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

The automated optimisation of a coarse-grained protein force field using free energy data
The automated optimisation of a coarse-grained protein 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 optimisation processes to parameterise these models could improve their quality and range of applicability. We present an automated approach for the optimisation of the SIRAH coarse-grained protein force field. A full optimisation of the SIRAH water model was performed using ForceBalance, based on experimental water properties. We implemented hydration free energy gradients as a new target for force field optimisation and applied it successfully to optimise the uncharged side-chains and the protein backbone. We managed to closely reproduce hydration free energies of atomistic models and improve agreement with experiment. An attempt was made for the optimisation of charged coarse-grained protein side-chains. Hydration free energies were improved, but at the expense of an over-fitted model, which led to an over-estimation of protein interactions. Simulations of folded proteins in water result in improved protein stabilities for the new model. We compute the opening/closing event of a Glutamate receptor binding domain using umbrella sampling simulations, showing a clear improvement on the estimation of the PMF with previously reported studies on atomistic systems, for the ligand-free and glutamate-bound states.
University of Southampton
Caceres-Delpiano, Javier
67ee9f15-f508-4a8d-b45d-37a31189f630
Caceres-Delpiano, Javier
67ee9f15-f508-4a8d-b45d-37a31189f630
Essex, Jonathan
1f409cfe-6ba4-42e2-a0ab-a931826314b5

Caceres-Delpiano, Javier (2019) The automated optimisation of a coarse-grained protein force field using free energy data. University of Southampton, Doctoral Thesis, 231pp.

Record type: Thesis (Doctoral)

Abstract

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 the SIRAH coarse-grained protein force field. A full optimisation of the SIRAH water model was performed using ForceBalance, based on experimental water properties. We implemented hydration free energy gradients as a new target for force field optimisation and applied it successfully to optimise the uncharged side-chains and the protein backbone. We managed to closely reproduce hydration free energies of atomistic models and improve agreement with experiment. An attempt was made for the optimisation of charged coarse-grained protein side-chains. Hydration free energies were improved, but at the expense of an over-fitted model, which led to an over-estimation of protein interactions. Simulations of folded proteins in water result in improved protein stabilities for the new model. We compute the opening/closing event of a Glutamate receptor binding domain using umbrella sampling simulations, showing a clear improvement on the estimation of the PMF with previously reported studies on atomistic systems, for the ligand-free and glutamate-bound states.

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PhD Thesis J C DELPIANO - Version of Record
Available under License University of Southampton Thesis Licence.
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Published date: October 2019

Identifiers

Local EPrints ID: 435923
URI: http://eprints.soton.ac.uk/id/eprint/435923
PURE UUID: 72b88fba-d2fe-4470-a56f-3a3e947c85aa
ORCID for Jonathan Essex: ORCID iD orcid.org/0000-0003-2639-2746

Catalogue record

Date deposited: 22 Nov 2019 17:30
Last modified: 05 Jan 2021 05:01

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Contributors

Author: Javier Caceres-Delpiano
Thesis advisor: Jonathan Essex ORCID iD

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