Sampling and Ranking of Protein Conformations Using Machine Learning Techniques Do Not Improve the Quality of Rigid Protein–Protein Docking

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F25%3A73633618" target="_blank" >RIV/61989592:15310/25:73633618 - isvavai.cz</a>

Result on the web

<a href="https://pubs.acs.org/doi/full/10.1021/acs.jcim.5c01765" target="_blank" >https://pubs.acs.org/doi/full/10.1021/acs.jcim.5c01765</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acs.jcim.5c01765" target="_blank" >10.1021/acs.jcim.5c01765</a>

Result language

angličtina

Original language name

Sampling and Ranking of Protein Conformations Using Machine Learning Techniques Do Not Improve the Quality of Rigid Protein–Protein Docking

Original language description

Rigid docking remains the most popular method of predicting protein–protein interactions in cases when experimental 3D structures of the complexes are not available. The docking often relies on known unbound (Apo) protein structures, which may differ significantly from their bound (Holo) forms. Modern machine learning (ML) based conformational sampling techniques allow generating ensembles of functionally relevant protein structures, which may be closer to their Holo forms and thus could improve the outcomes of the classical rigid protein–protein docking. Here, we sampled conformations of the protein subunits in 30 complexes from the novel PINDER data set with two state-of-the-art ML-based techniques and evaluated their docking performance using several physics-based, data-based, and ML-based scoring functions. We showed that such conformational sampling rarely produces structures that are closer to the Holo conformations than the corresponding Apo ones. Moreover, even when such conformations are generated, none of the tested scoring functions were able to prioritize and rank them correctly. Our work highlights critical limitations in the current ML-enhanced rigid protein–protein docking workflows and emphasizes the need for new approaches that can better utilize the potential of modern techniques for conformational generation and scoring.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10403 - Physical chemistry

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

Journal of Chemical Information and Modeling

ISSN

—

e-ISSN

1549-960X

Volume of the periodical

65

Issue of the periodical within the volume

19

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

10167-10179

UT code for WoS article

001572368300001

EID of the result in the Scopus database

2-s2.0-105018643888