Automated Training of ReaxFF Reactive Force Fields for
Energetics of Enzymatic Reactions

J 2018

Automated Training of ReaxFF Reactive Force Fields for Energetics of Enzymatic Reactions

TRNKA, Tomáš, Igor TVAROŠKA and Jaroslav KOČA

Basic information

Original name

Automated Training of ReaxFF Reactive Force Fields for Energetics of Enzymatic Reactions

Authors

TRNKA, Tomáš (203 Czech Republic, belonging to the institution), Igor TVAROŠKA (703 Slovakia, belonging to the institution) and Jaroslav KOČA (203 Czech Republic, guarantor, belonging to the institution)

Edition

Journal of Chemical Theory and Computation, Washington DC, American Chemical Society, 2018, 1549-9618

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10610 Biophysics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 5.313

RIV identification code

RIV/00216224:14740/18:00102246

Organization unit

Central European Institute of Technology

DOI

http://dx.doi.org/10.1021/acs.jctc.7b00870

UT WoS

000419998300026

Keywords (in Czech)

reakční mechanismus; enzymatická reakce

Keywords in English

reaction mechanism; enzymatic reaction

Abstract

V originále

Computational studies of the reaction mechanisms of various enzymes are nowadays based almost exclusively on hybrid QM/MM models. Unfortunately, the success of this approach strongly depends on the selection of the QM region, and computational cost is a crucial limiting factor. An interesting alternative is offered by empirical reactive molecular force fields, especially the ReaxFF potential developed by van Duin and co-workers. However, even though an initial parametrization of ReaxFF for biomolecules already exists, it does not provide the desired level of accuracy. We have conducted a thorough refitting of the ReaxFF force field to improve the description of reaction energetics. To minimize the human effort required, we propose a fully automated approach to generate an extensive training set comprised of thousands of different geometries and molecular fragments starting from a few model molecules. Electrostatic parameters were optimized with QM electrostatic potentials as the main target quantity, avoiding excessive dependence on the choice of reference atomic charges and improving robustness and transferability. The remaining force field parameters were optimized using the VD-CMA-ES variant of the CMA-ES optimization algorithm. This method is able to optimize hundreds of parameters simultaneously with unprecedented speed and reliability. The resulting force field was validated on a real enzymatic system, ppGalNAcT2 glycosyltransferase. The new force field offers excellent qualitative agreement with the reference QM/MM reaction energy profile, matches the relative energies of intermediate and product minima almost exactly, and reduces the overestimation of transition state energies by 27-48% compared with the previous parametrization.

Links

LM2015085, research and development project

Name: CERIT Scientific Cloud (Acronym: CERIT-SC)

Investor: Ministry of Education, Youth and Sports of the CR, CERIT Scientific Cloud

LQ1601, research and development project

Name: CEITEC 2020 (Acronym: CEITEC2020)

Investor: Ministry of Education, Youth and Sports of the CR

LTC17076, research and development project

Name: Interdisciplinární přístup ke studiu biologických systémů na molekulární úrovni

Investor: Ministry of Education, Youth and Sports of the CR, Interdisciplinary approach to study biological systems at the molecular level, INTER-COST

Citovat

TRNKA, Tomáš, Igor TVAROŠKA and Jaroslav KOČA. Automated Training of ReaxFF Reactive Force Fields for Energetics of Enzymatic Reactions. Journal of Chemical Theory and Computation. Washington DC: American Chemical Society, 2018, vol. 14, No 1, p. 291-302. ISSN 1549-9618. Available from: https://dx.doi.org/10.1021/acs.jctc.7b00870.

@article{1405845,
   author = {Trnka, Tomáš and Tvaroška, Igor and Koča, Jaroslav},
   article_location = {Washington DC},
   article_number = {1},
   doi = {http://dx.doi.org/10.1021/acs.jctc.7b00870},
   keywords = {reaction mechanism; enzymatic reaction},
   language = {eng},
   issn = {1549-9618},
   journal = {Journal of Chemical Theory and Computation},
   title = {Automated Training of ReaxFF Reactive Force Fields for Energetics of Enzymatic Reactions},
   url = {https://pubs.acs.org/doi/10.1021/acs.jctc.7b00870},
   volume = {14},
   year = {2018}
}

TY  - JOUR
ID  - 1405845
AU  - Trnka, Tomáš - Tvaroška, Igor - Koča, Jaroslav
PY  - 2018
TI  - Automated Training of ReaxFF Reactive Force Fields for Energetics of Enzymatic Reactions
JF  - Journal of Chemical Theory and Computation
VL  - 14
IS  - 1
SP  - 291-302
EP  - 291-302
PB  - American Chemical Society
SN  - 15499618
KW  - reaction mechanism
KW  - enzymatic reaction
UR  - https://pubs.acs.org/doi/10.1021/acs.jctc.7b00870
N2  - Computational studies of the reaction mechanisms of various enzymes are nowadays based almost exclusively on hybrid QM/MM models. Unfortunately, the success of this approach strongly depends on the selection of the QM region, and computational cost is a crucial limiting factor. An interesting alternative is offered by empirical reactive molecular force fields, especially the ReaxFF potential developed by van Duin and co-workers. However, even though an initial parametrization of ReaxFF for biomolecules already exists, it does not provide the desired level of accuracy. We have conducted a thorough refitting of the ReaxFF force field to improve the description of reaction energetics. To minimize the human effort required, we propose a fully automated approach to generate an extensive training set comprised of thousands of different geometries and molecular fragments starting from a few model molecules. Electrostatic parameters were optimized with QM electrostatic potentials as the main target quantity, avoiding excessive dependence on the choice of reference atomic charges and improving robustness and transferability. The remaining force field parameters were optimized using the VD-CMA-ES variant of the CMA-ES optimization algorithm. This method is able to optimize hundreds of parameters simultaneously with unprecedented speed and reliability. The resulting force field was validated on a real enzymatic system, ppGalNAcT2 glycosyltransferase. The new force field offers excellent qualitative agreement with the reference QM/MM reaction energy profile, matches the relative energies of intermediate and product minima almost exactly, and reduces the overestimation of transition state energies by 27-48% compared with the previous parametrization.
ER  -

TRNKA, Tomáš, Igor TVAROŠKA and Jaroslav KOČA. Automated Training of ReaxFF Reactive Force Fields for Energetics of Enzymatic Reactions. \textit{Journal of Chemical Theory and Computation}. Washington DC: American Chemical Society, 2018, vol.~14, No~1, p.~291-302. ISSN~1549-9618. Available from: https://dx.doi.org/10.1021/acs.jctc.7b00870.

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