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

Abstract

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.

Topics & Concepts