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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Journal of Chemical Information and Modeling

ISSN

—

e-ISSN

1549-960X

Svazek periodika

65

Číslo periodika v rámci svazku

19

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

10167-10179

Kód UT WoS článku

001572368300001

EID výsledku v databázi Scopus

2-s2.0-105018643888