Extraction of protein dynamics information from cryo-EM maps using deep learning

Abstract

Elucidation of both the three-dimensional structure and the dynamics of a protein is essential to understand its function. Technical breakthroughs in single-particle analysis based on cryo-electron microscopy (cryo-EM) have enabled the three-dimensional structures of numerous proteins to be solved at atomic or near-atomic resolution. However, the analysis of the dynamics of protein targets using cryo-EM is often challenging because of their large sizes and complex structural assemblies. Here, we describe DEFMap, a deep learning-based approach to directly extract the dynamics associated with the atomic fluctuations that are hidden in cryo-EM density maps. Using only cryo-EM density data, DEFMap provides dynamics that correlate well with data obtained from molecular dynamics simulations and experimental approaches. Furthermore, DEFMap successfully detects changes in dynamics that are associated with molecular recognition. This strategy combines deep learning, experimental data and molecular dynamics simulations, and may reveal a new multidisciplinary approach for protein science. Cryo-electron microscopy (cryo-EM) can be used to determine the three-dimensional structure of proteins at atomic-scale resolution. It is challenging to observe the dynamics of proteins using cryo-EM because of their large sizes and complex structural assemblies. A new deep-learning approach called DEFMap extracts the dynamics associated with the atomic fluctuations that are hidden in cryo-EM density maps.