{
  "abstract": "Lacuna, an open-source Python tool for discovering cryptic binding pockets: sites that are absent or too small to detect in a protein's unbound structure and open only during conformational fluctuation. Most binding-site predictors score a single static structure, which is precisely the structure in which a cryptic site is invisible. Lacuna instead generates a conformational ensemble from any input structure, detects pockets independently in every conformer, clusters the detections into persistent sites across the ensemble, and ranks those sites with a model fitted on within-structure pairs. Ensemble generation is pluggable: normal mode analysis by default, with implicit-solvent molecular dynamics, Boltz-2 diffusion sampling, or a user-supplied ensemble as alternatives. On the designated test fold of CryptoBench, Lacuna recovers 55.6% of cryptic sites in its top five predictions with the zero-dependency default and 66.1% with an optional PLM-assisted ranker; pooling the geometric detector with an optional learned surface detector recovers 73.9% while raising the fraction of sites found from 68.5% to 86.4%, measured on the held-out fold at five conformers. It recovers 73%, 45% and 87% on the PocketMiner set, a curated set of literature apo/holo pairs, and COACH420 respectively. The default backend completes in a median of 2.6 seconds per chain on one CPU core, so ensemble-based pocket finding does not require a simulation budget. Every site carries a continuous crypticity score, and outputs are emitted as docking-ready Boltz YAML constraints, AutoDock Vina boxes, pseudoatom PDB files, and the generated conformational ensemble as a multi-model PDB. Lacuna is MIT licensed and available at https://github.com/mooreneural/lacuna and on PyPI as lacuna-pockets.",
  "abstract_shown": true,
  "affiliations": [
    "Texas A and M University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "C. Moore"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bmyyyksj88jhb.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.14.744956",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.14.744956v2.full",
  "id": "bmyyyksj88jhb",
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  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.14.744956v2",
  "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",
      "bioinformatics"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7203755971",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.14.744956v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bmyyyksj88jhb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:48Z",
      "native_id": "10.64898/2026.08.14.744956",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.14.744956v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Lacuna: Cryptic Binding Pocket Discovery via Conformational Ensemble Analysis",
  "title_text": "Lacuna: Cryptic Binding Pocket Discovery via Conformational Ensemble Analysis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:48Z",
  "url": "https://research.draconitic.com/paper/bmyyyksj88jhb",
  "venue": "bioRxiv",
  "versions": []
}