Other formats:
BibTeX
LaTeX
RIS
@article{2212609,
author = {Spiwok, Vojtěch and Kurečka, Martin and Křenek, Aleš},
article_location = {SWITZERLAND},
article_number = {9},
doi = {http://dx.doi.org/10.3389/fmolb.2022.878133},
keywords = {protein folding; alphafold; collective variable},
language = {eng},
issn = {2296-889X},
journal = {FRONTIERS IN MOLECULAR BIOSCIENCES},
title = {Collective Variable for Metadynamics Derived From AlphaFold Output},
url = {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},
volume = {2022},
year = {2022}
}
TY - JOUR
ID - 2212609
AU - Spiwok, Vojtěch - Kurečka, Martin - Křenek, Aleš
PY - 2022
TI - Collective Variable for Metadynamics Derived From AlphaFold Output
JF - FRONTIERS IN MOLECULAR BIOSCIENCES
VL - 2022
IS - 9
SP - 1-10
EP - 1-10
PB - FRONTIERS MEDIA SA
SN - 2296889X
KW - protein folding
KW - alphafold
KW - collective variable
UR - 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
N2 - 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.
ER -
SPIWOK, Vojtěch, Martin KUREČKA and Aleš KŘENEK. Collective Variable for Metadynamics Derived From AlphaFold Output. \textit{FRONTIERS IN MOLECULAR BIOSCIENCES}. SWITZERLAND: FRONTIERS MEDIA SA, 2022, vol.~2022, No~9, p.~1-10. ISSN~2296-889X. Available from: https://dx.doi.org/10.3389/fmolb.2022.878133.
|
