Deep generalizable prediction of RNA secondary structure via base pair motif energy

Heqin Zhu, Fenghe Tang, Quan Quan, Ke Chen, Peng Xiong () and S. Kevin Zhou ()
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Heqin Zhu: University of Science and Technology of China (USTC)
Fenghe Tang: University of Science and Technology of China (USTC)
Quan Quan: Chinese Academy of Sciences
Ke Chen: University of Science and Technology of China (USTC)
Peng Xiong: University of Science and Technology of China (USTC)
S. Kevin Zhou: University of Science and Technology of China (USTC)

Nature Communications, 2025, vol. 16, issue 1, 1-13

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.

Date: 2025
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DOI: 10.1038/s41467-025-60048-1

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