Xiao Sian, Song Zilin, Tian Hao, Tao Peng
Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75205, United States.
J Comput Biophys Chem. 2023 Jun;22(4):489-501. doi: 10.1142/s2737416523500217. Epub 2023 Mar 27.
Molecular dynamics (MD) simulations have been extensively used to study protein dynamics and subsequently functions. However, MD simulations are often insufficient to explore adequate conformational space for protein functions within reachable timescales. Accordingly, many enhanced sampling methods, including variational autoencoder (VAE) based methods, have been developed to address this issue. The purpose of this study is to evaluate the feasibility of using VAE to assist in the exploration of protein conformational landscapes. Using three modeling systems, we showed that VAE could capture high-level hidden information which distinguishes protein conformations. These models could also be used to generate new physically plausible protein conformations for direct sampling in favorable conformational spaces. We also found that VAE worked better in interpolation than extrapolation and increasing latent space dimension could lead to a trade-off between performances and complexities.
分子动力学（MD）模拟已被广泛用于研究蛋白质动力学及其随后的功能。然而，MD模拟往往不足以在可及的时间尺度内探索蛋白质功能所需的足够构象空间。因此，人们开发了许多增强采样方法，包括基于变分自编码器（VAE）的方法来解决这个问题。本研究的目的是评估使用VAE辅助探索蛋白质构象景观的可行性。通过三个建模系统，我们表明VAE可以捕捉区分蛋白质构象的高级隐藏信息。这些模型还可用于生成新的物理上合理的蛋白质构象，以便在有利的构象空间中进行直接采样。我们还发现VAE在插值方面比外推效果更好，增加潜在空间维度会导致性能和复杂性之间的权衡。
