{"title":"用贝叶斯方法计算带循环的扰动图上的自由能","authors":"Xinqiang Ding, John Drohan","doi":"10.1021/acs.jctc.4c00948","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bayesian Approach for Computing Free Energy on Perturbation Graphs with Cycles.\",\"authors\":\"Xinqiang Ding, John Drohan\",\"doi\":\"10.1021/acs.jctc.4c00948\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":45,\"journal\":{\"name\":\"Journal of Chemical Theory and Computation\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2024-11-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Chemical Theory and Computation\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1021/acs.jctc.4c00948\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Theory and Computation","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.jctc.4c00948","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
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.
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.