Coarse-grained models have proven helpful for simulating complex systems over long timescales to provide molecular insights into various processes. Methodologies for systematic parameterization of the underlying energy function, or force field that describes the interactions among different components of the system are of great interest for ensuring simulation accuracy. We present a new method, potential contrasting, to enable efficient learning of force fields that can accurately reproduce the conformational distribution produced with all-atom simulations. Potential contrasting generalizes the noise contrastive estimation method with umbrella sampling to better learn the complex energy landscape of molecular systems. When applied to the Trp-cage protein, we found that the technique produces force fields that thoroughly capture the thermodynamics of the folding process despite the use of only $\alpha$-Carbons in the coarse-grained model. We further showed that potential contrasting could be applied over large datasets that combine the conformational ensembles of many proteins to ensure the transferability of coarse-grained force fields. We anticipate potential contrasting to be a powerful tool for building general-purpose coarse-grained force fields.
翻译:事实证明,粗糙的模型有助于模拟长时尺度的复杂系统,以便提供分子对各种过程的洞察力。 系统化参数化基本能源功能的方法,或描述系统不同组成部分之间相互作用的强制场,对于确保模拟准确性非常有益。 我们提出了一个新的方法,可能进行对比,以便能够有效地学习能够准确复制通过全原子模拟产生的相容分布的强力场。 有可能将噪声对比估计方法与伞式取样方法进行对比,以更好地了解分子系统的复杂能源景观。 在应用Trp-cage蛋白时,我们发现该技术产生力场,能够彻底捕捉折叠过程的热力学,尽管粗微粒模型中只使用了$\alpha$-carbons。我们进一步表明,对大数据集的潜在对比可以应用,将许多蛋白质的相容结合成一体的大型数据集,以确保粗重力场的可转移性。我们预计,该技术有可能成为建立通用焦粒力场的强大工具。