CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models

• URL: http://arxiv.org/abs/2602.02620v1
• Date: Mon, 02 Feb 2026 13:36:36 GMT
• Title: CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models
• Authors: Weining Fu, Kai Shu, Kui Xu, Qiangfeng Cliff Zhang,
• Abstract summary: We present CryoLVM, a foundation model that learns rich structural representations from experimental density maps with resolved structures.<n>We demonstrate CryoLVM's effectiveness across three critical cryo-EM tasks: density map sharpening, density map super-resolution, and missing wedge restoration.
• Score: 20.31346781705925
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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