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The automated optimisation of a coarse-grained force field using free energy data

Atomistic models provide a detailed representation of molecular systems, but are sometimes inadequate for simulations of large systems over long timescales. Coarse-grained models enable accelerated simulations by reducing the number of degrees of freedom, at the cost of reduced accuracy. New optimis...

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Detalles Bibliográficos
Autores principales: Caceres-Delpiano, Javier, Wang, Lee-Ping, Essex, Jonathan W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8579472/
https://www.ncbi.nlm.nih.gov/pubmed/34723311
http://dx.doi.org/10.1039/d0cp05041e
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author Caceres-Delpiano, Javier
Wang, Lee-Ping
Essex, Jonathan W.
author_facet Caceres-Delpiano, Javier
Wang, Lee-Ping
Essex, Jonathan W.
author_sort Caceres-Delpiano, Javier
collection PubMed
description Atomistic models provide a detailed representation of molecular systems, but are sometimes inadequate for simulations of large systems over long timescales. Coarse-grained models enable accelerated simulations by reducing the number of degrees of freedom, at the cost of reduced accuracy. New optimisation processes to parameterise these models could improve their quality and range of applicability. We present an automated approach for the optimisation of coarse-grained force fields, by reproducing free energy data derived from atomistic molecular simulations. To illustrate the approach, we implemented hydration free energy gradients as a new target for force field optimisation in ForceBalance and applied it successfully to optimise the un-charged side-chains and the protein backbone in the SIRAH protein coarse-grain force field. The optimised parameters closely reproduced hydration free energies of atomistic models and gave improved agreement with experiment.
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spelling pubmed-85794722021-12-13 The automated optimisation of a coarse-grained force field using free energy data Caceres-Delpiano, Javier Wang, Lee-Ping Essex, Jonathan W. Phys Chem Chem Phys Chemistry Atomistic models provide a detailed representation of molecular systems, but are sometimes inadequate for simulations of large systems over long timescales. Coarse-grained models enable accelerated simulations by reducing the number of degrees of freedom, at the cost of reduced accuracy. New optimisation processes to parameterise these models could improve their quality and range of applicability. We present an automated approach for the optimisation of coarse-grained force fields, by reproducing free energy data derived from atomistic molecular simulations. To illustrate the approach, we implemented hydration free energy gradients as a new target for force field optimisation in ForceBalance and applied it successfully to optimise the un-charged side-chains and the protein backbone in the SIRAH protein coarse-grain force field. The optimised parameters closely reproduced hydration free energies of atomistic models and gave improved agreement with experiment. The Royal Society of Chemistry 2021-10-19 /pmc/articles/PMC8579472/ /pubmed/34723311 http://dx.doi.org/10.1039/d0cp05041e Text en This journal is © the Owner Societies https://creativecommons.org/licenses/by/3.0/
spellingShingle Chemistry
Caceres-Delpiano, Javier
Wang, Lee-Ping
Essex, Jonathan W.
The automated optimisation of a coarse-grained force field using free energy data
title The automated optimisation of a coarse-grained force field using free energy data
title_full The automated optimisation of a coarse-grained force field using free energy data
title_fullStr The automated optimisation of a coarse-grained force field using free energy data
title_full_unstemmed The automated optimisation of a coarse-grained force field using free energy data
title_short The automated optimisation of a coarse-grained force field using free energy data
title_sort automated optimisation of a coarse-grained force field using free energy data
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8579472/
https://www.ncbi.nlm.nih.gov/pubmed/34723311
http://dx.doi.org/10.1039/d0cp05041e
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