AlphaFold is a neural-network-based tool for the prediction of 3D structures of protein. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, which makes 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. By parallel tempering metadynamics, we simulated folding of a mini-protein Trp-cage beta hairpin and predicted their folding equilibria. We see the potential of AlphaFold-based collective variable in applications beyond structure prediction, such as in structure refinement or prediction of the outcome of a mutation.
@article{arxiv.2203.04848,
title = {Collective Variable for Metadynamics Derived from AlphaFold Output},
author = {Vojtěch Spiwok and Martin Kurečka and Aleš Křenek},
journal= {arXiv preprint arXiv:2203.04848},
year = {2022}
}