GraphVAMPNet：利用图神经网络与马尔可夫过程变分方法进行生物分子动力学建模
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules
AI总结：
本研究结合VAMPNet与图神经网络，提出端到端的GraphVAMPNet框架，通过图消息传递生成嵌入并加入注意力机制，从生物分子长时间轨迹中学习动力学与亚稳态，得到分辨率更高、可解释性更强的马尔可夫模型。
摘要：
从蛋白质折叠或配体-受体结合等生物分子过程的长时间尺度轨迹中找到数据的低维表示具有根本重要性，而马尔可夫建模等动力学模型已被证明可有效描述这些系统的动力学特性。近期，一种名为VAMPNet的无监督机器学习技术被提出，用于以端到端方式学习低维表示和线性动力学模型。VAMPNet基于马尔可夫过程变分方法（VAMP），依靠神经网络来学习粗粒度动力学。在本研究中，我们将VAMPNet与图神经网络相结合，构建了一个端到端框架，可从长时间尺度分子动力学轨迹中高效学习高层动力学和亚稳态。该方法兼具图表示学习的优势，通过图消息传递操作为每个数据点生成嵌入，再将其输入VAMPNet以生成粗粒度表示。与标准VAMPNet相比，这种分子表示方式能得到分辨率更高、可解释性更强的马尔可夫模型，从而支持对生物分子过程开展更细致的动力学研究。我们的GraphVAMPNet方法还引入了注意力机制，可识别用于划分不同亚稳态的关键残基。
英文摘要：
Finding low dimensional representation of data from long-timescale trajectories of biomolecular processes such as protein-folding or ligand-receptor binding is of fundamental importance and kinetic models such as Markov modeling have proven useful in describing the kinetics of these systems. Recently, an unsupervised machine learning technique called VAMPNet was introduced to learn the low dimensional representation and linear dynamical model in an end-to-end manner. VAMPNet is based on variational approach to Markov processes (VAMP) and relies on neural networks to learn the coarse-grained dynamics. In this contribution, we combine VAMPNet and graph neural networks to generate an end-to-end framework to efficiently learn high-level dynamics and metastable states from the long-timescale molecular dynamics trajectories. This method bears the advantages of graph representation learning and uses graph message passing operations to generate an embedding for each datapoint which is used in the VAMPNet to generate a coarse-grained representation. This type of molecular representation results in a higher resolution and more interpretable Markov model than the standard VAMPNet enabling a more detailed kinetic study of the biomolecular processes. Our GraphVAMPNet approach is also enhanced with an attention mechanism to find the important residues for classification into different metastable states.
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