Variational embedding of protein folding simulations using Gaussian mixture variational autoencoders
Variational embedding of protein folding simulations using Gaussian mixture variational autoencoders
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DOI：
10.1063/5.0069708
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发表时间：
2021-11-21
影响因子：
4.4
通讯作者：
Brooks, Bernard R.
中科院分区：
文献类型：
--
作者：
Ghorbani, Mahdi;Prasad, Samarjeet;Brooks, Bernard R.
利用分子动力学模拟对生物分子进行构象采样通常会产生大量的高维数据，这使得传统的分析技术很难解释。因此，需要降维方法来提取有用的和相关的信息。在这里，我们设计了一种机器学习方法--高斯混合变分自动编码器(GMVAE)，它可以在无监督的情况下同时执行生物分子构象的降维和聚类。我们表明，GMVAE可以学习蛋白质折叠的自由能图景的简化表示，其中包含与折叠过程中亚稳态相对应的高度分离的簇。由于GMVAE使用高斯混合作为其先验，它可以直接承认蛋白质折叠自由能景观的多盆地性质。为了使模型端到端可微，我们使用Gumbel-Softmax分布。我们在三个长时间尺度的蛋白质折叠轨迹上测试了该模型，并表明GMVAE嵌入类似于折叠漏斗，在漏斗路径下具有折叠状态，而在漏斗路径之外具有未折叠状态。此外，我们还证明了GMVAE的潜在空间可以用于动力学分析，在此基础上建立的马尔可夫状态模型产生的折叠和展开时间尺度与其他严格的动态嵌入(如时间独立分量分析)非常一致。
Conformational sampling of biomolecules using molecular dynamics simulations often produces a large amount of high dimensional data that makes it difficult to interpret using conventional analysis techniques. Dimensionality reduction methods are thus required to extract useful and relevant information. Here, we devise a machine learning method, Gaussian mixture variational autoencoder (GMVAE), that can simultaneously perform dimensionality reduction and clustering of biomolecular conformations in an unsupervised way. We show that GMVAE can learn a reduced representation of the free energy landscape of protein folding with highly separated clusters that correspond to the metastable states during folding. Since GMVAE uses a mixture of Gaussians as its prior, it can directly acknowledge the multi-basin nature of the protein folding free energy landscape. To make the model end-to-end differentiable, we use a Gumbel-softmax distribution. We test the model on three long-timescale protein folding trajectories and show that GMVAE embedding resembles the folding funnel with folded states down the funnel and unfolded states outside the funnel path. Additionally, we show that the latent space of GMVAE can be used for kinetic analysis and Markov state models built on this embedding produce folding and unfolding timescales that are in close agreement with other rigorous dynamical embeddings such as time independent component analysis.