Chung Jeong Min, Durie Clarissa L, Lee Jinseok
Department of Biotechnology, The Catholic University of Korea, Bucheon-si 14662, Gyeonggi, Korea.
Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA.
Life (Basel). 2022 Aug 19;12(8):1267. doi: 10.3390/life12081267.
Cryo-electron microscopy (cryo-EM) has become an unrivaled tool for determining the structure of macromolecular complexes. The biological function of macromolecular complexes is inextricably tied to the flexibility of these complexes. Single particle cryo-EM can reveal the conformational heterogeneity of a biochemically pure sample, leading to well-founded mechanistic hypotheses about the roles these complexes play in biology. However, the processing of increasingly large, complex datasets using traditional data processing strategies is exceedingly expensive in both user time and computational resources. Current innovations in data processing capitalize on artificial intelligence (AI) to improve the efficiency of data analysis and validation. Here, we review new tools that use AI to automate the data analysis steps of particle picking, 3D map reconstruction, and local resolution determination. We discuss how the application of AI moves the field forward, and what obstacles remain. We also introduce potential future applications of AI to use cryo-EM in understanding protein communities in cells.
冷冻电子显微镜(cryo-EM)已成为确定大分子复合物结构的无与伦比的工具。大分子复合物的生物学功能与这些复合物的灵活性紧密相关。单颗粒冷冻电子显微镜可以揭示生化纯样品的构象异质性,从而得出关于这些复合物在生物学中所起作用的有充分依据的机制假设。然而,使用传统数据处理策略处理日益庞大、复杂的数据集在用户时间和计算资源方面都极其昂贵。当前数据处理方面的创新利用人工智能(AI)来提高数据分析和验证的效率。在这里,我们回顾了使用人工智能自动执行颗粒挑选、三维图谱重建和局部分辨率测定等数据分析步骤的新工具。我们讨论了人工智能的应用如何推动该领域的发展,以及仍然存在哪些障碍。我们还介绍了人工智能在利用冷冻电子显微镜理解细胞中蛋白质群落方面潜在的未来应用。