AUTHOR=Tian Hao , Jiang Xi , Trozzi Francesco , Xiao Sian , Larson Eric C. , Tao Peng TITLE=Explore Protein Conformational Space With Variational Autoencoder JOURNAL=Frontiers in Molecular Biosciences VOLUME=8 YEAR=2021 URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2021.781635 DOI=10.3389/fmolb.2021.781635 ISSN=2296-889X ABSTRACT=

Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational resources and takes a prohibitive amount of time. In this study, we demonstrated that variational autoencoders (VAEs), a type of deep learning model, can be employed to explore the conformational space of a protein through MD simulations. VAEs are shown to be superior to autoencoders (AEs) through a benchmark study, with low deviation between the training and decoded conformations. Moreover, we show that the learned latent space in the VAE can be used to generate unsampled protein conformations. Additional simulations starting from these generated conformations accelerated the sampling process and explored hidden spaces in the conformational landscape.