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

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Heqin Zhu, Fenghe Tang, Quan Quan, Ke Chen, Peng Xiong, S. Kevin Zhou

Published: 01 Jul 2025, Last Modified: 12 Nov 2025Nature CommunicationsEveryoneRevisionsCC BY-SA 4.0
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. Generalizable prediction of RNA secondary structure remains a challenge on unseen families. Here, the authors propose BPfold, which integrates a base pair motif library with thermodynamic energy into a deep learning framework, gaining superior accuracy and robustness over state-of-the-art methods.