Deep generalizable prediction of RNA secondary structure via base pair motif energy
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Abstract
Abstract Deep learning methods have demonstrated great performance for RNA secondary structure prediction. However, generalizability is a common unsolved issue on unseen out-of-distribution RNA families, which hinders further improvement of the accuracy and robustness of deep learning methods. Here we construct a base pair motif library that enumerates the complete space of the locally adjacent three-neighbor base pair and records the thermodynamic energy of corresponding base pair motifs through de novo modeling of tertiary structures, and we further develop a deep learning approach for RNA secondary structure prediction, named BPfold, which learns relationship between RNA sequence and the energy map of base pair motif. Experiments on sequence-wise and family-wise datasets have demonstrated the great superiority of BPfold compared to other state-of-the-art approaches in accuracy and generalizability. We hope this work contributes to integrating physical priors and deep learning methods for the further discovery of RNA structures and functionalities.
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- Authors
- Zhu, H., Tang, F., Quan, Q., Chen, K., Xiong, P., & Zhou, S. K.
- Affiliations
- University of Science and Technology of China (CN)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Xiong, Peng
- Zhou, S. Kevin
- Venue
- Nature Communications
- Published
- 2025-07-01
- Volume
- 16
- Issue
- 1
- Pages
- 5856-5856
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC12216785
- OpenAlex
- W4411882193
- Cited by
- 17
- References
- 66
- Citations by year
- 2026: 9 · 2025: 8
- FWCI
- 5.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (32370581, 62271465)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Keywords
- RNA secondary structure prediction, thermodynamic energy, out-of-distribution generalization, RNA tertiary structure, physical priors, sequence-structure relationship, RNA families
- MeSH terms
- Deep Learning, Nucleic Acid Conformation, RNA, Thermodynamics, Computational Biology, Base Pairing, Nucleotide Motifs
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