P2RANK: knowledge-based ligand binding site prediction using aggregated local features
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
P2RANK: knowledge-based ligand binding site prediction using aggregated local features
Original language description
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
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GP14-29032P" target="_blank" >GP14-29032P: Efficient chemical space exploration using multi-objective optimization</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Algorithms for Computational Biology
ISBN
978-3-319-21232-6
ISSN
0302-9743
e-ISSN
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Number of pages
12
Pages from-to
41-52
Publisher name
Springer International Publishing
Place of publication
Switzerland
Event location
Mexico City, Mexico
Event date
Aug 4, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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