School of Chemistry, University of Southampton, Southapton, S017 1BJ, UK.
Department of Chemistry, University of California, Davis, California 95616, USA.
Phys Chem Chem Phys. 2021 Nov 10;23(43):24842-24851. doi: 10.1039/d0cp05041e.
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.
原子模型提供了对分子系统的详细表示,但有时对于长时间跨度的大系统模拟来说不够充分。粗粒模型通过减少自由度来实现加速模拟,但代价是降低了准确性。新的优化过程可以对这些模型进行参数化,从而提高它们的质量和适用范围。我们提出了一种自动化的方法来优化粗粒力场,通过复制来自原子分子模拟的自由能数据来实现。为了说明这种方法,我们将水合自由能梯度实现为 ForceBalance 中力场优化的新目标,并成功地将其应用于优化 SIRAH 蛋白质粗粒力场中的非带电侧链和蛋白质主链。优化后的参数紧密地再现了原子模型的水合自由能,并与实验结果更加吻合。