Deep learning for intrinsically disordered proteins: From improved predictions to deciphering conformational ensembles

Résumé fourni par la source

Intrinsically disordered proteins (IDPs) lack a stable three-dimensional structure under physiological conditions, challenging traditional structure-based prediction methods. This review explores how modern deep learning approaches, which have revolutionized structure prediction for globular proteins, have impacted protein disorder predictions. We highlight the role of community-driven efforts in curating data and assessing state-of-the-art, which have been crucial in advancing the field. We also review state-of-the-art methods utilizing deep learning techniques, highlighting innovative approaches. We also address advancements in characterizing protein conformational ensembles directly from sequence data using novel machine learning methods. • Intrinsically disordered proteins (IDPs) defy traditional structure-based prediction methods, pushing for novel approaches in protein research. • Recent advances in deep learning are now significantly enhancing the prediction and characterization of protein disorder. • Collaborative data curation and benchmarking initiatives have driven progress in IDP research. • Machine learning techniques now enable direct prediction of protein conformational ensembles from sequence data, providing new insights into the dynamic nature of IDPs.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.