稀有事件动力学的有效估计
Efficient estimation of rare-event kinetics
摘要：
复杂动力学系统中稀有事件动力学的有效计算，如配体从蛋白质中解离的速率和途径，是一个普遍未解决的问题。马尔可夫状态模型可以系统地集成短模拟的集合，从而有效地并行计算工作，但感兴趣的罕见事件仍然需要在数据中自发采样。增强的采样方法，如平行回火或伞式采样，可以大大加速平衡期望的计算，但牺牲了计算动态期望的能力。在这项工作中，我们建立了一个原则，结合知识的平衡分布与动力学从快速“下坡”松弛轨迹使用可逆马尔可夫模型。这种方法是通用的，因为它不调用任何特定的动力学模型，并可以提供罕见事件动力学的准确估计。当过程的一个方向比其相反方向发生得更快时，可以实现采样效率的大幅提高，使得该方法对于生物分子中的折叠和结合等下坡过程特别有吸引力。
英文摘要：
The efficient calculation of rare-event kinetics in complex dynamical systems, such as the rate and pathways of ligand dissociation from a protein, is a generally unsolved problem. Markov state models can systematically integrate ensembles of short simulations and thus effectively parallelize the computational effort, but the rare events of interest still need to be spontaneously sampled in the data. Enhanced sampling approaches, such as parallel tempering or umbrella sampling, can accelerate the computation of equilibrium expectations massively - but sacrifice the ability to compute dynamical expectations. In this work we establish a principle to combine knowledge of the equilibrium distribution with kinetics from fast "downhill" relaxation trajectories using reversible Markov models. This approach is general as it does not invoke any specific dynamical model, and can provide accurate estimates of the rare event kinetics. Large gains in sampling efficiency can be achieved whenever one direction of the process occurs more rapid than its reverse, making the approach especially attractive for downhill processes such as folding and binding in biomolecules.
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