{
  "abstract": "Cryo-electron microscopy (cryo-EM) has become one of the most powerful techniques for macromolecular structure determination. However, accurate model building from cryo-EM maps remains challenging, particularly for nucleic acids. Here, we present EM3DFold, a unified de novo model-building framework for accurate structure determination of proteins, nucleic acids, and protein-nucleic acid complexes from cryo-EM maps using a density-aware, large language model-powered three-track attention (TTA) network. The TTA network effectively integrates sequence, density, and structural information to enable accurate all-atom model building. EM3DFold was extensively evaluated on independent benchmarks of 298 experimental cryo-EM maps at < 4.0 [A] resolutions, and achieves an unprecedentedly high median accuracy of 75% completeness (88% coverage and 94% sequence accuracy) for 178 nucleic acid maps, 90% completeness (95% coverage and 96% sequence accuracy) for 124 protein-nucleic acid complexes, and 95% completeness (97% coverage and 98% sequence accuracy) for 120 protein-only targets, substantially outperforming state-of-the-art methods including ModelAngelo, EM2NA, CryoREAD, and EMProt. In addition, EM3DFold also produces models with superior model-to-map fit and stereochemical quality, providing a robust and reliable solution for automated cryo-EM model building. The EM3DFold package is freely available at https://github.com/huang-laboratory/EM3DFold.",
  "abstract_shown": true,
  "affiliations": [
    "Huazhong University of Science and Technology"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "T. Li"
    },
    {
      "name": "H. Cao"
    },
    {
      "affs": [
        0
      ],
      "name": "S.-Y. Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8m6xybs1ctnzg.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.16.752067",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752067v1.full",
  "id": "8m6xybs1ctnzg",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752067v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1315|Advanced Electron Microscopy Techniques and Applications"
    ]
  ],
  "openalex": "W7213623256",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752067v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8m6xybs1ctnzg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:13Z",
      "native_id": "10.64898/2026.09.16.752067",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752067v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "EM3DFold: accurate de novo protein and nucleic acid model building for cryo-EM maps using language model-powered deep learning",
  "title_text": "EM3DFold: accurate de novo protein and nucleic acid model building for cryo-EM maps using language model-powered deep learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:13Z",
  "url": "https://research.draconitic.com/paper/8m6xybs1ctnzg",
  "venue": "bioRxiv",
  "versions": []
}