{
  "abstract": "Sequence-based models enable scalable protein-protein interaction (PPI) prediction, but whether high scores identify direct physical partners or broader biological compatibility remains unclear. Here we show that biological-context recognition and direct-partner specificity are separable prediction capabilities. We introduce ProtNegDB (http://protnegdb.bjmu.edu.cn/), an evidence-resolved resource distinguishing experimentally supported non-binding from coordinate-verified noncontact within PDB biological assemblies. Using context-matched benchmarks, we find that five sequence-based predictors show limited ability to distinguish direct partners from noncontacting members of the same assembly, whereas PLM-Interact readily ranks both above unrelated proteins. Representation analyses reveal that adaptation to PPI prediction can strengthen biological-context encoding without yielding transferable direct-contact specificity. AlphaFold3 shows greater, though still incomplete, direct-partner resolution, suggesting that explicit multimer modeling can help refine partner specificity. In PLM-Interact, negatives defined by reconstituted direct-binding, cellular binary/proximity and co-complex assays occupy distinct score regimes. Across independent datasets and multiple models, experimental direct non-binding and assembly noncontact further show reproducible, model-dependent scoring patterns. High sequence-based PPI scores should not be interpreted as direct-interface probabilities. Mechanistically interpretable evaluation instead requires explicit consideration of interaction-evidence semantics and partner specificity.",
  "abstract_shown": true,
  "affiliations": [
    "Peking University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "H. Cheng"
    },
    {
      "name": "C. Sun"
    },
    {
      "name": "C. Ye"
    },
    {
      "name": "C. Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "D. Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b9gb30xe24d5e.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.07.757219",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757219v1.full",
  "id": "b9gb30xe24d5e",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757219v1",
  "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",
      "1312|Machine Learning in Bioinformatics"
    ]
  ],
  "openalex": "W7221983787",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757219v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b9gb30xe24d5e.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:53Z",
      "native_id": "10.64898/2026.10.07.757219",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757219v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Evidence-resolved benchmarks separate biological context recognition from direct-partner specificity in protein interaction models",
  "title_text": "Evidence-resolved benchmarks separate biological context recognition from direct-partner specificity in protein interaction models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:53Z",
  "url": "https://research.draconitic.com/paper/b9gb30xe24d5e",
  "venue": "bioRxiv",
  "versions": []
}