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

DOI number:

10.1038/s41467-025-60048-1

Affiliation of Author(s):

School of Biomedical Engineering, Suzhou Institute for Advanced Research

Journal:

Nature Communications

Place of Publication:

London, UK

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.

First Author:

Heqin Zhu

Co-author:

Fenghe Tang,Quan Quan,Ke Chen

Indexed by:

Journal paper

Correspondence Author:

Peng Xiong,S. Kevin Zhou

Volume:

16

Issue:

1

Page Number:

5856

ISSN No.:

2041-1723

Translation or Not:

no

Date of Publication:

2025/07/01

Included Journals:

SCI

Links to published journals:

https://www.nature.com/articles/s41467-025-60048-1