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

TL;DR

CryoCCD is presented, a synthesis framework that unifies versatile biophysical modeling with the first conditional cycle-consistent diffusion model tailored for cryo-EM and achieves superior performance over state-of-the-art baselines, while also generalizing effectively to held-out protein families.

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.

Figures (9)

• Figure 1: Cryo-EM simulation results: (a) Visualization of the processed particles. (b) Different placement strategies are adopted based on the particles' properties. (c) Generation of multi-scale synthetic data from real cryo-EM images according to EMPIAR-10421. (d) Visualization of simulated cryo-EM images.
• Figure 2: CryoCCD pipeline: (1) The input structures are inserted into a volume through specific placement strategies, which then undergoes physics-based projection and density conversion to generate synthetic images. (2) In the image translation, we use two diffusion models, mask-guided contrastive learning, and discriminator to achieve realistic synthetic-to-real image translation.
• Figure 3: Visual Examples of CryoCCD Pipeline.
• Figure 4: Comparison between real images and generated fake real images. Our method produces more authentic micrographs with superior noise characteristics across all datasets.
• Figure 5: AlphaFold3-based pipeline for enhancing compositional and conformational heterogeneity of the structure library. Sequences lacking experimental structures are selected from the UniProt uniprot2019uniprot database to increase compositional heterogeneity, and entries with potential conformational diversity are selected to enhance conformational heterogeneity. The green frame shows the atomic models of two human PNPase (Q8TCS8) conformations processed based on AlphaFold3; in the blue frame, for comparison, are the open formation (9KJR) and closed formation (9KJT) of human PNPase from the Protein Data Bank. Below the frames are illustrations of the differences between these conformations. The generated particles are then converted into density maps and undergo biophysical modeling to generate simulated cryo-EM images.