{
  "abstract": "Identifying the molecular components of cryoEM/cryoET maps of endogenous or poorly characterized complexes remains a major bottleneck. CryoID2 addresses this by integrating deep learning-based feature prediction with sequence prioritization and structural evaluation. Three 3D U-Nets extract structure classes, key-atom positions, and reduced residue/nucleotide types from the input map. These predictions support backbone tracing and the generation of short sequence queries that rank candidates from a user-supplied pool-using BLASTp for proteins and a maximin strategy for RNA. Corresponding PDB or AlphaFold models are then retrieved, rigidly fitted using predicted features and experimental density, and ranked by map agreement. Module-level tests show high-recall feature prediction and resolution-dependent atom clustering, while a 52-case benchmark recovers near-reference fitting quality. In practice, cryoID2 correctly identified PfA-M17 from a 166-protein mass-spectrometry pool and the unannotated Ca-raiA RNA from a genome-derived non-coding sequence set, demonstrating a unified sequence-plus-structure route to molecular identification in cryoEM maps.",
  "abstract_shown": true,
  "affiliations": [
    "University of California, Los Angeles"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Q. Xu"
    },
    {
      "name": "X. Yu"
    },
    {
      "name": "L. Wu"
    },
    {
      "name": "S. Feng"
    },
    {
      "affs": [
        0
      ],
      "name": "Z. H. Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/98wcx1v7ed5h7.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.03.764069",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.03.764069v1.full",
  "id": "98wcx1v7ed5h7",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.03.764069v1",
  "language": null,
  "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",
      "molecular biology"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.03.764069v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/98wcx1v7ed5h7.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:21:36Z",
      "native_id": "10.64898/2026.11.03.764069",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.03.764069v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "biorxiv:molecular biology",
      "subfield": "molecular-biology"
    }
  ],
  "title": "CryoID2: deep learning-enhanced identification of protein and RNA from cryoEM maps",
  "title_text": "CryoID2: deep learning-enhanced identification of protein and RNA from cryoEM maps",
  "type": "preprint",
  "updated_at": "2026-11-06T00:21:25Z",
  "url": "https://research.draconitic.com/paper/98wcx1v7ed5h7",
  "venue": "bioRxiv",
  "versions": []
}