Collective Variable for Metadynamics Derived From AlphaFold Output

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Authors

SPIWOK Vojtěch KUREČKA Martin KŘENEK Aleš

Year of publication 2022
Type Article in Periodical
Magazine / Source FRONTIERS IN MOLECULAR BIOSCIENCES
MU Faculty or unit

Institute of Computer Science

Citation
Web https://www.frontiersin.org/articles/10.3389/fmolb.2022.878133/full?&utm_source=Email_to_authors_&utm_medium=Email&utm_content=T1_11.5e1_author&utm_campaign=Email_publication&field=&journalName=Frontiers_in_Molecular_Biosciences&id=878133
Doi http://dx.doi.org/10.3389/fmolb.2022.878133
Keywords protein folding; alphafold; collective variable
Description 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 ß 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.
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