{
  "abstract": "In recent years, deep learning has revolutionized protein structure prediction, achieving remarkable speed and accuracy. RNA structure prediction, however, has lagged behind. Although several methods have shown moderate success in predicting RNA secondary and tertiary structures, none have reached the accuracy observed with contemporary protein models. The lack of success of these RNA structure prediction models has been proposed to be due to limited high-quality structural information that can be used as training data. To probe this proposed limitation, we developed a large and diverse data set comprising paired RNA sequences and their corresponding secondary structures. We assessed the utility of this enhanced data set by retraining a deep-learning model, SincFold. We find that SincFold exhibited improved performance on a set of previously unseen RNA families, enhancing its capability to predict accurate de novo RNA secondary structures. We additionally implemented Lyra-TransPred, which achieved the highest mean F1 and MCC among the evaluated models while requiring substantially less training time per epoch. The RNASSTR data set provides a substantial advance for RNA structure modeling, laying a strong foundation for the development of future RNA secondary structure prediction algorithms.",
  "abstract_shown": true,
  "affiliations": [
    "Innovative Genomics Institute, University of California, Berkeley, California 94720, USA",
    "California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, California 94720, USA",
    "Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California 94720, USA",
    "Department of Computer Science, University of California, Berkeley, Berkeley, California 94720, USA",
    "Department of Bioengineering, University of California, Berkeley, Berkeley, California 94720, USA",
    "Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, California 94720, USA",
    "Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, California 94720, USA",
    "Department of Chemistry, University of California, Berkeley, Berkeley, California 94720, USA",
    "MBIB Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Conner J. Langeberg",
      "orcid": "0000-0002-5609-3758"
    },
    {
      "affs": [
        3
      ],
      "name": "Taehan Kim"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Roma Nagle"
    },
    {
      "affs": [
        3
      ],
      "name": "Agni Rajinikanth"
    },
    {
      "affs": [
        2
      ],
      "name": "Charlotte Meredith"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Dimple Amitha Garuadapuri"
    },
    {
      "affs": [
        0,
        1,
        2,
        6,
        7,
        8
      ],
      "name": "Jennifer A. Doudna",
      "orcid": "0000-0001-9161-999X"
    },
    {
      "affs": [
        0,
        2,
        7,
        8
      ],
      "name": "Jamie H.D. Cate",
      "orcid": "0000-0001-5965-7902"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4p2scm2wckpbb.bib",
  "cited_by": 3,
  "doi": "10.1261/rna.081259.126",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13674262/",
  "id": "4p2scm2wckpbb",
  "issns": [
    "1355-8382",
    "1469-9001"
  ],
  "landing_url": "https://doi.org/10.1261/rna.081259.126",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "1312|RNA and protein synthesis mechanisms"
    ]
  ],
  "openalex": "W4410074781",
  "pdf_url": "https://www.biorxiv.org/content/biorxiv/early/2025/05/03/2025.05.03.652028.full.pdf",
  "pmcid": "PMC13674262",
  "pmid": "42680564",
  "published": "2026-11-01",
  "publisher": "Cold Spring Harbor Laboratory Press",
  "ris": "https://research.draconitic.com/paper/4p2scm2wckpbb.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:23:36Z",
      "native_id": "PMC13674262",
      "source": "pmc",
      "url": "https://doi.org/10.1261/rna.081259.126"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|RNA and protein synthesis mechanisms",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Improving RNA secondary structure prediction through expanded training data",
  "title_text": "Improving RNA secondary structure prediction through expanded training data",
  "type": "peer-reviewed",
  "updated_at": "2026-11-04T12:21:16Z",
  "url": "https://research.draconitic.com/paper/4p2scm2wckpbb",
  "venue": "RNA",
  "versions": []
}