{
  "abstract": "Cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) can yield density maps whose molecular identities are unknown. Predicted-structure databases enable proteome-scale searches, but distinguishing the correct candidates remains challenging, particularly for intermediate-resolution maps without clear side-chain features. We developed CryoAssign, an automated framework that combines large-scale structure matching with learning-to-rank prioritization. CryoAssign searches intact density maps without prior segmentation and integrates complementary measures of map-model agreement. On held-out benchmark maps, CryoAssign robustly prioritized the correct candidates. It also identified proteins or narrowed the set of candidates in previously unassigned cryo-EM maps and recovered multiple components from complex cryo-EM and cryo-ET maps. Controlled low-pass filtering showed generally robust recovery across a broad range of intermediate resolutions. CryoAssign provides a scalable approach for identifying proteins from cryo-EM and cryo-ET density maps.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA",
    "Structural Biology Core, Mayo Clinic, Rochester, MN, USA"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Qi Hu",
      "orcid": "0000-0003-3647-8471"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Georges Mer",
      "orcid": "0000-0002-1900-1578"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/95etx4shcf47f.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.12.758677",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.12.758677v1.full",
  "id": "95etx4shcf47f",
  "issns": [
    "2692-8205"
  ],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.12.758677v1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "biophysics"
    ],
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "1315|Advanced Electron Microscopy Techniques and Applications"
    ]
  ],
  "openalex": "W7221697195",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.12.758677v1.full.pdf",
  "pmcid": "PMC13715899",
  "pmid": "42959064",
  "published": "2026-10-12",
  "publisher": "Cold Spring Harbor Laboratory Preprints",
  "ris": "https://research.draconitic.com/paper/95etx4shcf47f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:37Z",
      "native_id": "10.64898/2026.10.12.758677",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.12.758677v1"
    },
    {
      "first_seen": "2026-11-01T12:23:05Z",
      "native_id": "PMC13715899",
      "source": "pmc",
      "url": "https://doi.org/10.64898/2026.10.12.758677"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "biorxiv:biophysics",
      "subfield": "biophysics"
    }
  ],
  "title": "A learning-to-rank framework for large-scale identification of unknown proteins from cryo-EM and cryo-ET maps",
  "title_text": "A learning-to-rank framework for large-scale identification of unknown proteins from cryo-EM and cryo-ET maps",
  "type": "preprint",
  "updated_at": "2026-11-01T12:23:05Z",
  "url": "https://research.draconitic.com/paper/95etx4shcf47f",
  "venue": "bioRxiv",
  "versions": []
}