Critical Assessment of Methods for Predicting ...
Document type :
Article dans une revue scientifique: Article de synthèse/Review paper
PMID :
Permalink :
Title :
Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes
Author(s) :
Wodak, Shoshana J [Auteur]
VIB-VUB Center for Structural Biology [Bruxelles]
Vajda, Sandor [Auteur]
Department of Biomedical Engineering [Boston]
Boston University [Boston] [BU]
Lensink, Marc [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle (UGSF) - UMR 8576
Kozakov, Dima [Auteur]
Stony Brook University [SUNY] [SBU]
Bates, Paul A [Auteur]
Biomolecular Modelling laboratory [London]
VIB-VUB Center for Structural Biology [Bruxelles]
Vajda, Sandor [Auteur]
Department of Biomedical Engineering [Boston]
Boston University [Boston] [BU]
Lensink, Marc [Auteur]
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Unité de Glycobiologie Structurale et Fonctionnelle (UGSF) - UMR 8576
Kozakov, Dima [Auteur]
Stony Brook University [SUNY] [SBU]
Bates, Paul A [Auteur]
Biomolecular Modelling laboratory [London]
Journal title :
Annual Reviews of Biophysics
Abbreviated title :
Annu Rev Biophys
Volume number :
52
Pages :
183-206
Publisher :
Annual Reviews
Publication date :
2023-05-09
ISSN :
1936-1238
English keyword(s) :
Artificial Intelligence
Protein Conformation
CAPRI
CASP
artificial intelligence
critical assessment of predicted interactions
critical assessment of structure predictions
protein interactions
protein structure prediction
Protein Conformation
CAPRI
CASP
artificial intelligence
critical assessment of predicted interactions
critical assessment of structure predictions
protein interactions
protein structure prediction
HAL domain(s) :
Chimie/Chimie théorique et/ou physique
English abstract : [en]
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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
Université de Lille
CNRS
CNRS
Research team(s) :
Computational Molecular Systems Biology
Submission date :
2023-10-20T13:22:36Z
2023-10-25T07:40:17Z
2023-11-09T18:32:33Z
2023-10-25T07:40:17Z
2023-11-09T18:32:33Z
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