Deep generalizable prediction of RNA secondary structure via base pair motif energy
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60048-1 |
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| author | Heqin Zhu Fenghe Tang Quan Quan Ke Chen Peng Xiong S. Kevin Zhou |
| author_facet | Heqin Zhu Fenghe Tang Quan Quan Ke Chen Peng Xiong S. Kevin Zhou |
| author_sort | Heqin Zhu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e72e90e187784a47a5cd0723cfe50987 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-e72e90e187784a47a5cd0723cfe509872025-08-20T03:37:37ZengNature PortfolioNature Communications2041-17232025-07-0116111310.1038/s41467-025-60048-1Deep generalizable prediction of RNA secondary structure via base pair motif energyHeqin Zhu0Fenghe Tang1Quan Quan2Ke Chen3Peng Xiong4S. Kevin Zhou5School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC)School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC)Key Laboratory of Intelligent Information Processing of Institute of Computing Technology, Chinese Academy of SciencesSchool of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC)School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC)School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC)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.https://doi.org/10.1038/s41467-025-60048-1 |
| spellingShingle | Heqin Zhu Fenghe Tang Quan Quan Ke Chen Peng Xiong S. Kevin Zhou Deep generalizable prediction of RNA secondary structure via base pair motif energy Nature Communications |
| title | Deep generalizable prediction of RNA secondary structure via base pair motif energy |
| title_full | Deep generalizable prediction of RNA secondary structure via base pair motif energy |
| title_fullStr | Deep generalizable prediction of RNA secondary structure via base pair motif energy |
| title_full_unstemmed | Deep generalizable prediction of RNA secondary structure via base pair motif energy |
| title_short | Deep generalizable prediction of RNA secondary structure via base pair motif energy |
| title_sort | deep generalizable prediction of rna secondary structure via base pair motif energy |
| url | https://doi.org/10.1038/s41467-025-60048-1 |
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