Optimal use of data in parallel tempering simulations for the construction of discrete-state Markov models of biomolecular dynamics.
Optimal use of data in parallel tempering simulations for the construction of discrete-state Markov models of biomolecular dynamics.
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DOI：
10.1063/1.3592153
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发表时间：
2011-06
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通讯作者：
Jan-Hendrik Prinz;J. Chodera;V. Pande;William Swope;Jeremy C. Smith;F. Noé
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作者：
Jan-Hendrik Prinz;J. Chodera;V. Pande;William Swope;Jeremy C. Smith;F. Noé
平行回火（PT）分子动力学模拟作为生物分子系统结构的有效采样手段已被广泛研究。最近的工作证明了如何使用生物分子 PT 模拟中生成的短物理轨迹来构建描述每个模拟温度下生物分子动力学的马尔可夫模型。虽然这种方法描述了温度依赖性动力学，但它并没有充分利用所有可用的 PT 数据，而是仅使用给定温度下的数据来估计该温度下的速率。这可能是有问题的，因为一些相关的转变或状态可能无法在感兴趣的温度下充分采样，但可能在附近的温度下很容易采样。此外，温度相关特性的比较可能会受到错误假设的影响，即从不同温度收集的数据不相关。我们在这里提出了一种策略，通过对 PT 协议的简单修改，可以重新加权收获的轨迹，从而允许来自所有温度的数据有助于估计的动力学模型。该方法相对于单一温度方法减少了动力学模型中的统计不确定性，并且即使对于在感兴趣的温度下未观察到的转变也提供了转变概率的估计。此外，该方法允许在模拟运行温度以外的温度下估计动力学。我们通过将其应用于溶剂化末端封闭丙氨酸肽构象动力学的马尔可夫模型的生成来说明该方法。
Parallel tempering (PT) molecular dynamics simulations have been extensively investigated as a means of efficient sampling of the configurations of biomolecular systems. Recent work has demonstrated how the short physical trajectories generated in PT simulations of biomolecules can be used to construct the Markov models describing biomolecular dynamics at each simulated temperature. While this approach describes the temperature-dependent kinetics, it does not make optimal use of all available PT data, instead estimating the rates at a given temperature using only data from that temperature. This can be problematic, as some relevant transitions or states may not be sufficiently sampled at the temperature of interest, but might be readily sampled at nearby temperatures. Further, the comparison of temperature-dependent properties can suffer from the false assumption that data collected from different temperatures are uncorrelated. We propose here a strategy in which, by a simple modification of the PT protocol, the harvested trajectories can be reweighted, permitting data from all temperatures to contribute to the estimated kinetic model. The method reduces the statistical uncertainty in the kinetic model relative to the single temperature approach and provides estimates of transition probabilities even for transitions not observed at the temperature of interest. Further, the method allows the kinetics to be estimated at temperatures other than those at which simulations were run. We illustrate this method by applying it to the generation of a Markov model of the conformational dynamics of the solvated terminally blocked alanine peptide.