Enhanced conformational sampling in Monte Carlo simulations of proteins: application to a constrained peptide.

A Monte Carlo simulation method for globular proteins, called extended-scaled-collective-variable (ESCV) Monte Carlo, is proposed. This method combines two Monte Carlo algorithms known as entropy-sampling and scaled-collective-variable algorithms. Entropy-sampling Monte Carlo is able to sample a large configurational space even in a disordered system that has a large number of potential barriers. In contrast, scaled-collective-variable Monte Carlo provides an efficient sampling for a system whose dynamics is highly cooperative. Because a globular protein is a disordered system whose dynamics is characterized by collective motions, a combination of these two algorithms could provide an optimal Monte Carlo simulation for a globular protein. As a test case, we have carried out an ESCV Monte Carlo simulation for a cell adhesive Arg-Gly-Asp-containing peptide, Lys-Arg-Cys-Arg-Gly-Asp-Cys-Met-Asp, and determined the conformational distribution at 300 K. The peptide contains a disulfide bridge between the two cysteine residues. This bond mimics the strong geometrical constraints that result from a protein's globular nature and give rise to highly cooperative dynamics. Computation results show that the ESCV Monte Carlo was not trapped at any local minimum and that the canonical distribution was correctly determined.

本研究提出一种针对球状蛋白质（globular proteins）的蒙特卡洛（Monte Carlo）模拟方法，即扩展尺度集体变量（extended-scaled-collective-variable，ESCV）蒙特卡洛方法。该方法结合了两种被称为熵采样（entropy-sampling）与尺度集体变量（scaled-collective-variable）的蒙特卡洛算法。熵采样蒙特卡洛即便在存在大量势垒的无序体系中，也可对广阔的构象空间进行采样。与之相对，尺度集体变量蒙特卡洛则能为动力学具有高度协同性的体系提供高效采样。由于球状蛋白质属于以集体运动为动力学特征的无序体系，因此将这两种算法结合，可为球状蛋白质提供最优的蒙特卡洛模拟方案。作为测试案例，我们针对一段含精氨酸-甘氨酸-天冬氨酸（Arg-Gly-Asp）基的细胞黏附性肽——赖氨酸-精氨酸-半胱氨酸-精氨酸-甘氨酸-天冬氨酸-半胱氨酸-甲硫氨酸-天冬氨酸（Lys-Arg-Cys-Arg-Gly-Asp-Cys-Met-Asp）——开展了ESCV蒙特卡洛模拟，并确定了其在300 K下的构象分布。该肽的两个半胱氨酸残基之间形成了二硫键，此键模拟了由蛋白质球状结构所带来的强几何约束，进而催生了高度协同的动力学行为。计算结果表明，ESCV蒙特卡洛方法未陷入任何局部极小值，且可正确确定体系的正则（canonical）分布。