Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes - Université de Lille Accéder directement au contenu
Article Dans Une Revue (Article De Synthèse) Annual Reviews of Biophysics Année : 2023

Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes

Résumé

Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.
Fichier principal
Vignette du fichier
wodak-et-al-2023-critical-assessment-of-methods-for-predicting-the-3d-structure-of-proteins-and-protein-complexes.pdf (2.71 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04257466 , version 1 (25-10-2023)
hal-04257466 , version 2 (09-11-2023)

Licence

Identifiants

Citer

Shoshana J Wodak, Sandor Vajda, Marc Lensink, Dima Kozakov, Paul A Bates. Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes. Annual Reviews of Biophysics, 2023, Annual Reviews of Biophysics, 52, pp.183-206. ⟨10.1146/annurev-biophys-102622-084607⟩. ⟨hal-04257466v2⟩

Collections

CNRS UNIV-LILLE
24 Consultations
13 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More