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Modeling conformational states of proteins with AlphaFold.
- Source :
-
Current opinion in structural biology [Curr Opin Struct Biol] 2023 Aug; Vol. 81, pp. 102645. Date of Electronic Publication: 2023 Jun 29. - Publication Year :
- 2023
-
Abstract
- Many proteins exert their function by switching among different structures. Knowing the conformational ensembles affiliated with these states is critical to elucidate key mechanistic aspects that govern protein function. While experimental determination efforts are still bottlenecked by cost, time, and technical challenges, the machine-learning technology AlphaFold showed near experimental accuracy in predicting the three-dimensional structure of monomeric proteins. However, an AlphaFold ensemble of models usually represents a single conformational state with minimal structural heterogeneity. Consequently, several pipelines have been proposed to either expand the structural breadth of an ensemble or bias the prediction toward a desired conformational state. Here, we analyze how those pipelines work, what they can and cannot predict, and future directions.<br />Competing Interests: 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.<br /> (Copyright © 2023 Elsevier Ltd. All rights reserved.)
- Subjects :
- Protein Conformation
Proteins chemistry
Subjects
Details
- Language :
- English
- ISSN :
- 1879-033X
- Volume :
- 81
- Database :
- MEDLINE
- Journal :
- Current opinion in structural biology
- Publication Type :
- Academic Journal
- Accession number :
- 37392556
- Full Text :
- https://doi.org/10.1016/j.sbi.2023.102645