In the last decade, advances in molecular dynamics (MD) and Markov State Model (MSM) methodologies have made possible accurate and efficient estimation of kinetic rates and reactive pathways for complex biomolecular dynamics occurring on slow time scales. A promising approach to enhanced sampling of MSMs is to use "adaptive" methods, in which new MD trajectories are "seeded" preferentially from previously identified states. Here, we investigate the performance of various MSM estimators applied to reseeding trajectory data, for both a simple 1D free energy landscape and mini-protein folding MSMs of WW domain and NTL9(1-39). Our results reveal the practical challenges of reseeding simulations and suggest a simple way to reweight seeding trajectory data to better estimate both thermodynamic and kinetic quantities.

中文翻译：

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使用短播种轨迹的自适应马尔可夫状态模型估计。

在过去的十年中，分子动力学（MD）和马尔可夫状态模型（MSM）方法学的进步使得准确，高效地估计复杂速度下发生的复杂生物分子动力学的动力学速率和反应途径成为可能。增强MSM采样的一种有前途的方法是使用“自适应”方法，其中新的MD轨迹优先从先前确定的状态“播种”。在这里，我们研究了应用于简单的一维自由能态和WW域和NTL9（1-39）的微型蛋白质折叠MSM的各种MSM估计器应用于补种轨迹数据的性能。我们的结果揭示了重新播种模拟的实际挑战，并提出了一种对种子轨迹数据进行加权的简单方法，以更好地估算热力学和动力学数量。