用自监督学习让大模型理解冷冻电镜密度图，一模型通用于多种结构分析任务
CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models
- 基于JEPA架构与SCUNet骨干网络，从实验密度图中学习通用结构表征
- 在锐化、超分辨率、缺角修复三项任务上均超越现有最佳方法
- 适合结构生物学研究者快速适配新任务，提升数据处理效率
冷冻电镜（cryo-EM）已实现生物分子复合物的近原子级可视化。然而，数据量激增与任务多样性迫切需要超越现有专用深度学习模型的统一计算框架。我们提出CryoLVM，一种基于联合嵌入预测架构（JEPA）与SCUNet骨干网络的大规模视觉模型，可从已解析结构的实验密度图中学习丰富的结构表征，并快速适配各类下游任务。我们还设计了一种基于直方图的分布对齐损失，显著加速收敛并提升微调性能。在密度图锐化、超分辨率和缺失楔形修复三个关键任务中，该方法在多个质量指标上持续优于现有最优基线，验证了其作为通用模型在广泛冷冻电镜应用中的潜力。
原文摘要 · Abstract (English)
Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling near-atomic-level visualization of biomolecular assemblies. However, the exponential growth in cryo-EM data throughput and complexity, coupled with diverse downstream analytical tasks, necessitates unified computational frameworks that transcend current task-specific deep learning approaches with limited scalability and generalizability. We present CryoLVM, a foundation model that learns rich structural representations from experimental density maps with resolved structures by leveraging the Joint-Embedding Predictive Architecture (JEPA) integrated with SCUNet-based backbone, which can be rapidly adapted to various downstream tasks. We further introduce a novel histogram-based distribution alignment loss that accelerates convergence and enhances fine-tuning performance. We demonstrate CryoLVM's effectiveness across three critical cryo-EM tasks: density map sharpening, density map super-resolution, and missing wedge restoration. Our method consistently outperforms state-of-the-art baselines across multiple density map quality metrics, confirming its potential as a versatile model for a wide spectrum of cryo-EM applications.
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