{
  "abstract": "Cryo-EM maps frequently suffer from weak structural signals, spatially variable quality, and noise, complicating interpretation and downstream modeling. Existing post-processing approaches treat density enhancement, quality assessment, and masking as separate tasks, often amplifying residual noise or introducing local artifacts. Here, we present CryoCodex, a unified framework that jointly performs density enhancement, local quality estimation, and molecular masking. CryoCodex introduces a multi-head, multi-scale vector-quantization module that projects encoder-derived latent features onto a shared codebook of structural prototypes. By imposing discrete constraints across feature subspaces and spatial scales, the codebook suppresses stochastic noise while preserving recurrent structural patterns. On a non-redundant test set of 126 experimental maps spanning 3-8 [A], CryoCodex outperformed four representative methods, achieving a mean map-model FSC-0.5 of 3.93 [A] versus 4.62 [A] for EMReady2 and 6.87 [A] for the deposited maps. Downstream automated de novo model building with Phenix further confirmed the quality of the CryoCodex-enhanced maps. These gains extended to maps containing nucleic acids, where CryoCodex produced the best map-model agreement and improved model building. Half-map cross-validation indicated that these improvements are anchored to experimental data rather than driven by half-map-specific noise or structural hallucination. CryoCodex also delivered reliable local quality estimates and molecular masks within the same framework. Together, these results establish CryoCodex as a practical tool for cryo-EM map post-processing and structural determination in challenging cases.",
  "abstract_shown": true,
  "affiliations": [
    "MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China."
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "B. Cheng"
    },
    {
      "name": "B. Su"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/01mvccw1tyaaf.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.30.755675",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.30.755675v1.full",
  "id": "01mvccw1tyaaf",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.30.755675v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1315|Advanced Electron Microscopy Techniques and Applications"
    ]
  ],
  "openalex": "W7218652365",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.30.755675v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/01mvccw1tyaaf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:39Z",
      "native_id": "10.64898/2026.09.30.755675",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.30.755675v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "CryoCodex: learning discrete structural representations for cryo-EM map post-processing",
  "title_text": "CryoCodex: learning discrete structural representations for cryo-EM map post-processing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:39Z",
  "url": "https://research.draconitic.com/paper/01mvccw1tyaaf",
  "venue": "bioRxiv",
  "versions": []
}