Acceleration of Molecular Simulations by Parametric Time-Lagged
tSNE Metadynamics

J 2024

Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics

HRADISKÁ, Helena; Martin KUREČKA; Jan BERÁNEK; Guglielmo TEDESCHI; Vladimír VIŠŇOVSKÝ et al.

Základní údaje

Originální název

Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics

Autoři

HRADISKÁ, Helena; Martin KUREČKA; Jan BERÁNEK; Guglielmo TEDESCHI; Vladimír VIŠŇOVSKÝ; Aleš KŘENEK a Vojtěch SPIWOK

Vydání

The Journal of Physical Chemistry B, 2024, 1520-6106

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10403 Physical chemistry

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 2.900

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14610/24:00135420

Organizační jednotka

Ústav výpočetní techniky

Klíčová slova anglicky

REACTION COORDINATEDYNAMICSKINETICS

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 4. 4. 2025 13:08, Mgr. Eva Špillingová

Anotace

V originále

The potential of molecular simulations is limited by their computational costs. There is often a need to accelerate simulations using some of the enhanced sampling methods. Metadynamics applies a history-dependent bias potential that disfavors previously visited states. To apply metadynamics, it is necessary to select a few properties of the system─collective variables (CVs) that can be used to define the bias potential. Over the past few years, there have been emerging opportunities for machine learning and, in particular, artificial neural networks within this domain. In this broad context, a specific unsupervised machine learning method was utilized, namely, parametric time-lagged t-distributed stochastic neighbor embedding (ptltSNE) to design CVs. The approach was tested on a Trp-cage trajectory (tryptophan cage) from the literature. The trajectory was used to generate a map of conformations, distinguish fast conformational changes from slow ones, and design CVs. Then, metadynamic simulations were performed. To accelerate the formation of the α-helix, we added the α-RMSD collective variable. This simulation led to one folding event in a 350 ns metadynamics simulation. To accelerate degrees of freedom not addressed by CVs, we performed parallel tempering metadynamics. This simulation led to 10 folding events in a 200 ns simulation with 32 replicas.

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

90131, velká výzkumná infrastruktura

Název: ELIXIR-CZ II

90140, velká výzkumná infrastruktura

Název: e-INFRA CZ

Citovat

HRADISKÁ, Helena; Martin KUREČKA; Jan BERÁNEK; Guglielmo TEDESCHI; Vladimír VIŠŇOVSKÝ; Aleš KŘENEK a Vojtěch SPIWOK. Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics. The Journal of Physical Chemistry B. 2024, roč. 128, č. 4, s. 903-913. ISSN 1520-6106. Dostupné z: https://doi.org/10.1021/acs.jpcb.3c05669.

@article{2367761,
   author = {Hradiská, Helena and Kurečka, Martin and Beránek, Jan and Tedeschi, Guglielmo and Višňovský, Vladimír and Křenek, Aleš and Spiwok, Vojtěch},
   article_number = {4},
   doi = {https://doi.org/10.1021/acs.jpcb.3c05669},
   keywords = {REACTION COORDINATEDYNAMICSKINETICS},
   language = {eng},
   issn = {1520-6106},
   journal = {The Journal of Physical Chemistry B},
   title = {Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics},
   url = {https://pubs.acs.org/doi/full/10.1021/acs.jpcb.3c05669},
   volume = {128},
   year = {2024}
}

TY  - JOUR
ID  - 2367761
AU  - Hradiská, Helena - Kurečka, Martin - Beránek, Jan - Tedeschi, Guglielmo - Višňovský, Vladimír - Křenek, Aleš - Spiwok, Vojtěch
PY  - 2024
TI  - Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics
JF  - The Journal of Physical Chemistry B
VL  - 128
IS  - 4
SP  - 903-913
EP  - 903-913
SN  - 15206106
KW  - REACTION COORDINATEDYNAMICSKINETICS
UR  - https://pubs.acs.org/doi/full/10.1021/acs.jpcb.3c05669
N2  - The potential of molecular simulations is limited by their computational costs. There is often a need to accelerate simulations using some of the enhanced sampling methods. Metadynamics applies a history-dependent bias potential that disfavors previously visited states. To apply metadynamics, it is necessary to select a few properties of the system─collective variables (CVs) that can be used to define the bias potential. Over the past few years, there have been emerging opportunities for machine learning and, in particular, artificial neural networks within this domain. In this broad context, a specific unsupervised machine learning method was utilized, namely, parametric time-lagged t-distributed stochastic neighbor embedding (ptltSNE) to design CVs. The approach was tested on a Trp-cage trajectory (tryptophan cage) from the literature. The trajectory was used to generate a map of conformations, distinguish fast conformational changes from slow ones, and design CVs. Then, metadynamic simulations were performed. To accelerate the formation of the α-helix, we added the α-RMSD collective variable. This simulation led to one folding event in a 350 ns metadynamics simulation. To accelerate degrees of freedom not addressed by CVs, we performed parallel tempering metadynamics. This simulation led to 10 folding events in a 200 ns simulation with 32 replicas.
ER  -

HRADISKÁ, Helena; Martin KUREČKA; Jan BERÁNEK; Guglielmo TEDESCHI; Vladimír VIŠŇOVSKÝ; Aleš KŘENEK a Vojtěch SPIWOK. Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics. \textit{The Journal of Physical Chemistry B}. 2024, roč.~128, č.~4, s.~903-913. ISSN~1520-6106. Dostupné z: https://doi.org/10.1021/acs.jpcb.3c05669.

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