用贝叶斯方法计算带循环的扰动图上的自由能

IF 5.7 1区 化学 Q2 CHEMISTRY, PHYSICAL
Xinqiang Ding, John Drohan
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引用次数: 0

摘要

计算多个状态之间自由能差的常用方法是建立一个连接各状态的扰动图,并计算该图所有边上的自由能差。这种扰动图通常设计成具有循环。由于自由能是状态的函数,因此任何循环周围的自由能都为零,我们称之为循环一致性条件。由于循环一致性条件与循环边缘的自由能差异有关,因此可用于提高自由能估算的准确性。在此,我们提出了一种称为耦合贝叶斯多态贝内特接受率(CBayesMBAR)的贝叶斯方法,它能以一种原则性的方式适当地耦合计算循环边缘的自由能差异。我们应用 CBayesMBAR 计算谐波振荡器之间的自由能差和相对蛋白质配体结合自由能。在这两种情况下,与不考虑循环一致性条件的方法相比,CBayesMBAR 都能提供更精确的结果。此外,它还优于同样使用周期一致性条件的周期闭合校正方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian Approach for Computing Free Energy on Perturbation Graphs with Cycles.

A common approach for computing free energy differences among multiple states is to build a perturbation graph connecting the states and compute free energy differences on all edges of the graph. Such perturbation graphs are often designed to have cycles. Because free energy is a function of states, the free energy around any cycle is zero, which we refer to as the cycle consistency condition. Since the cycle consistency condition relates free energy differences on the edges of a cycle, it could be used to improve the accuracy of free energy estimates. Here, we propose a Bayesian method called the coupled Bayesian multistate Bennett acceptance ratio (CBayesMBAR) that can properly couple the calculations of free energy differences on the edges of cycles in a principled way. We apply the CBayesMBAR to compute free energy differences among harmonic oscillators and relative protein-ligand binding free energies. In both cases, the CBayesMBAR provides more accurate results compared to methods that do not consider the cycle consistency condition. Additionally, it outperforms the cycle closure correction method that also uses cycle consistency conditions.

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来源期刊
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation 化学-物理:原子、分子和化学物理
CiteScore
9.90
自引率
16.40%
发文量
568
审稿时长
1 months
期刊介绍: The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.
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