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Collective Variable for Metadynamics Derived From AlphaFold Output
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 probab...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9234394/ https://www.ncbi.nlm.nih.gov/pubmed/35769910 http://dx.doi.org/10.3389/fmolb.2022.878133 |
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author | Spiwok, Vojtěch Kurečka, Martin Křenek, Aleš |
author_facet | Spiwok, Vojtěch Kurečka, Martin Křenek, Aleš |
author_sort | Spiwok, Vojtěch |
collection | PubMed |
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 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. |
format | Online Article Text |
id | pubmed-9234394 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92343942022-06-28 Collective Variable for Metadynamics Derived From AlphaFold Output Spiwok, Vojtěch Kurečka, Martin Křenek, Aleš Front Mol Biosci Molecular Biosciences 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. Frontiers Media S.A. 2022-06-13 /pmc/articles/PMC9234394/ /pubmed/35769910 http://dx.doi.org/10.3389/fmolb.2022.878133 Text en Copyright © 2022 Spiwok, Kurečka and Křenek. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Spiwok, Vojtěch Kurečka, Martin Křenek, Aleš Collective Variable for Metadynamics Derived From AlphaFold Output |
title | Collective Variable for Metadynamics Derived From AlphaFold Output |
title_full | Collective Variable for Metadynamics Derived From AlphaFold Output |
title_fullStr | Collective Variable for Metadynamics Derived From AlphaFold Output |
title_full_unstemmed | Collective Variable for Metadynamics Derived From AlphaFold Output |
title_short | Collective Variable for Metadynamics Derived From AlphaFold Output |
title_sort | collective variable for metadynamics derived from alphafold output |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9234394/ https://www.ncbi.nlm.nih.gov/pubmed/35769910 http://dx.doi.org/10.3389/fmolb.2022.878133 |
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