A generalized-Yvon-Born-Green method for coarse-grained modeling
A generalized-Yvon-Born-Green method for coarse-grained modeling
复制标题
粗粒度建模的广义 Yvon-Born-Green 方法
DOI：
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
W. Noid
中科院分区：
文献类型：
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作者：
J. F. Rudzinski;W. Noid
Yvon-Born-Green （YBG）积分方程是液态理论的一个基本结果，它将简单流体的对势与由此产生的平衡二体和三体相关函数联系起来。最近，我们导出了一种更一般的形式，可以应用于复杂的分子系统。这种广义ybg （g-YBG）理论不仅提供了给定势与由此产生的平衡相关函数之间的精确关系，而且为直接解决由平衡结构系综确定势的统计力学逆问题提供了一个非常强大的框架。在粗粒度（CG）建模的背景下，g-YBG理论直接（即，非迭代地）从结构相关函数确定了对平均力的多体势的变优逼近，特别是允许没有力的“力匹配”。虽然我们最初的努力是用相对简单的系统在数值上验证g-YBG理论，但我们最近的努力已经考虑了越来越复杂的系统，如肽和聚合物。这篇小型综述总结了这一进展和由此产生的见解，并讨论了g-YBG理论的突出挑战和未来方向。
The Yvon-Born-Green (YBG) integral equation is a basic result of liquid state theory that relates the pair potential of a simple fluid to the resulting equilibrium two- and three-body correlation functions. Quite recently, we derived a more general form that can be applied to complex molecular systems. This generalized-YBG (g-YBG) theory provides not only an exact relation between a given potential and the resulting equilibrium correlation functions, but also a remarkably powerful framework for directly solving the statistical mechanics inverse problem of determining potentials from equilibrium structure ensembles. In the context of coarse-grained (CG) modeling, the g-YBG theory determines a variationally optimal approximation to the many-body potential of mean force directly (i.e., noniteratively) from structural correlation functions and, in particular, allows “force-matching” without forces. While our initial efforts numerically validated the g-YBG theory with relatively simple systems, our more recent efforts have considered increasingly complex systems, such as peptides and polymers. This minireview summarizes this progress and the resulting insight, as well as discusses the outstanding challenges and future directions for the g-YBG theory.
DOI：
10.1093/protein/gzp028
发表时间：
2009-09
期刊：
Protein engineering, design & selection : PEDS
影响因子：
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作者：
Emil Sobolewski;M. Makowski;S. Ołdziej;C. Czaplewski;A. Liwo;H. Scheraga
通讯作者：
Emil Sobolewski;M. Makowski;S. Ołdziej;C. Czaplewski;A. Liwo;H. Scheraga
影响因子：
12.4
作者：
Li Z;Yang Y;Zhan J;Dai L;Zhou Y
通讯作者：
Zhou Y
影响因子：
3.3
作者：
Liwo, Adam;Khalili, Mey;Scheraga, Harold A.
通讯作者：
Scheraga, Harold A.