{
  "abstract": "Protein function is shaped by cellular context, yet most protein representations and interaction maps remain context-agnostic. Here we present ProtScape, a multiscale graph-learning framework integrating global protein interactions, cell-type gene expression and protein language models to learn context-specific representations and infer interactomes across more than 200 cell types. ProtScape substantially outperforms existing approaches in interaction reconstruction, increasing the area under the precision-recall curve by 40 percentage points. Its predicted interactions were supported by held-out continuous STRING global evidence, while its representations recovered higher-order protein organisation. In patient-derived amyotrophic lateral sclerosis motor neurons, ProtScape revealed stage-specific network changes implicating RAB-dependent trafficking as a candidate early disease mechanism. In Parkinson's disease, it recovered clinically supported therapeutic targets from a proteome-wide search space 16-fold smaller than that required by competing representations. Together, ProtScape provides a scalable framework for translating context-specific interactome organisation into experimentally testable disease mechanisms and therapeutic hypotheses.",
  "abstract_shown": true,
  "affiliations": [
    "University of Bern"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "A. Thomas"
    },
    {
      "name": "L. Fournier"
    },
    {
      "name": "V. Jung"
    },
    {
      "name": "R. Patani"
    },
    {
      "name": "P. Frossard"
    },
    {
      "name": "R. Luisier"
    },
    {
      "affs": [
        0
      ],
      "name": "C. Vincent-Cuaz"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/crs2qa3j37pbt.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.08.750135",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.750135v1.full",
  "id": "crs2qa3j37pbt",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.750135v1",
  "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",
      "1312|Bioinformatics and Genomic Networks"
    ]
  ],
  "openalex": "W7213299210",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.750135v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/crs2qa3j37pbt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:22Z",
      "native_id": "10.64898/2026.09.08.750135",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.08.750135v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Resolving context-specific protein-protein interactomes forbiological discovery and therapeutic target prioritisation",
  "title_text": "Resolving context-specific protein-protein interactomes forbiological discovery and therapeutic target prioritisation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:22Z",
  "url": "https://research.draconitic.com/paper/crs2qa3j37pbt",
  "venue": "bioRxiv",
  "versions": []
}