Extraction of Protein Dynamics Information Hidden in Cryo-EM Map Using Deep Learning

Abstract

Technical breakthroughs in cryogenic electron microscopy (cryo-EM)-based single-particle analysis have enabled the structures of numerous proteins to be solved at atomic or near-atomic resolutions, including extremely large macromolecules whose structures could not be solved by conventional techniques. Determining the dynamics properties of these macromolecules, based on their solved structures, can further improve our understanding of their functional mechanisms. However, such analysis is often hampered by the large molecular size and complex structural assembly, making both experimental and computational approaches to determine dynamics properties challenging. Here, we report a deep learning-based approach, DEFMap, to extract the dynamics information "hidden" in cryo-EM density maps. By relying only on cryo-EM maps, DEFMap successfully provided dynamics information equivalent to that determined from molecular dynamics (MD) simulations and experimental approaches at the atomic and residue levels. Additionally, DEFMap could detect dynamics changes associated with molecular recognition and the accompanying allosteric conformational stabilizations, which trigger various biological events such as signal transduction and enzyme catalysis. This approach will provide new insights into the functional mechanisms of biological molecules, accelerating modern molecular biology researches. Furthermore, this advanced strategy combining experimental data, deep learning approaches, and MD simulations would open a new multidisciplinary science area