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EMProt improves structure determination from cryo-EM maps

Nature Structural & Molecular Biology (2025)Cite this article

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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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Fig. 1: Overview of the EMProt framework.
Fig. 2: Comparison of EMProt, DeepMainmast, ModelAngelo and PHENIX in recovering the PDB structures on the test set of n = 177 maps.
Fig. 3: Comparison of the built models by EMProt, DeepMainmast, ModelAngelo and PHENIX in terms of recovering the PDB structures.
Fig. 4: Comparison of EMProt, DeepMainmast, PHENIX and AF3 in building the complete structure on the test set of n = 177 maps.
Fig. 5: Comparison of the models built by EMProt, DeepMainmast, PHENIX and AF3 in terms of building the complete structures.
Fig. 6: Quality assessment of the built models by local confidence scores.

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Data availability

All published datasets used in this paper were taken from the EMDB and PDB (accession codes specified in the figure captions and Supplementary Tables). All raw data of the evaluation results are provided in the article and Supplementary Information. The list of items in the test set is available in Supplementary Table 1. The list of training items is available in Supplementary Table 6. Source data are provided with this paper.

Code availability

The EMProt package is freely available online for academic or noncommercial users (http://huanglab.phys.hust.edu.cn/EMProt or https://github.com/huang-laboratory/EMProt). A link to a demo that runs on Google Colab and requires no installation on the local device is available on the GitHub repository.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (32161133002, 32430020 and 62072199 to S.-Y.H.), the startup grant of Huazhong University of Science and Technology (to S.-Y.H.) and the Postdoctoral Fellowship Program of the China Postdoctoral Science Fund (GZB20250617 to T.L.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. School of Physics and Key Laboratory of Molecular Biophysics of MOE, Huazhong University of Science and Technology, Wuhan, China

    Tao Li, Ji Chen, Hao Li, Hong Cao & Sheng-You Huang

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  1. Tao Li
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  2. Ji Chen
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  3. Hao Li
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  4. Hong Cao
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Contributions

S.-Y.H. conceptualized and supervised the project. T.L., J.C., H.L. and H.C. designed and performed the experiments. S.-Y.H. and T.L analyzed the data. T.L. and S.-Y.H. wrote the paper. All authors reviewed and approved the final version of the paper.

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Correspondence to Sheng-You Huang.

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Nature Structural & Molecular Biology thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Sara Osman, in collaboration with the Nature Structural & Molecular Biology team.

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Li, T., Chen, J., Li, H. et al. EMProt improves structure determination from cryo-EM maps. Nat Struct Mol Biol (2025). https://doi.org/10.1038/s41594-025-01723-1

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