Swiss Federal Research Institute WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland.
J Chem Phys. 2012 Aug 14;137(6):064903. doi: 10.1063/1.4742969.
Coarse-grained (lattice-) models have a long tradition in aiding efforts to decipher the physical or biological complexity of proteins. Despite the simplicity of these models, however, numerical simulations are often computationally very demanding and the quest for efficient algorithms is as old as the models themselves. Expanding on our previous work [T. Wüst and D. P. Landau, Phys. Rev. Lett. 102, 178101 (2009)], we present a complete picture of a Monte Carlo method based on Wang-Landau sampling in combination with efficient trial moves (pull, bond-rebridging, and pivot moves) which is particularly suited to the study of models such as the hydrophobic-polar (HP) lattice model of protein folding. With this generic and fully blind Monte Carlo procedure, all currently known putative ground states for the most difficult benchmark HP sequences could be found. For most sequences we could also determine the entire energy density of states and, together with suitably designed structural observables, explore the thermodynamics and intricate folding behavior in the virtually inaccessible low-temperature regime. We analyze the differences between random and protein-like heteropolymers for sequence lengths up to 500 residues. Our approach is powerful both in terms of robustness and speed, yet flexible and simple enough for the study of many related problems in protein folding.
粗粒化（格子）模型在帮助人们理解蛋白质的物理或生物学复杂性方面有着悠久的历史。然而，尽管这些模型很简单，但数值模拟通常计算量非常大，而对高效算法的探索与这些模型本身一样古老。在我们之前的工作 [T. Wüst 和 D. P. Landau, Phys. Rev. Lett. 102, 178101 (2009)] 的基础上，我们提出了一种基于 Wang-Landau 抽样的完整的蒙特卡罗方法，结合了有效的试探移动（拉伸、键重连和枢轴移动），特别适合于研究疏水-极性（HP）蛋白质折叠晶格模型等模型。使用这种通用的、完全盲目的蒙特卡罗程序，可以找到最困难的 HP 序列的所有当前已知的假定基态。对于大多数序列，我们还可以确定整个能量态密度，并与适当设计的结构观测值一起，探索在几乎无法进入的低温区的热力学和复杂折叠行为。我们分析了序列长度高达 500 个残基的随机和蛋白质样杂聚物之间的差异。我们的方法在稳健性和速度方面都很强大，但足够灵活和简单，可以用于研究蛋白质折叠中的许多相关问题。
