Improving quality of ligand-binding site prediction with Bayesian optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10369452" target="_blank" >RIV/00216208:11320/17:10369452 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/BIBM.2017.8218024" target="_blank" >https://doi.org/10.1109/BIBM.2017.8218024</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/BIBM.2017.8218024" target="_blank" >10.1109/BIBM.2017.8218024</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving quality of ligand-binding site prediction with Bayesian optimization

Popis výsledku v původním jazyce

Ligand binding site prediction from protein structure plays an important role in various complex rational drug design efforts. Its applications include drug side effects prediction, docking prioritization in inverse virtual screening and elucidation of protein function in genome wide structural studies. Currently available tools have limitations that disqualify them from many possible use cases. In general they are either fast and relatively inaccurate (e.g. purely geometric methods) or accurate but too slow for large scale applications (e.g. methods that rely on a large template libraries of known protein-ligand complexes). P2Rank is a recently intorduced machine learning based method that have already exhibited speeds comparable to fastest geometric methods while providing much higher identification success rates. Here we present an improved version that brings speed-up as well as higher quality predictions. A leap in predictive performance was achieved thanks to the technique of Bayesian optimization, which allowed simultaneous optimization of numerous arbitrary parameters of the algorithm. We have evaluated our method with respect to various performance and prediction quality criteria and compared it to other state of the art methods, as well as to it&apos;s previous version, with encouraging results.

Název v anglickém jazyce

Improving quality of ligand-binding site prediction with Bayesian optimization

Popis výsledku anglicky

Ligand binding site prediction from protein structure plays an important role in various complex rational drug design efforts. Its applications include drug side effects prediction, docking prioritization in inverse virtual screening and elucidation of protein function in genome wide structural studies. Currently available tools have limitations that disqualify them from many possible use cases. In general they are either fast and relatively inaccurate (e.g. purely geometric methods) or accurate but too slow for large scale applications (e.g. methods that rely on a large template libraries of known protein-ligand complexes). P2Rank is a recently intorduced machine learning based method that have already exhibited speeds comparable to fastest geometric methods while providing much higher identification success rates. Here we present an improved version that brings speed-up as well as higher quality predictions. A leap in predictive performance was achieved thanks to the technique of Bayesian optimization, which allowed simultaneous optimization of numerous arbitrary parameters of the algorithm. We have evaluated our method with respect to various performance and prediction quality criteria and compared it to other state of the art methods, as well as to it&apos;s previous version, with encouraging results.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

2017 IEEE International Conference on Bioinformatics and Biomedicine

ISBN

978-1-5090-3049-1

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

2

Strana od-do

2278-2279

Název nakladatele

IEEE (The Institute of Electrical and Electronics Engineers)

Místo vydání

Neuveden

Místo konání akce

Kansas City, MO, USA

Datum konání akce

13. 11. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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