{
  "abstract": "Protein-RNA interactions regulate diverse biological processes and are increasingly exploited in therapeutic RNA discovery, but accurate inferences of nucleotide preferences and reliable structure prediction remain challenging. Here, we present PRIS, a unified structure-based deep-learning framework that combines two complementary components: PRISeq for nucleotide probability estimation at each RNA position and PRIScore for residue-nucleotide distance prediction to discriminate native-like from incorrect poses. Both share a feature extractor that integrates an Anti-Symmetric Graph Attention Network (A-GAT) with sparse k-Maximum Inner Product (k-MPI) attention to capture long-range interactions across large graphs. PRIScore improves the selection of native-like protein-RNA predictions generated by AlphaFold3, achieving a top-1 success rate of 81.91% on a docking benchmark, compared to 79.26% for AlphaFold3. The selected structures are then fed into PRISeq, which infers position-specific binding preferences and screens RNA libraries. On a PWM benchmark, PRISeq achieved a mean absolute error (MAE) of 0.75, outperforming FoldX, Rosetta-based scoring functions, and NA-MPNN. In virtual screening against MS2 protein, PRISeq screens 129,248 RNA hairpins within 11.95 seconds, achieving the highest EF0.5% of 14.40, approximately double the best baseline. PRIS also effectively enriches active aptamers against NELF-E and GFP while preserving sequence diversity. By integrating structure selection with binding-preference inference, PRIS provides an efficient framework for large-scale RNA library screening and aptamer design.",
  "abstract_shown": true,
  "affiliations": [
    "Zhejiang University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "y. zhao"
    },
    {
      "name": "j. han"
    },
    {
      "name": "j. wang"
    },
    {
      "name": "j. chu"
    },
    {
      "name": "y. kang"
    },
    {
      "affs": [
        0
      ],
      "name": "t. hou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8cqf1rm0zxgpk.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.07.749737",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.07.749737v1.full",
  "id": "8cqf1rm0zxgpk",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.07.749737v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|RNA and protein synthesis mechanisms"
    ]
  ],
  "openalex": "W7212072712",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.07.749737v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8cqf1rm0zxgpk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:25Z",
      "native_id": "10.64898/2026.09.07.749737",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.07.749737v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "A Unified Structure-Based Deep-Learning Framework for High-Throughput Screening of Protein-Binding RNAs",
  "title_text": "A Unified Structure-Based Deep-Learning Framework for High-Throughput Screening of Protein-Binding RNAs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:25Z",
  "url": "https://research.draconitic.com/paper/8cqf1rm0zxgpk",
  "venue": "bioRxiv",
  "versions": []
}