An alchemical enhanced sampling (ACES) method has recently been introduced to facilitate importance sampling in free energy simulations. The method achieves enhanced sampling from Hamiltonian replica exchange within a dual topology framework while utilizing new smoothstep softcore potentials. A common sampling problem encountered in lead optimization is the functionalization of aromatic rings that exhibit distinct conformational preferences when interacting with the protein. It is difficult to converge the distribution of ring conformations due to the long time scale of ring flipping events; however, the ACES method addresses this issue by modeling the syn and anti ring conformations within a dual topology. ACES thereby samples the conformer distributions by alchemically tunneling between states, as opposed to traversing a physical pathway with a high rotational barrier. We demonstrate the use of ACES to overcome conformational sampling issues involving ring flipping in ML300-derived noncovalent inhibitors of SARS-CoV-2 Main Protease (M^pro). The demonstrations explore how the use of replica exchange and the choice of softcore selection affects the convergence of the ring conformation distributions. Furthermore, we examine how the accuracy of the calculated free energies is affected by the degree of phase space overlap (PSO) between adjacent states (i.e., between neighboring λ-windows) and the Hamiltonian replica exchange acceptance ratios. Both of these factors are sensitive to the spacing between the intermediate states. We introduce a new method for choosing a schedule of λ values. The method analyzes short “burn-in” simulations to construct a 2D map of the nonlocal PSO. The schedule is obtained by optimizing an alchemical pathway on the 2D map that equalizes the PSO between the λ intervals. The optimized phase space overlap λ-spacing method (Opt-PSO) leads to more numerous end-to-end single passes and round trips due to the correlation between PSO and Hamiltonian replica exchange acceptance ratios. The improved exchange statistics enhance the efficiency of ACES method. The method has been implemented into the FE-ToolKit software package, which is freely available.

中文翻译：

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具有优化相空间重叠的 Alchemical 增强采样

最近引入了炼金增强采样（ACES）方法，以促进自由能模拟中的重要性采样。该方法在利用新的 smoothstep 软核势的同时，在双拓扑框架内实现了哈密顿副本交换的增强采样。先导化合物优化中遇到的一个常见采样问题是芳香环的功能化，当与蛋白质相互作用时，芳香环表现出不同的构象偏好。由于环翻转事件的时间尺度较长，环构象分布难以收敛；然而，ACES 方法通过对双拓扑中的顺环和反环构象进行建模来解决这个问题。因此，ACES 通过状态之间的炼金隧道对构象异构体分布进行采样，而不是穿过具有高旋转势垒的物理路径。我们演示了使用 ACES 来克服 SARS-CoV-2 主要蛋白酶 (M ^pro ) 的 ML300 衍生非共价抑制剂中涉及环翻转的构象采样问题。这些演示探讨了副本交换的使用和软核选择的选择如何影响环构象分布的收敛。此外，我们还研究了相邻状态之间（即相邻 λ 窗之间）的相空间重叠 (PSO) 程度和哈密顿副本交换接受率如何影响计算的自由能的准确性。这两个因素都对中间态之间的间距敏感。我们引入了一种选择 λ 值表的新方法。该方法分析简短的“老化”模拟，以构建非局部 PSO 的 2D 地图。该时间表是通过优化 2D 地图上的炼金路径来获得的，该路径使 λ 区间之间的 PSO 相等。由于 PSO 和哈密顿副本交换接受率之间的相关性，优化的相空间重叠 λ 间距方法 (Opt-PSO) 会导致更多的端到端单程和往返。改进的交换统计提高了ACES方法的效率。该方法已被实施到FE-ToolKit软件包中，该软件包可免费使用。