School of Physics, University College Dublin, Dublin, Ireland; Institute for Discovery, University College Dublin, Dublin, Ireland.
Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, United States.
Prog Mol Biol Transl Sci. 2020;170:215-237. doi: 10.1016/bs.pmbts.2020.01.002. Epub 2020 Feb 5.
Molecular dynamics (MD) studies of biomolecules require the ability to simulate complex biochemical systems with an increasingly larger number of particles and for longer time scales, a problem that cannot be overcome by computational hardware advances alone. A main problem springs from the intrinsically high-dimensional and complex nature of the underlying free energy landscape of most systems, and from the necessity to sample accurately such landscapes for identifying kinetic and thermodynamic states in the configurations space, and for accurate calculations of both free energy differences and of the corresponding transition rates between states. Here, we review and present applications of two increasingly popular methods that allow long-time MD simulations of biomolecular systems that can open a broad spectrum of new studies. A first approach, Markov State Models (MSMs), relies on identifying a set of configuration states in which the system resides sufficiently long to relax and loose the memory of previous transitions, and on using simulations for mapping the underlying complex energy landscape and for extracting accurate thermodynamic and kinetic information. The Markovian independence of the underlying transition probabilities creates the opportunity to increase the sampling efficiency by using sets of appropriately initialized short simulations rather than typically long MD trajectories, which also enhances sampling. This allows MSM-based studies to unveil bio-molecular mechanisms and to estimate free energy barriers with high accuracy, in a manner that is both systematic and relatively automatic, which accounts for their increasing popularity. The second approach presented, Milestoning, targets accurate studies of the ensemble of pathways connecting specific end-states (e.g., reactants and products) in a similarly systematic, accurate and highly automatic manner. Applications presented range from studies of conformational dynamics and binding of amyloid-forming peptides, cell-penetrating peptides and the DFG-flip dynamics in Abl kinase. As highlighted by the increasing number of studies using both methods, we anticipate that they will open new avenues for the investigation of systematic sampling of reactions pathways and mechanisms occurring on longer time scales than currently accessible by purely computational hardware developments.
生物分子的分子动力学 (MD) 研究需要能够模拟具有越来越多粒子和更长时间尺度的复杂生化系统，仅靠计算硬件的进步是无法解决这个问题的。一个主要问题源于大多数系统的潜在自由能景观本质上的高维性和复杂性，以及需要准确地对这些景观进行采样，以识别构型空间中的动力学和热力学状态，并准确计算自由能差和状态之间的相应跃迁率。在这里，我们回顾并介绍了两种越来越流行的方法的应用，这两种方法允许对生物分子系统进行长时间的 MD 模拟，从而开辟了广泛的新研究领域。第一种方法是马尔可夫状态模型 (MSM)，它依赖于识别一组系统足够长时间驻留以放松并失去对先前跃迁的记忆的构型状态，并通过模拟来绘制潜在的复杂能量景观，并提取准确的热力学和动力学信息。基础跃迁概率的马尔可夫独立性创造了通过使用适当初始化的短模拟集而不是典型的长 MD 轨迹来提高采样效率的机会，这也增强了采样。这使得基于 MSM 的研究能够以系统的和相对自动的方式揭示生物分子机制并以高精度估计自由能势垒，这是它们越来越受欢迎的原因。呈现的第二种方法是里程碑，它以类似的系统、准确和高度自动的方式靶向准确研究连接特定末端状态（例如反应物和产物）的途径集合。呈现的应用范围从构象动力学和淀粉样肽、穿膜肽和 Abl 激酶的 DFG-flip 动力学的结合研究。正如越来越多使用这两种方法的研究所强调的那样，我们预计它们将为研究在比当前纯计算硬件发展更可访问的时间尺度上发生的反应途径和机制的系统采样开辟新途径。
