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）研究要求具有模拟复杂生物化学系统的能力，该系统具有越来越多的粒子且具有更长的时间尺度，这是仅凭计算硬件无法解决的问题。一个主要问题源于大多数系统潜在的自由能态的内在高维和复杂性，以及从有必要对此类态势进行准确采样以识别构型空间中的动力学和热力学状态，以及对两种自由度进行精确计算的源泉能量差和状态之间的相应转换率。在这里，我们回顾并介绍了两种日益流行的方法的应用，这些方法允许对生物分子系统进行长期MD模拟，从而可以开展广泛的新研究。第一种方法 马尔可夫状态模型（MSM）依赖于确定一组配置状态，系统在其中驻留足够长的时间以放松和释放先前转换的记忆，并依赖于使用仿真来绘制基础复杂的能源格局并提取准确的热力学和动力学信息。潜在转移概率的马尔科夫独立性为通过使用适当初始化的短模拟集而不是通常较长的MD轨迹集提供了提高采样效率的机会，这也提高了采样率。这允许基于MSM的研究以系统化和相对自动化的方式揭示生物分子机制并以高精度估算自由能垒，这说明了它们的日益普及。提出了第二种方法，Milestoning旨在以类似的系统，准确和高度自动化的方式，精确地研究连接特定最终状态（例如反应物和产物）的途径的整体。提出的应用范围包括构象动力学和淀粉样蛋白形成肽，细胞穿透肽的结合以及Abl激酶中DFG翻转动力学的研究。正如使用这两种方法的越来越多的研究所突显的那样，我们预计它们将为研究系统途径的反应途径和机理的系统取样提供新的途径，而这些反应途径和机理的发生时间比纯计算硬件开发所能达到的更长。提出的应用范围包括构象动力学和淀粉样蛋白形成肽，细胞穿透肽的结合以及Abl激酶中DFG翻转动力学的研究。正如使用这两种方法的越来越多的研究所突显的那样，我们预计它们将为调查系统中比纯计算硬件开发目前可​​访问的更长的时间范围内发生的反应途径和机制的系统取样提供新的途径。提出的应用范围包括构象动力学和淀粉样蛋白形成肽，细胞穿透肽的结合以及Abl激酶中DFG翻转动力学的研究。正如使用这两种方法的越来越多的研究所突显的那样，我们预计它们将为调查系统中比纯计算硬件开发目前可​​访问的更长的时间范围内发生的反应途径和机制的系统取样提供新的途径。