{
  "abstract": "Motivation: Recent advances in biological foundation models have enabled new opportunities for data-driven molecular engineering. However, designing programmable RNA devices remains challenging because of the vast, nonlinear sequence-structure-function landscape. Here, we present Ernie-Switcher, a deep learning framework built upon the RNA foundation model (RNAErnie), comprising three coupled components: (i) integration of RNAErnie-derived sequence-phenotype representations for realistic design generation and filtering, (ii) characterization of RNA landscape navigability as a theoretical basis for sequence optimization, and (iii) incorporation of biophysical constraints to maintain optimization trajectories within biologically plausible bounds.\n\nResults: We validated Ernie-Switcher using RNA toehold switches. Theoretically, the observed lower bound on threshold navigability (86.4%) of the RNA landscape supports the theoretical feasibility of RNA switch sequence optimization. Experimentally, the optimized toehold switches achieved ON/OFF expression ratios of up to 35-fold in Escherichia coli. Collectively, RNAErnie-guided sequence optimization provides a scalable strategy for engineering high-performance RNA devices. To facilitate broad adoption, we deployed Ernie-Switcher as a freely accessible web platform.\n\nAvailability and implementation: The Python code and pre-trained models are available at https://github.com/zhutiblah/Ernie-Switcher and are archived on Zenodo at https://doi.org/10.5281/zenodo.21859277. The RNA toehold switch design platform is available at https://ernie-switcher.bioinformatics-syn.org/.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "Xin Lei",
      "orcid": "0009-0008-3124-7416"
    },
    {
      "name": "Runting Li"
    },
    {
      "name": "Yuzhong Liu"
    },
    {
      "name": "Jiesheng Huang"
    },
    {
      "name": "Yicheng Yang"
    },
    {
      "name": "Ruyuan Li"
    },
    {
      "name": "Shimin Liu"
    },
    {
      "name": "Pengxia Wang"
    },
    {
      "name": "Huaiguang Jiang"
    },
    {
      "name": "Wei Xiong",
      "orcid": "0000-0002-7046-4172"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5v1hak2wsrzfg.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag774",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13726693/",
  "id": "5v1hak2wsrzfg",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://doi.org/10.1093/bioinformatics/btag774",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "AcademicSubjects/SCI01060"
    ],
    [
      "heading",
      "Original Paper"
    ],
    [
      "heading",
      "Sequence Analysis"
    ],
    [
      "openalex",
      "1312|RNA and protein synthesis mechanisms"
    ]
  ],
  "openalex": "W7223661702",
  "pdf_url": null,
  "pmcid": "PMC13726693",
  "pmid": "42929764",
  "published": "2026-10-26",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/5v1hak2wsrzfg.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T12:22:59Z",
      "native_id": "PMC13726693",
      "source": "pmc",
      "url": "https://doi.org/10.1093/bioinformatics/btag774"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|RNA and protein synthesis mechanisms",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Ernie-Switcher: RNA foundation model-guided design of programmable RNA devices in a navigable landscape",
  "title_text": "Ernie-Switcher: RNA foundation model-guided design of programmable RNA devices in a navigable landscape",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T12:22:35Z",
  "url": "https://research.draconitic.com/paper/5v1hak2wsrzfg",
  "venue": "Bioinformatics",
  "versions": []
}