High-Resolution Markov State Models for the Dynamics of Trp-Cage Miniprotein Constructed Over Slow Folding Modes Identified by State-Free Reversible VAMPnets
High-Resolution Markov State Models for the Dynamics of Trp-Cage Miniprotein Constructed Over Slow Folding Modes Identified by State-Free Reversible VAMPnets
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DOI：
10.1021/acs.jpcb.9b05578
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发表时间：
2019-09-26
影响因子：
3.3
通讯作者：
Ferguson, Andrew L.
中科院分区：
文献类型：
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作者：
Sidky, Hythem;Chen, Wei;Ferguson, Andrew L.
无状态可逆VAMPnet(SRV)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传递算子的主导特征函数。在分子动力学模拟中，这些数据驱动的集体变量捕捉最慢的动力学模式，并对增强采样和自由能估计有用。在这项工作中，我们使用SRV坐标作为马尔可夫状态模型(MSM)构建的特征集。与目前最先进的MSM相比，基于SRV坐标构建的MSM对输入特征的选择更健壮，表现出更快的隐含时间尺度收敛，并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Trp-Cage小蛋白的折叠动力学和构象图景。折叠和展开的平均首次通过时间与以前的文献符合得很好，并提出了一个九个宏观状态模型。未折叠的系综包括一个中心动能枢纽，它相互转化为几个亚稳态的未折叠的构象，并作为通往折叠的系综的门户。折叠的系综由本机状态、部分展开的中间“环”状态和以前未报道的短暂中间状态组成，由于SRV-MSM的高时间分辨率，我们能够解析该中间状态。我们建议SRV作为集成到现代MSM建筑管道中的一个极好的候选者。
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 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 time scale 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 the 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.