{
  "abstract": "Designing RNAs with prescribed structures is central to RNA engineering. Backbone-first approaches generate geometry before sequence assignment, yet how to transfer relational information to sequence design remains unclear. We introduce RiboCouple, which infers a probabilistic residue-pair state from backbone geometry, recycles pair probabilities through a second pass and transfers them to sequence decoding as soft attention biases. On 550 generated backbones, pair-type decoding achieved joint refolding validity of 45.1 percent versus 33.5 percent for a matched topology-free decoder. In another 110-backbone cohort, replacing aligned pair states with statistics-matched donor states reduced validity from 46.4 percent to 21.4 percent, demonstrating that decoding depends on backbone-aligned relational information. Before external refolding, pair-confidence ranking enriched validity to 59.6 percent in the top 248 and transferred to other generators; on a 94-chain temporally held-out native-backbone benchmark, pair-coupled decoding improved secondary-structure agreement. These findings establish backbone-inferred interaction topology as an intermediate representation connecting backbone generation, sequence decoding, and candidate prioritization.",
  "abstract_shown": true,
  "affiliations": [
    "School of Engineering, Westlake University, Hangzhou, China"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Z. Ma"
    },
    {
      "affs": [
        0
      ],
      "name": "Y. Zheng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/faw8zp63jwsx9.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.15.759394",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.15.759394v1.full",
  "id": "faw8zp63jwsx9",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.15.759394v1",
  "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|RNA and protein synthesis mechanisms"
    ]
  ],
  "openalex": "W7222968928",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.15.759394v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/faw8zp63jwsx9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:24Z",
      "native_id": "10.64898/2026.10.15.759394",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.15.759394v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "RiboBridge couples RNA backbone generation to sequence design through a learned interaction topology",
  "title_text": "RiboBridge couples RNA backbone generation to sequence design through a learned interaction topology",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:38Z",
  "url": "https://research.draconitic.com/paper/faw8zp63jwsx9",
  "venue": "bioRxiv",
  "versions": []
}