Collective Variable for Metadynamics Derived From AlphaFold
Output

J 2022

Collective Variable for Metadynamics Derived From AlphaFold Output

SPIWOK, Vojtěch, Martin KUREČKA and Aleš KŘENEK

Basic information

Original name

Collective Variable for Metadynamics Derived From AlphaFold Output

Authors

SPIWOK, Vojtěch (203 Czech Republic), Martin KUREČKA (203 Czech Republic, belonging to the institution) and Aleš KŘENEK (203 Czech Republic, belonging to the institution)

Edition

FRONTIERS IN MOLECULAR BIOSCIENCES, SWITZERLAND, FRONTIERS MEDIA SA, 2022, 2296-889X

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 5.000

RIV identification code

RIV/00216224:14610/22:00129134

Organization unit

Institute of Computer Science

DOI

http://dx.doi.org/10.3389/fmolb.2022.878133

UT WoS

000816408600001

Keywords in English

protein folding; alphafold; collective variable

Abstract

V originále

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.

Links

GA22-29667S, research and development project

Name: Analýza a vzorkování molekulárních simulací pomocí kontradiktorních autokodérů

Investor: Czech Science Foundation

Citovat

SPIWOK, Vojtěch, Martin KUREČKA and Aleš KŘENEK. Collective Variable for Metadynamics Derived From AlphaFold Output. 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.

@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.

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