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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

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

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 periodika

Journal of Cheminformatics

ISSN

1758-2946

e-ISSN

—

Svazek periodika

7

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

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EID výsledku v databázi Scopus

2-s2.0-84928563231