Department of Chemistry, Stanford University, United States.
Curr Opin Struct Biol. 2013 Feb;23(1):58-65. doi: 10.1016/j.sbi.2012.11.002. Epub 2012 Dec 10.
Quantitatively accurate all-atom molecular dynamics (MD) simulations of protein folding have long been considered a holy grail of computational biology. Due to the large system sizes and long timescales involved, such a pursuit was for many years computationally intractable. Further, sufficiently accurate forcefields needed to be developed in order to realistically model folding. This decade, however, saw the first reports of folding simulations describing kinetics on the order of milliseconds, placing many proteins firmly within reach of these methods. Progress in sampling and forcefield accuracy, however, presents a new challenge: how to turn huge MD datasets into scientific understanding. Here, we review recent progress in MD simulation techniques and show how the vast datasets generated by such techniques present new challenges for analysis. We critically discuss the state of the art, including reaction coordinate and Markov state model (MSM) methods, and provide a perspective for the future.
长期以来,对蛋白质折叠进行定量准确的全原子分子动力学(MD)模拟一直被认为是计算生物学的圣杯。由于涉及的系统尺寸大和时间尺度长,多年来这种追求在计算上是不可行的。此外,需要开发足够准确的力场才能真实地模拟折叠。然而,这十年见证了首次报道的折叠模拟描述了毫秒级的动力学,使许多蛋白质都在这些方法的范围内。然而,在采样和力场准确性方面的进展提出了一个新的挑战:如何将庞大的 MD 数据集转化为科学理解。在这里,我们回顾了 MD 模拟技术的最新进展,并展示了这些技术产生的大量数据集如何为分析带来新的挑战。我们批判性地讨论了包括反应坐标和马尔可夫状态模型(MSM)方法在内的最新技术,并为未来提供了一个视角。