Abstract
Cryogenic electron microscopy (cryo-EM) has become a widely used technique for determining the three-dimensional structures of biological macromolecules. Despite its advantages, building accurate structural models from cryo-EM data remains challenging, particularly at non-atomic resolutions. Here, we present CryoZeta, a de novo structure modeling program that leverages a diffusion-based generative deep neural network to integrate cryo-EM map density features with a biomolecular structure prediction pipeline similar to Alphafold3. By jointly leveraging sequence information and density-based features, CryoZeta generates highly accurate structural models that are consistent with the experimental map density. Evaluated on benchmark datasets covering protein complexes, protein–nucleic acid assemblies, and nucleic acid–only systems at resolutions up to 10 Å, CryoZeta consistently outperforms existing cryo-EM modeling methods in atomic accuracy. These results highlight the benefits of directly incorporating cryo-EM density into modern structure prediction pipelines and establish the method as a robust tool for automated, high-fidelity modeling from cryo-EM maps.
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