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@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 a Aleš KŘENEK. Collective Variable for Metadynamics Derived From AlphaFold Output. \textit{FRONTIERS IN MOLECULAR BIOSCIENCES}. SWITZERLAND: FRONTIERS MEDIA SA, 2022, roč.~2022, č.~9, s.~1-10. ISSN~2296-889X. Dostupné z: https://dx.doi.org/10.3389/fmolb.2022.878133.
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