Cryptic binding site prediction with protein language models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10476944" target="_blank" >RIV/00216208:11320/23:10476944 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11310/23:10476944

Výsledek na webu

<a href="https://doi.org/10.1109/BIBM58861.2023.10385497" target="_blank" >https://doi.org/10.1109/BIBM58861.2023.10385497</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/BIBM58861.2023.10385497" target="_blank" >10.1109/BIBM58861.2023.10385497</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cryptic binding site prediction with protein language models

Popis výsledku v původním jazyce

Structure-based identification of protein-ligand binding sites plays a crucial role in the initial stages of rational drug discovery pipelines. As machine learning methods are increasingly integrated into the process, a significant challenge arises while training these methods, as labeled data are typically derived from ligand-bound structures. Consequently, these methods struggle to detect binding sites within proteins where the binding site is concealed in the absence of a bound ligand. Here, we explore the possibility of harnessing protein language models to address this issue and compare their performance against state-of-the-art methods, both those specialized in the cryptic binding site (CBS) detection and those that are not. We show that applying pre-trained protein-language models in a relatively straightforward manner enables us to surpass the state-of-the-art of CBS prediction.

Název v anglickém jazyce

Cryptic binding site prediction with protein language models

Popis výsledku anglicky

Structure-based identification of protein-ligand binding sites plays a crucial role in the initial stages of rational drug discovery pipelines. As machine learning methods are increasingly integrated into the process, a significant challenge arises while training these methods, as labeled data are typically derived from ligand-bound structures. Consequently, these methods struggle to detect binding sites within proteins where the binding site is concealed in the absence of a bound ligand. Here, we explore the possibility of harnessing protein language models to address this issue and compare their performance against state-of-the-art methods, both those specialized in the cryptic binding site (CBS) detection and those that are not. We show that applying pre-trained protein-language models in a relatively straightforward manner enables us to surpass the state-of-the-art of CBS prediction.

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

<a href="/cs/project/GA23-07349S" target="_blank" >GA23-07349S: Detekce kryptických aktivních míst proteinů pomocí strojového učení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

ISBN

979-8-3503-3748-8

ISSN

—

e-ISSN

2156-1133

Počet stran výsledku

3

Strana od-do

4935-4937

Název nakladatele

IEEE

Místo vydání

USA

Místo konání akce

Istanbul, Turkiye

Datum konání akce

5. 12. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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