The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors.
Deep learning
Protein Structure Predictions
Structural Bioinformatics
Structural biology
Journal
Current opinion in structural biology
ISSN: 1879-033X
Titre abrégé: Curr Opin Struct Biol
Pays: England
ID NLM: 9107784
Informations de publication
Date de publication:
04 2023
04 2023
Historique:
received:
11
10
2022
revised:
04
01
2023
accepted:
13
01
2023
pubmed:
23
2
2023
medline:
16
3
2023
entrez:
22
2
2023
Statut:
ppublish
Résumé
The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.
Identifiants
pubmed: 36807079
pii: S0959-440X(23)00017-9
doi: 10.1016/j.sbi.2023.102543
pii:
doi:
Substances chimiques
Proteins
0
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
102543Subventions
Organisme : Wellcome Trust
ID : 223739/Z/21/Z
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 221327/Z/20/Z
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 218303/Z/19/Z
Pays : United Kingdom
Informations de copyright
Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.