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

2026-02-02

AI Summary

This work addresses the limited generalization of existing deep learning methods in cryo-electron microscopy (cryo-EM), which are typically task-specific and struggle with increasingly complex density map analysis. To overcome this, we propose CryoLVM—the first self-supervised foundation model for cryo-EM density maps—built upon a Joint Embedding Predictive Architecture (JEPA) with an SCUNet backbone and enhanced by a novel histogram distribution alignment loss that significantly accelerates convergence and improves fine-tuning performance. CryoLVM supports multi-task transfer learning and consistently outperforms state-of-the-art methods across three critical tasks: density map sharpening, super-resolution, and missing wedge inpainting, achieving leading results on multiple evaluation metrics.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Large Vision Models
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• cryo-EM
• foundation model
• self-supervised learning
• structural biology
• density map analysis

Innovation

• CryoLVM
• self-supervised learning
• JEPA
• density map enhancement
• foundation model