Choice of Adaptive Sampling Strategy Impacts State Discovery, Transition Probabilities, and the Apparent Mechanism of Conformational Changes.
Choice of Adaptive Sampling Strategy Impacts State Discovery, Transition Probabilities, and the Apparent Mechanism of Conformational Changes.
复制标题
DOI：
10.1021/acs.jctc.8b00500
复制
发表时间：
2018-11-13
影响因子：
5.5
通讯作者：
Bowman GR
中科院分区：
文献类型：
--
作者：
Zimmerman MI;Porter JR;Sun X;Silva RR;Bowman GR
随着计算硬件和马尔可夫状态建模（MSM）方法的最新进展，对原子详细模拟的兴趣显着增长，但仍然存在一些悬而未决的问题，阻碍了它们的广泛采用。也就是说，替代抽样策略如何探索构象空间，以及这如何影响从数据中生成的预测？在这里，我们试图回答这些问题的四个常用的采样方法：1）一个单一的长模拟，2）许多短模拟并行运行，3）自适应采样，和4）我们最近开发的目标导向的采样算法，FAST。我们首先开发了一个理论框架，分析计算发现选择状态的概率在简单的景观，在那里我们发现了不同的模拟的数量和长度的巨大影响。然后，我们使用动力学蒙特卡罗模拟各种物理启发的景观，以表征发现特定的状态和过渡路径的四种方法的概率。一致地，我们发现FAST模拟发现每个目标状态的概率最高，而遍历现实的路径。此外，我们发现了潜在的病理，短的并行模拟有时预测一个不正确的过渡途径，通过跨越大的能量障碍，长的模拟通常会绕行。我们将这种病理学称为“通道隧穿”。为了防止这种现象时，使用自适应采样和FAST模拟，我们介绍了快速字符串方法。该方法增强了沿最高通量跃迁路径的沿着采样，以细化MSM跃迁概率并区分竞争路径。此外，我们比较了各种MSM估计器在描述准确的热力学和动力学的性能。对于自适应采样，我们建议在添加小的伪计数后简单地将每个状态的转换计数归一化，以避免创建源或汇。最后，我们评估我们的见解是否从简单的景观持有的全原子分子动力学模拟的λ-阻遏蛋白的折叠。值得注意的是，我们发现，快速接触预测相同的折叠途径作为一组长的模拟，但数量级更少的模拟时间。
Interest in atomically-detailed simulations has grown significantly with recent advances in computational hardware and Markov state modeling (MSM) methods, yet outstanding questions remain that hinder their widespread adoption. Namely, how do alternative sampling strategies explore conformational space and how might this influence predictions generated from the data? Here, we seek to answer these questions for four commonly used sampling methods: 1) a single long simulation, 2) many short simulations run in parallel, 3) adaptive sampling, and 4) our recently developed goal-oriented sampling algorithm, FAST. We first develop a theoretical framework for analytically calculating the probability of discovering select states on simple landscapes, where we uncover the drastic effects of varying the number and length of simulations. We then use kinetic Monte Carlo simulations on a variety of physically inspired landscapes to characterize the probability of discovering particular states and transition pathways for each of the four methods. Consistently, we find that FAST simulations discover each target state with the highest probability, while traversing realistic pathways. Furthermore, we uncover the potential pathology that short parallel simulations sometimes predict an incorrect transition pathway by crossing large energy barriers that long simulations would typically circumnavigate. We refer to this pathology as “pathway tunneling”. To protect against this phenomenon when using adaptive-sampling and FAST simulations, we introduce the FAST-string method. This method enhances sampling along the highest-flux transition paths to refine an MSMs transition probabilities and discriminate between competing pathways. Additionally, we compare the performance of a variety of MSM estimators in describing accurate thermodynamics and kinetics. For adaptive sampling, we recommend simply normalizing the transition counts out of each state after adding small pseudo-counts to avoid creating sources or sinks. Lastly, we evaluate whether our insights from simple landscapes hold for all-atom molecular dynamics simulations of the folding of the λ-repressor protein. Remarkably, we find that FAST-contacts predicts the same folding pathway as a set of long simulations but with orders of magnitude less simulation time.
登录
查看更多内容
影响因子：
4.4
作者：
Fukunishi, H;Watanabe, O;Takada, S
通讯作者：
Takada, S
影响因子：
3
作者：
Duan, Y;Wu, C;Kollman, P
通讯作者：
Kollman, P
影响因子：
6.8
作者：
Chodera JD;Noé F
通讯作者：
Noé F
影响因子：
4.4
作者：
Bussi, Giovanni;Donadio, Davide;Parrinello, Michele
通讯作者：
Parrinello, Michele
影响因子：
4.4
作者：
Hamelberg, D;Mongan, J;McCammon, JA
通讯作者：
McCammon, JA