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Collective Variable for Metadynamics Derived From AlphaFold Output

Authors :
Vojtěch Spiwok
Martin Kurečka
Aleš Křenek
Source :
Frontiers in Molecular Biosciences, Vol 9 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

Abstract

AlphaFold is a neural network–based tool for the prediction of 3D structures of proteins. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, making it the best available structure prediction tool. One of the outputs of AlphaFold is the probability profile of residue–residue distances. This makes it possible to score any conformation of the studied protein to express its compliance with the AlphaFold model. Here, we show how this score can be used to drive protein folding simulation by metadynamics and parallel tempering metadynamics. Using parallel tempering metadynamics, we simulated the folding of a mini-protein Trp-cage and β hairpin and predicted their folding equilibria. We observe the potential of the AlphaFold-based collective variable in applications beyond structure prediction, such as in structure refinement or prediction of the outcome of a mutation.

Details

Language :
English
ISSN :
2296889X
Volume :
9
Database :
Directory of Open Access Journals
Journal :
Frontiers in Molecular Biosciences
Publication Type :
Academic Journal
Accession number :
edsdoj.2421ecb4b8274e9a88038f742176b794
Document Type :
article
Full Text :
https://doi.org/10.3389/fmolb.2022.878133