<author><name sortKey="Poupon, A" uniqKey="Poupon A">A Poupon</name>
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</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Bourquard, T" uniqKey="Bourquard T">T Bourquard</name>
</author>
<author><name sortKey="Bernauer, J" uniqKey="Bernauer J">J Bernauer</name>
</author>
<author><name sortKey="Aze, J" uniqKey="Aze J">J Azé</name>
</author>
<author><name sortKey="Poupon, A" uniqKey="Poupon A">A Poupon</name>
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</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Boissonnat, Jd" uniqKey="Boissonnat J">JD Boissonnat</name>
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</biblStruct>
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</biblStruct>
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<biblStruct><analytic><author><name sortKey="Joachims, T" uniqKey="Joachims T">T Joachims</name>
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</biblStruct>
<biblStruct><analytic><author><name sortKey="Lensink, Mf" uniqKey="Lensink M">MF Lensink</name>
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<author><name sortKey="Mendez, R" uniqKey="Mendez R">R Mendez</name>
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</biblStruct>
<biblStruct><analytic><author><name sortKey="Tong, Y" uniqKey="Tong Y">Y Tong</name>
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</biblStruct>
<biblStruct><analytic><author><name sortKey="Le Cessie, S" uniqKey="Le Cessie S">S le Cessie</name>
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<biblStruct><analytic><author><name sortKey="Scholkopf, B" uniqKey="Scholkopf B">B Schölkopf</name>
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</biblStruct>
<biblStruct><analytic><author><name sortKey="Cohen, Ww" uniqKey="Cohen W">WW Cohen</name>
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</biblStruct>
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</author>
</analytic>
</biblStruct>
</listBibl>
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