CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis

TL;DR

CryoCCD introduces a novel conditional diffusion framework combined with biophysical modeling to generate realistic cryo-EM micrographs, improving data synthesis for structural biology applications.

Contribution

It presents the first conditional cycle-consistent diffusion model integrated with biophysical modeling for cryo-EM data synthesis, addressing data scarcity and noise realism.

Findings

Generates structurally faithful cryo-EM micrographs

Enhances particle picking and pose estimation accuracy

Outperforms existing synthesis methods in realism and generalization

Abstract

Single-particle cryo-electron microscopy (cryo-EM) has become a cornerstone of structural biology, enabling near-atomic resolution analysis of macromolecules through advanced computational methods. However, the development of cryo-EM processing tools is constrained by the scarcity of high-quality annotated datasets. Synthetic data generation offers a promising alternative, but existing approaches lack thorough biophysical modeling of heterogeneity and fail to reproduce the complex noise observed in real imaging. To address these limitations, we present CryoCCD, a synthesis framework that unifies versatile biophysical modeling with the first conditional cycle-consistent diffusion model tailored for cryo-EM. The biophysical engine provides multi-functional generation capabilities to capture authentic biological organization, and the diffusion model is enhanced with cycle consistency and mask-guided contrastive learning to ensure realistic noise while preserving structural fidelity. Extensive experiments demonstrate that CryoCCD generates structurally faithful micrographs, enhances particle picking and pose estimation, as well as achieves superior performance over state-of-the-art baselines, while also generalizing effectively to held-out protein families.