Computational methods to predict protein aggregation.
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 2022
04 2022
Historique:
received:
16
08
2021
revised:
20
12
2021
accepted:
17
01
2022
pubmed:
4
3
2022
medline:
19
4
2022
entrez:
3
3
2022
Statut:
ppublish
Résumé
In most cases, protein aggregation stems from the establishment of non-native intermolecular contacts. The formation of insoluble protein aggregates is associated with many human diseases and is a major bottleneck for the industrial production of protein-based therapeutics. Strikingly, fibrillar aggregates are naturally exploited for structural scaffolding or to generate molecular switches and can be artificially engineered to build up multi-functional nanomaterials. Thus, there is a high interest in rationalizing and forecasting protein aggregation. Here, we review the available computational toolbox to predict protein aggregation propensities, identify sequential or structural aggregation-prone regions, evaluate the impact of mutations on aggregation or recognize prion-like domains. We discuss the strengths and limitations of these algorithms and how they can evolve in the next future.
Identifiants
pubmed: 35240456
pii: S0959-440X(22)00016-1
doi: 10.1016/j.sbi.2022.102343
pii:
doi:
Substances chimiques
Protein Aggregates
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
102343Informations de copyright
Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.