Cryptic binding site prediction with protein language models

Result code in 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>

Alternative codes found

RIV/00216208:11310/23:10476944

Result on the web

<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>

Result language

angličtina

Original language name

Cryptic binding site prediction with protein language models

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/GA23-07349S" target="_blank" >GA23-07349S: Targeting protein cryptic binding sites with machine learning</a><br>

Continuities

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

Publication year

2023

Confidentiality

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

Article name in the collection

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

ISBN

979-8-3503-3748-8

ISSN

—

e-ISSN

2156-1133

Number of pages

3

Pages from-to

4935-4937

Publisher name

IEEE

Place of publication

USA

Event location

Istanbul, Turkiye

Event date

Dec 5, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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