Cryogenic electron microscopy (cryo-EM) enables high-resolution structural determination of large macromolecular complexes. However, the interpretability of cryo-EM maps is often hindered by substantial background noise and signal attenuation, which obscure structural details. Although existing post-processing methods can partially mitigate these artifacts, they typically suffer from over-smoothing and lack reliable confidence estimation. Here, we present CryoDiff, an uncertainty-aware diffusion model for cryo-EM map enhancement. CryoDiff employs a multi-step diffusion process to progressively denoise and restore high-resolution structural features. Importantly, CryoDiff incorporates a voxel-wise confidence metric derived from Monte Carlo sampling. It unifies map enhancement and voxel-level uncertainty estimation within a diffusion-based generative framework, representing the first approach to achieve such joint modeling for cryo-EM map enhancement. In comprehensive experiments, CryoDiff markedly outperforms existing methods in both map-model correlation and map interpretability, improving the average FSC0.5 metric by 0.356 [A] over state-of-the-art approaches. When applied to textit{de novo} model building with ModelAngelo, CryoDiff further increases model completeness by 5.5%, exceeding the gains achieved by competing method.
Wen, B., He, B., Cheng, Y., Zhou, S., Han, R., Zhang, F.
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