Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793263
Title: The automated optimisation of a coarse-grained protein force field using free energy data
Author: Caceres-Delpiano, Javier
ISNI:       0000 0004 8502 0405
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2019
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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.
Supervisor: Essex, Jonathan Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.793263  DOI: Not available
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