ProRB: a structure-free unified framework for joint prediction and design of protein–RNA interactions

Clicks: 19
ID: 328027
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #222 of 1,229 articles by views in Nucleic Acids Research

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,229 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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.
Reference Key
openalex_W7211913330 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yiming Xue, Xiaojian Liu, Weimin Zhu, Shengfan Wang, Hong‐Bin Shen, Xiaoyong Pan
Journal Nucleic Acids Research
Year 2026
DOI
10.1093/nar/gkag870
URL
https://doi.org/10.1093/nar/gkag870
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.