MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an 710049, China.
Comb Chem High Throughput Screen. 2023;26(3):449-458. doi: 10.2174/1386207325666220514143909.
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.
随着结构生物学的不断发展，在生物大分子功能调节过程中对准确三维结构的要求越来越高。因此，确定生物大分子的动态结构已成为一项优先任务。随着冷冻电子显微镜（cryo-EM）技术的发展，生物大分子在原子分辨率水平上的柔性结构迅速发展。然而，cryo-EM 很难在不投入大量人力和时间的情况下产生高分辨率的动态结构。幸运的是，深度学习属于人工智能领域，它加快并简化了处理高通量 cryo-EM 数据的工作流程。在这里，我们对深度学习在 cryo-EM 数据处理方面具有显著效果的一些软件包和参考算法进行了总结和概括，包括 Warp、无用户预处理例程、 TranSPHIRE、PARSED、Topaz、crYOLO 和自监督工作流程，并指出了提高三维重建分辨率和效率的策略。我们希望这将为生物大分子的动态结构建模提供一些启示。
