P2RANK: knowledge-based ligand binding site prediction using aggregated local features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10294721" target="_blank" >RIV/00216208:11320/15:10294721 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-21233-3_4" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-21233-3_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-21233-3_4" target="_blank" >10.1007/978-3-319-21233-3_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

P2RANK: knowledge-based ligand binding site prediction using aggregated local features

Popis výsledku v původním jazyce

The knowledge of protein-ligand binding sites is vital prerequisite for any structure-based virtual screening campaign. If no prior knowledge about binding sites is available, the ligand-binding site prediction methods are the only way to obtain the necessary information. Here we introduce P2RANK, a novel machine learning-based method for prediction of ligand binding sites from protein structure. P2RANK uses Random Forests learner to infer ligandability of local chemical neighborhoods near the protein surface which are represented by specic near-surface points and described by aggregating physico-chemical features projected on those points from neighboring protein atoms. The points with high predicted ligandability are clustered and ranked to obtain the resulting list of binding site predictions. The new method was compared with a state-of-the-art binding site prediction method Fpocket on three representative datasets. The results show that P2RANK outperforms Fpocket by 10 to 20 percen

Název v anglickém jazyce

P2RANK: knowledge-based ligand binding site prediction using aggregated local features

Popis výsledku anglicky

The knowledge of protein-ligand binding sites is vital prerequisite for any structure-based virtual screening campaign. If no prior knowledge about binding sites is available, the ligand-binding site prediction methods are the only way to obtain the necessary information. Here we introduce P2RANK, a novel machine learning-based method for prediction of ligand binding sites from protein structure. P2RANK uses Random Forests learner to infer ligandability of local chemical neighborhoods near the protein surface which are represented by specic near-surface points and described by aggregating physico-chemical features projected on those points from neighboring protein atoms. The points with high predicted ligandability are clustered and ranked to obtain the resulting list of binding site predictions. The new method was compared with a state-of-the-art binding site prediction method Fpocket on three representative datasets. The results show that P2RANK outperforms Fpocket by 10 to 20 percen

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GP14-29032P" target="_blank" >GP14-29032P: Efektivní explorace chemického prostoru s využitím vícekriteriální optimalizace</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Algorithms for Computational Biology

ISBN

978-3-319-21232-6

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

12

Strana od-do

41-52

Název nakladatele

Springer International Publishing

Místo vydání

Switzerland

Místo konání akce

Mexico City, Mexico

Datum konání akce

4. 8. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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