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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Review Article

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

Author(s): Qiushi Ye, Yizhen Zhao, Xuhua Li, Yimin Zhao, Xinyue Fu, Shengli Zhang, Zhiwei Yang* and Lei Zhang

Volume 26, Issue 3, 2023

Published on: 17 August, 2022

Page: [449 - 458] Pages: 10

DOI: 10.2174/1386207325666220514143909

Price: $65

Abstract

With the continuous development of structural biology, the requirement for accurate threedimensional structures during functional modulation of biological macromolecules is increasing. Therefore, determining the dynamic structures of bio-macromolecular at high resolution has been a highpriority 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 efficiency of three-dimensional reconstruction. We hope it will shed some light on the bio-macromolecular dynamic structure modeling with the deep learning algorithms.

Keywords: Dynamic structures of biomacromolecules, Cryo-EM, Data preprocessing, Particle selection, Deep learning.

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