{
  "abstract": "Proteins interact with a wide range of biomolecular partners, yet identifying the interaction sites that mediate these events remains a major challenge for understanding cellular function and guiding drug discovery. Existing computational methods are typically designed for specific interaction classes and often rely on task-specific features. Here, we introduce PRIME-ISP, a structure-based framework for predicting interaction sites across diverse biomolecular interaction types using a unified architecture. PRIME-ISP represents proteins at five organizational levels, including solvent-excluded surfaces, atoms, residues, secondary-structure elements, and whole proteins, and integrates information across these scales through coupled bottom-up and top-down propagation. This hierarchical representation jointly captures local geometry and global structural context, enabling a single architecture to address chemically and geometrically distinct binding problems without task-specific modifications. We evaluate PRIME-ISP on protein, peptide, DNA, RNA, small-molecule, metal, and carbohydrate interaction-site prediction benchmarks. Across these tasks, PRIME-ISP consistently outperforms a recent unified sequence-based predictor and achieves performance comparable to specialized methods while requiring no interaction-specific features. These results demonstrate that hierarchical structural representations provide a flexible foundation for interaction-site prediction across diverse biomolecular interactions. Source code and data are publicly available at https://github.com/HySonLab/PRIME-ISP",
  "abstract_shown": true,
  "affiliations": [
    "University of Alabama at Birmingham, United States"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "V. T. D. Nguyen"
    },
    {
      "name": "J. K. Johnstone"
    },
    {
      "affs": [
        0
      ],
      "name": "T.-S. Hy"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/br3k8bdkzj0nr.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.18.759950",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.18.759950v1.full",
  "id": "br3k8bdkzj0nr",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.18.759950v1",
  "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|Protein Structure and Dynamics"
    ]
  ],
  "openalex": "W7223303267",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.18.759950v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/br3k8bdkzj0nr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:15Z",
      "native_id": "10.64898/2026.10.18.759950",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.18.759950v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Towards a Unified Framework for Protein Interaction Site Prediction Using Hierarchical Protein Representations",
  "title_text": "Towards a Unified Framework for Protein Interaction Site Prediction Using Hierarchical Protein Representations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:15Z",
  "url": "https://research.draconitic.com/paper/br3k8bdkzj0nr",
  "venue": "bioRxiv",
  "versions": []
}