Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10294719" target="_blank" >RIV/00216208:11320/15:10294719 - isvavai.cz</a>
Result on the web
<a href="http://www.jcheminf.com/content/7/1/12/" target="_blank" >http://www.jcheminf.com/content/7/1/12/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1186/s13321-015-0059-5" target="_blank" >10.1186/s13321-015-0059-5</a>

Result language
angličtina
Original language name
Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features
Original language description
Background Protein-ligand binding site prediction from a 3D protein structure plays a pivotal role in rational drug design and can be helpful in drug side-effects prediction or elucidation of protein function. Embedded within the binding site detection problem is the problem of pocket ranking - how to score and sort candidate pockets so that the best scored predictions correspond to true ligand binding sites. Although there exist multiple pocket detection algorithms, they mostly employ a fairly simple ranking function leading to sub-optimal prediction results. Results We have developed a new pocket scoring approach (named PRANK) that prioritizes putative pockets according to their probability to bind a ligand. The method first carefully selects pocketpoints and labels them by physico-chemical characteristics of their local neighborhood. Random Forests classifier is subsequently applied to assign a ligandability score to each of the selected pocket point. The ligandability scores are f
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Project
<a href="/en/project/GP14-29032P" target="_blank" >GP14-29032P: Efficient chemical space exploration using multi-objective optimization</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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