Accounting Conformational Dynamics into Structural Modeling Reflected by Cryo-EM with Deep Learning

With the continuous development of structural biology, the requirement for accurate three-dimensional structures during functional modulation of biological macromolecules is increasing. There-fore, determining the dynamic structures of bio-macromolecular at high resolution has been a high-priority task. With the development of cryo-electron microscopy (cryo-EM) techniques, the flexible structures of biomacromolecules at the atomic resolution level grow rapidly. Nevertheless, it is difficult for cryo-EM to produce high-resolution dynamic structures without a great deal of manpower and time. Fortunately, deep learning, belonging to the domain of artificial intelligence, speeds up and simplifies this workflow for handling the high-throughput cryo-EM data. Here, we generalized and summarized some software packages and referred algorithms of deep learning with remarkable effects on cryo-EM data processing, including Warp, user-free preprocessing routines, TranSPHIRE, PARSED, Topaz, crYOLO, and self-supervised workflow, and pointed out the strategies to improve the resolution and ef-ficiency of three-dimensional reconstruction. We hope it will shed some light on the bio-macromolecular dynamic structure modeling with the deep learning algorithms.