Authors
Yiming Xue, Xiaojian Liu, Weimin Zhu, Shengfan Wang, Hong-Bin Shen, Xiaoyong Pan
Published in
Nucleic acids research. Volume 54. Issue 17. Sep 07, 2026.
Abstract
While protein-RNA interactions are fundamental to post-transcriptional processes, achieving a holistic understanding of their regulatory logic remains challenging. Current computational models often treat binding affinity, interface mapping, and RNA design as isolated tasks, thereby failing to provide a unified perspective of the protein-RNA interactome. Here, we introduce ProRB, a unified sequence-based framework that jointly estimates protein-RNA binding affinity, predicts binding interfaces in proteins and RNAs, and generates protein-binding RNA sequences from protein sequences. By fusing protein and RNA embeddings from language models via adaptive cross-modal attention, ProRB learns contextual and relational features for predicting protein-RNA binding affinity and interface contacts, outperforming or achieving competitive performance compared to structure-based methods. Notably, its cross-attention maps reveal interpretable, motif-centric binding logic hidden in protein-RNA interactions. Building on this interpretability, ProRB enables computationally prioritized design of protein-binding RNA sequences with enhanced biophysical properties and functional motifs. By unifying the prediction, interpretation, and generation tasks, ProRB provides a scalable unified model for decoding the protein-RNA interaction and engineering motif-guided RNA therapeutics.
PMID:
42703973
Bibliographic data and abstract were imported from PubMed on 13 Oct 2026.
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