西湖大学申怀宗研究团队取得一项新突破。他们揭示了低温电镜图像处理的综合基础模型。2025年11月27日出版的《自然—方法学》发表了这项成果。

为了解决这一限制，课题组研究人员引入了Cryo-EM图像评估基础（Cryo-IEF）模型，这是一个通过无监督学习对约6500万Cryo-EM颗粒图像进行预训练的多功能工具。Cryo-IEF执行各种cryo-EM处理任务，包括按结构进行颗粒分类，基于姿态的clthemtering和图像质量评估。在此基础上，课题组人员开发了CryoWizard，这是一个全自动的单粒子低温电镜处理管道，通过微调的低温IEF实现有效的粒子质量排序。CryoWizard解决了不同性质样品的高分辨率结构，并有效地缓解了低温电镜中首选取向的普遍挑战。Cryo-IEF是一个预训练的基础模型，用于在单粒子cryo-EM中执行各种图像处理任务。CryoWizard，由Cryo-IEF启用，是一个全自动的低温电镜处理管道。

据介绍，低温电子显微镜（cryo-EM）已成为确定生物大分子高分辨率结构的首要技术。然而，它的广泛应用受到对专门知识的需求的限制。

附：英文原文

Title: A comprehensive foundation model for cryo-EM image processing

Author: Yan, Yang, Fan, Shiqi, Yuan, Fajie, Shen, Huaizong

Issue&Volume: 2025-11-27

Abstract: Cryogenic electron microscopy (cryo-EM) has become a premier technique for determining high-resolution structures of biological macromolecules. However, its broad application is constrained by the demand for specialized expertise. Here, to address this limitation, we introduce the Cryo-EM Image Evaluation Foundation (Cryo-IEF) model, a versatile tool pre-trained on ~65 million cryo-EM particle images through unsupervised learning. Cryo-IEF performs diverse cryo-EM processing tasks, including particle classification by structure, pose-based clustering and image quality assessment. Building on this foundation, we developed CryoWizard, a fully automated single-particle cryo-EM processing pipeline enabled by fine-tuned Cryo-IEF for efficient particle quality ranking. CryoWizard resolves high-resolution structures across samples of varied properties and effectively mitigates the prevalent challenge of preferred orientation in cryo-EM. Cryo-IEF is a pre-trained foundation model for performing diverse image-processing tasks in single-particle cryo-EM. CryoWizard, enabled by Cryo-IEF, is a fully automated cryo-EM processing pipeline.

DOI: 10.1038/s41592-025-02916-8

Source: https://www.nature.com/articles/s41592-025-02916-8