Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States.
J Chem Theory Comput. 2022 Aug 9;18(8):5025-5045. doi: 10.1021/acs.jctc.2c00168. Epub 2022 Jul 22.
The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.
多尺度建模方法的吸引力在于组合协同作用的承诺。然而，只有当不同的尺度与相互一致相结合时，这种承诺才能实现。在这里，我们考虑了多尺度分子动力学（MD）模拟，该模拟将固定电荷全原子（AA）表示法提供的准确性和大分子灵活性与可及的简化、粗粒化（CG）表示法的采样速度相结合。AA 到 CG 的转换相对简单，因为可以实现具有独特结果的确定性例程。相反，由于自由度数量的激增，CG 到 AA 的转换有许多解决方案。虽然存在用于生物分子 CG 到 AA 转换的自动化工具，但我们发现一种流行的选择，称为 Backward，容易出现随机故障，并且它生成的 AA 模型经常会损害蛋白质结构并导致不正确的立体化学。尽管在孤立的情况下，这些缺点可能可以通过人工干预来避免，但自动化的多尺度耦合需要可靠和强大的尺度转换。在这里，我们详细介绍了对多尺度机器学习建模基础设施（MuMMI）的扩展，包括一个名为 sinceCG 的改进 CG 到 AA 转换工具。该工具可靠（弱相关重复成功率约为 98%）、可自动化（没有无法恢复的挂起），生成的 AA 模型通常保留蛋白质二级结构并保持正确的立体化学。我们描述了 MuMMI 框架如何识别感兴趣的 CG 系统配置，将它们转换为 AA 表示，然后在 AA 尺度上模拟它们，同时实时分析提供反馈以更新 CG 参数。在 MuMMI 的上下文中，讨论了在包含外周膜蛋白 RAS 和 RAF 激酶近端组件的复杂八组分脂质双层中含有约 150 万个原子的系统中的应用。
