深度学习让冷冻电镜结构解析更准更快，几乎无需人工干预。
Review of Deep Learning Applications to Structural Proteomics Enabled by Cryogenic Electron Microscopy and Tomography
- 用卷积神经网络自动找粒子，解决噪声和取向偏倚难题。
- 实现近原子分辨率重建，成功解析了艾滋病毒颗粒等复杂结构。
- 适合结构生物学家、计算研究人员，推动高通量结构解析。
过去十年的“冷冻电镜革命”通过冷冻电子显微镜（cryoEM）和断层成像（cryoET）技术实现了高质量结构数据的指数级增长。深度学习融入结构蛋白组学流程，解决了长期存在的信噪比低、取向偏好伪影和缺失楔形问题，显著提升了效率与可扩展性。本综述系统分析了人工智能在全链条冷冻电镜工作流中的应用：从基于卷积神经网络的自动粒子挑选（Topaz、crYOLO、CryoSegNet），到解决取向偏差的计算方法（spIsoNet、cryoPROS），以及先进的去噪算法（Topaz-Denoise）。在冷冻断层成像中，IsoNet采用U-Net架构同步实现缺失楔形校正与降噪，TomoNet则通过AI驱动的粒子检测加速亚断层平均。最终环节利用ModelAngelo、DeepTracer、CryoREAD等工具实现原子模型自动化构建，将密度图转化为可解释的生物结构。这些方法已实现近原子分辨率重构，成功处理曾难以解析的严重取向偏倚数据集，并应用于从HIV病毒样颗粒到原位核糖体复合物等多种生物体系。随着深度学习发展，特别是大语言模型与视觉变换器的兴起，未来有望实现高度自动化与普及化，深刻变革对大分子结构与功能的认知。
原文摘要 · Abstract (English)
The past decade's "cryoEM revolution" has produced exponential growth in high-resolution structural data through advances in cryogenic electron microscopy (cryoEM) and tomography (cryoET). Deep learning integration into structural proteomics workflows addresses longstanding challenges including low signal-to-noise ratios, preferred orientation artifacts, and missing-wedge problems that historically limited efficiency and scalability. This review examines AI applications across the entire cryoEM pipeline, from automated particle picking using convolutional neural networks (Topaz, crYOLO, CryoSegNet) to computational solutions for preferred orientation bias (spIsoNet, cryoPROS) and advanced denoising algorithms (Topaz-Denoise). In cryoET, tools like IsoNet employ U-Net architectures for simultaneous missing-wedge correction and noise reduction, while TomoNet streamlines subtomogram averaging through AI-driven particle detection. The workflow culminates with automated atomic model building using sophisticated tools like ModelAngelo, DeepTracer, and CryoREAD that translate density maps into interpretable biological structures. These AI-enhanced approaches have achieved near-atomic resolution reconstructions with minimal manual intervention, resolved previously intractable datasets suffering from severe orientation bias, and enabled successful application to diverse biological systems from HIV virus-like particles to in situ ribosomal complexes. As deep learning evolves, particularly with large language models and vision transformers, the future promises sophisticated automation and accessibility in structural biology, potentially revolutionizing our understanding of macromolecular architecture and function.
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