基于无状态可逆VAMPnets识别的Trp笼微蛋白慢折叠模式动力学的高分辨率Markov状态模型
High-resolution Markov state models for the dynamics of Trp-cage miniprotein constructed over slow folding modes identified by state-free reversible VAMPnets
摘要：
无状态可逆VAMPnets（SRVs）是一种基于神经网络的框架，能够从轨迹数据中学习动力系统的转移算子的前导特征函数。在分子动力学模拟中，这些数据驱动的集体变量（CV）捕获动力学的最慢模式，并且对于增强采样和自由能估计是有用的。在这项工作中，我们采用SRV坐标作为马尔可夫状态模型（MSM）建设的特征集。与现有技术相比，从SRV坐标构建的MSM对输入特征的选择更鲁棒，表现出更快的隐含时标收敛，并且允许使用更短的滞后时间来构建更高的动力学分辨率模型。我们应用这种方法来研究折叠动力学和构象景观的色氨酸笼miniprotein。折叠和展开的平均第一通过时间与以前的文献，并提出了一个九宏观状态模型。未折叠的合奏包括一个中央动力学枢纽与几个亚稳态未折叠构象的相互转换，并作为网关折叠合奏。折叠的合奏包括本机状态，部分展开的中间“循环”状态，和以前未报告的短暂的中间，我们能够解决由于高时间分辨率的SRV-MSM。我们建议将SRVs作为集成到现代MSM建筑管道的优秀候选人。
英文摘要：
State-free reversible VAMPnets (SRVs) are a neural network-based framework capable of learning the leading eigenfunctions of the transfer operator of a dynamical system from trajectory data. In molecular dynamics simulations, these data-driven collective variables (CVs) capture the slowest modes of the dynamics and are useful for enhanced sampling and free energy estimation. In this work, we employ SRV coordinates as a feature set for Markov state model (MSM) construction. Compared to the current state of the art, MSMs constructed from SRV coordinates are more robust to the choice of input features, exhibit faster implied timescale convergence, and permit the use of shorter lagtimes to construct higher kinetic resolution models. We apply this methodology to study the folding kinetics and conformational landscape of the Trp-cage miniprotein. Folding and unfolding mean first passage times are in good agreement with prior literature, and a nine macrostate model is presented. The unfolded ensemble comprises a central kinetic hub with interconversions to several metastable unfolded conformations and which serves as the gateway to the folded ensemble. The folded ensemble comprises the native state, a partially unfolded intermediate "loop" state, and a previously unreported short-lived intermediate that we were able to resolve due to the high time-resolution of the SRV-MSM. We propose SRVs as an excellent candidate for integration into modern MSM construction pipelines.
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