Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA.
J Phys Chem B. 2010 May 6;114(17):5870-7. doi: 10.1021/jp910112d.
We applied our previously developed library-based Monte Carlo (LBMC) to equilibrium sampling of several implicitly solvated all-atom peptides. LBMC can perform equilibrium sampling of molecules using precalculated statistical libraries of molecular-fragment configurations and energies. For this study, we employed residue-based fragments distributed according to the Boltzmann factor of the optimized potential for liquid simulations all-atom (OPLS-AA) forcefield describing the individual fragments. Two solvent models were employed: a simple uniform dielectric and the generalized Born/surface area (GBSA) model. The efficiency of LBMC was compared to standard Langevin dynamics (LD) using three different statistical tools. The statistical analyses indicate that LBMC is more than 100 times faster than LD not only for the simple solvent model but also for GBSA.
我们应用了先前开发的基于库的蒙特卡罗（LBMC）方法对几个隐式溶剂化的全原子肽进行平衡采样。LBMC 可以使用预先计算的分子片段构象和能量统计库对分子进行平衡采样。在这项研究中，我们根据优化的液体模拟势能（OPLS-AA）力场描述的各个片段的玻尔兹曼因子分布使用基于残基的片段。我们使用了两种溶剂模型：简单的均匀介电模型和广义 Born/表面积（GBSA）模型。使用三种不同的统计工具比较了 LBMC 和标准朗之万动力学（LD）的效率。统计分析表明，LBMC 不仅对于简单的溶剂模型，而且对于 GBSA 模型，其速度比 LD 快 100 多倍。
