ArticleNature structural & molecular biology2026
EMProt improves structure determination from cryo-EM maps.
Article in Nature structural & molecular biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- When cryo-EM modeling meets structure prediction.Nature structural & molecular biology · 2026Article
- Closing 2025, and a look ahead.Nature structural & molecular biology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cryo-electron microscopy (cryo-EM) has become the mainstream technique for macromolecular structure determination. However, because of intrinsic resolution heterogeneity, accurate modeling of all-atom structure from cryo-EM maps remains challenging even for maps at near-atomic resolution. Addressing the challenge, we present EMProt, a fully automated method for accurate protein structure determination from cryo-EM maps by efficiently integrating map information and structure prediction with a three-track attention network. EMProt is extensively evaluated on a diverse test set of 177 experimental cryo-EM maps with up to 54 chains in a case at <4-Å resolution, and compared to state-of-the-art methods including DeepMainmast, ModelAngelo, phenix.dock_and_rebuild and AlphaFold3. It is shown that EMProt greatly outperforms the existing methods in recovering the protein structure and building the complete structure. In addition, the built models by EMrot exhibit a high accuracy in model-to-map fit and structure validations.
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Registered trials
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