基于流模型的冷冻电镜密度图生成模型，可通用解决多种结构生物学任务。
CryoFM: A Flow-based Foundation Model for Cryo-EM Densities
- 用流匹配学习高质量密度图分布，构建通用先验
- 无需微调即在多个任务上达顶尖性能
- 适合结构生物学家与冷冻电镜研究者使用
冷冻电子显微镜（cryo-EM）是结构生物学与药物发现中的强大技术，可实现高分辨率分子结构解析。近年来，结构生物学家已生成超过38,626个蛋白质密度图，涵盖不同分辨率。然而，现有数据处理算法尚未充分利用这些密度图知识，少数数据驱动模型也仅限特定任务。本文提出CryoFM，一种基于流匹配的生成式基础模型，旨在学习高质量密度图的先验分布，并有效泛化至下游任务。我们引入流后验采样方法，使CryoFM作为灵活先验，适用于多个冷冻电镜与冷冻电子断层成像（cryo-ET）任务，无需微调即可达到多数任务的最先进性能，展现出在该领域广泛应用的潜力。
原文摘要 · Abstract (English)
Cryo-electron microscopy (cryo-EM) is a powerful technique in structural biology and drug discovery, enabling the study of biomolecules at high resolution. Significant advancements by structural biologists using cryo-EM have led to the production of over 38,626 protein density maps at various resolutions1. However, cryo-EM data processing algorithms have yet to fully benefit from our knowledge of biomolecular density maps, with only a few recent models being data-driven but limited to specific tasks. In this study, we present CryoFM, a foundation model designed as a generative model, learning the distribution of high-quality density maps and generalizing effectively to downstream tasks. Built on flow matching, CryoFM is trained to accurately capture the prior distribution of biomolecular density maps. Furthermore, we introduce a flow posterior sampling method that leverages CRYOFM as a flexible prior for several downstream tasks in cryo-EM and cryo-electron tomography (cryo-ET) without the need for fine-tuning, achieving state-of-the-art performance on most tasks and demonstrating its potential as a foundational model for broader applications in these fields.
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