Explore Protein Conformational Space With Variational Autoencoder - Tian, Hao; Jiang, Xi; Trozzi, Francesco; Xiao, Sian; Larson, Eric C.; Tao, Peng (Frontiers in Molecular Biosciences, 2021)
Molecular dynamic simulations have been extensively used in the studies of protein dynamical and functional properties. However, extensive and sufficient sampling in the protein conformational space requires large amount of computational powers and time. In this study, we demonstrated that variational autoencoders, a type of deep learning model, can be employed to explore protein conformational space through combination of molecular dynamics simulations. Variational autoencoders are shown to be superior to autoencoders through a benchmark study, with low deviation between the training and decoded conformations. Moreover, we showed that the learned latent space in the trained variational autoencoder can be used to generate unsampled protein conformations. Additional simulations starting from these generated conformations greatly enhanced the sampling efficiency and explored hidden spaces in the protein conformational landscape.