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

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F18%3A00102246" target="_blank" >RIV/00216224:14740/18:00102246 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1021/acs.jctc.7b00870" target="_blank" >http://dx.doi.org/10.1021/acs.jctc.7b00870</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1021/acs.jctc.7b00870" target="_blank" >10.1021/acs.jctc.7b00870</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10610 - Biophysics

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Journal of Chemical Theory and Computation

  • ISSN

    1549-9618

  • e-ISSN

  • Volume of the periodical

    14

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    12

  • Pages from-to

    291-302

  • UT code for WoS article

    000419998300026

  • EID of the result in the Scopus database

    2-s2.0-85040347593