深度马尔可夫状态建模的进展：粗粒化和实验数据约束
Progress in deep Markov State Modeling: Coarse graining and experimental data restraints
摘要：
深度学习框架的最新进展为分析蛋白质等复杂系统的长期行为建立了有价值的工具。特别是纳入物理限制，例如时间可逆性，是使这些方法适用于生物物理系统的关键步骤。此外，我们提出的方法，将实验观测到的模型估计表明，模拟数据中的偏差可以补偿。我们进一步开发了一个新的神经网络层，以建立一个分层模型，允许不同层次的细节进行研究。最后，我们提出了一个注意机制，突出了重要的残基的分类到不同的状态。我们展示了一个超长的分子动力学模拟的Villin头部miniprotein的新方法。
英文摘要：
Recent advances in deep learning frameworks have established valuable tools for analyzing the long-timescale behavior of complex systems such as proteins. Especially the inclusion of physical constraints, e.g. time-reversibility, was a crucial step to make the methods applicable to biophysical systems. Furthermore, we advance the method by incorporating experimental observables into the model estimation showing that biases in simulation data can be compensated for. We further develop a new neural network layer in order to build an hierarchical model allowing for different level of details to be studied. Finally, we propose an attention mechanism which highlights important residues for the classification into different states. We demonstrate the new methodology on an ultralong molecular dynamics simulation of the Villin headpiece miniprotein.
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