Collective Variable for Metadynamics Derived From AlphaFold
Output

J 2022

Collective Variable for Metadynamics Derived From AlphaFold Output

SPIWOK, Vojtěch, Martin KUREČKA a Aleš KŘENEK

Základní údaje

Originální název

Collective Variable for Metadynamics Derived From AlphaFold Output

Autoři

SPIWOK, Vojtěch (203 Česká republika), Martin KUREČKA (203 Česká republika, domácí) a Aleš KŘENEK (203 Česká republika, domácí)

Vydání

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

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 5.000

Kód RIV

RIV/00216224:14610/22:00129134

Organizační jednotka

Ústav výpočetní techniky

DOI

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

UT WoS

000816408600001

Klíčová slova anglicky

protein folding; alphafold; collective variable

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 14. 3. 2023 15:21, Mgr. Alena Mokrá

Anotace

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.

Návaznosti

GA22-29667S, projekt VaV

Název: Analýza a vzorkování molekulárních simulací pomocí kontradiktorních autokodérů

Investor: Grantová agentura ČR, Analýza a vzorkování molekulárních simulací pomocí kontradiktorních autokodérů

Citovat

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

@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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