Abstract：
Molecular dynamics simulation is a crucial research domain within the life sciences, focusing on comprehending the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield, often utilizes MD for implementation, with trajectory data play a pivotal role in drug discovery. The advancement of high-performance computing and deep learning technology becomes popular and critical to predict protein properties from vast trajectory data, posing challenges regarding data features extraction from the complicated simulation data and dimensionality reduction. Simultaneously, it is essential to provide a meaningful explanation of the biological mechanism behind dimensionality. To tackle this challenge, we propose a new unsupervised model named RevGraphVAMP to intelligently analyze the simulation trajectory. This model is based on the variational approach for Markov processes (VAMP) and integrates graph convolutional neural networks and physical constraint optimization to enhance the learning performance. Additionally, we introduce attention mechanism to assess the importance of key interaction region, facilitating the interpretation of molecular mechanism. In comparison to other VAMPNets models, our model showcases competitive performance, improved accuracy in state transition prediction, as demonstrated through its application to two public datasets and the Shank3-Rap1 complex, which is associated with autism spectrum disorder. Moreover, it enhanced dimensionality reduction discrimination across different substates and provides interpretable results for protein structural characterization.
摘要：
分子模拟(MD)是生命科学中的一个重要研究领域,专注于理解原子尺度上生物分子相互作用的机制。蛋白质模拟,作为一个关键的子场，经常利用MD来实现，轨迹数据在药物发现中起着举足轻重的作用。高性能计算和深度学习技术的进步对于从大量轨迹数据中预测蛋白质属性变得流行和关键，对从复杂的仿真数据中提取数据特征和降维提出了挑战。同时,对维度背后的生物学机制提供有意义的解释是至关重要的。为了应对这一挑战，我们提出了一种新的无监督模型RevGraphVAMP来智能分析仿真轨迹。该模型基于马尔可夫过程（VAMP）的变分方法，并集成了图卷积神经网络和物理约束优化来增强学习性能。此外,我们引入关注机制来评估关键交互区域的重要性，促进分子机制的解释。与其他VAMPNets型号相比，我们的模型展示了有竞争力的表现，提高了状态转移预测的准确性，正如它在两个公共数据集和Shank3-Rap1复合体上的应用所证明的那样，这与自闭症谱系障碍有关。此外，它增强了不同子状态之间的降维区分，并为蛋白质结构表征提供了可解释的结果。
