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 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 gr

SUBMITTER: Zhu H 

PROVIDER: S-EPMC12216785 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature