Abstract
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.
Abstract (translated)
低温电子显微镜（cryo-EM）通过实现生物分子组装的近原子级可视化，彻底革新了结构生物学领域。然而，随着cryo-EM数据通量和复杂性的指数增长以及各种下游分析任务的需求，迫切需要能够超越目前仅限于特定任务且可扩展性和泛化能力有限的深度学习方法的统一计算框架。我们介绍了CryoLVM，这是一种基础模型，它通过利用集成SCUNet骨干网的联合嵌入预测架构（JEPA）从具有解析结构的实验密度图中学习丰富的结构表示，可以快速适应各种下游任务。此外，我们提出了一种基于直方图的分布对齐损失函数，该函数加速了收敛速度并增强了微调性能。我们在三个关键的cryo-EM任务上展示了CryoLVM的有效性：密度图锐化、密度图超分辨率和缺失楔形恢复。我们的方法在多个密度图质量指标上始终优于最先进的基准模型，证明了其作为广泛cryo-EM应用领域灵活模型的巨大潜力。
URL
https://arxiv.org/abs/2602.02620
