Latent Models of Molecular Dynamics Data: Automatic Order Parameter Generation for Peptide Fibrillization
Latent Models of Molecular Dynamics Data: Automatic Order Parameter Generation for Peptide Fibrillization
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DOI：
10.1021/acs.jpcb.0c05763
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发表时间：
2020-09-17
影响因子：
3.3
通讯作者：
Shea, Joan-Emma
中科院分区：
文献类型：
--
作者：
Charest, Nathaniel;Tro, Michael;Shea, Joan-Emma
变分自动编码器是人工神经网络，能够将高维数据集减少为更小维的潜在表示。在这项工作中，这些模型应用于粗粒肽自组装的分子动力学模拟，以获得淀粉样蛋白聚集的单值有序参数。这种自动学习的顺序参数是通过对内部坐标表示的潜在参数化进行时间平均来构造的，并将其与向列顺序参数进行比较，向列顺序参数通常用于研究文献中类似系统的排序。研究发现，潜在空间值提供了对聚集机制细节的更有针对性的洞察，在向列序参数无法做到这一点的情况下正确识别原纤维形成。提供了一种方法，通过该方法可以分析潜在空间值，从而识别主要贡献的内部坐标，从而允许根据系统的行为直接解释潜在空间顺序参数。潜在模型被发现是一种有效且方便的表示动态系综数据的方法，并提供了一种降低系统维数的方法，该系统的规模超过迄今为止使用类似工具考虑的分子系统。这绕过了研究人员猜测系统的哪些元素最有助于总结主要转变的需要，并表明当应用于具有多种复杂行为的大型系统时，潜在模型是有效且富有洞察力的。
Variational autoencoders are artificial neural networks with the capability to reduce highly dimensional sets of data to smaller dimensional, latent representations. In this work, these models are applied to molecular dynamics simulations of the self-assembly of coarse-grained peptides to obtain a singled-valued order parameter for amyloid aggregation. This automatically learned order parameter is constructed by time-averaging the latent parametrizations of internal coordinate representations and compared to the nematic order parameter which is commonly used to study ordering of similar systems in literature. It is found that the latent space value provides more tailored insight into the aggregation mechanism's details, correctly identifying fibril formation in instances where the nematic order parameter fails to do so. A means is provided by which the latent space value can be analyzed so that the major contributing internal coordinates are identified, allowing for a direct interpretation of the latent space order parameter in terms of the behavior of the system. The latent model is found to be an effective and convenient way of representing the data from the dynamic ensemble and provides a means of reducing the dimensionality of a system whose scale exceeds molecular systems so-far considered with similar tools. This bypasses a need for researcher speculation on what elements of a system best contribute to summarizing major transitions and suggests latent models are effective and insightful when applied to large systems with a diversity of complex behaviors.