M2-PRNet: Multi-Scale and Multi-Modal Learning for Protein–RNA Binding Affinity Prediction

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ID: 325147
2026
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Abstract
MOTIVATION: Predicting protein-RNA binding affinity is crucial for understanding cellular regulation and advancing RNA-targeted drug discovery. However, this task remains challenging due to structural complexity, limited labeled data, and insufficient modeling of fine-grained interactions. RESULTS: We propose M2-PRNet, a multi-scale and multi-modal framework that integrates atom-level graphs, residue-level graphs, and tri-view molecular representations to capture complementary structural information. A cross-scale contrastive learning objective is introduced to align representations across different structural resolutions of the same complex. Under a clustering-based five-fold cross-validation setting on benchmark datasets, M2-PRNet achieves state-of-the-art performance. To further assess generalization under reduced sequence homology, we construct homology-aware RNA-cold, protein-cold, and dual-cold evaluations under a stricter 40% sequence identity threshold, where M2-PRNet maintains competitive performance. To account for conformational flexibility, we evaluate the model on MD150-1ns and an extended MD75-10ns subset, demonstrating stable performance under MD-derived structural perturbations. In addition, representative case studies suggest that M2-PRNet can highlight relevant RNA-binding regions and support preliminary discrimination between strong and weak binders when plausible complex structures are available. These results demonstrate the effectiveness of integrating multi-scale and multi-modal representations with cross-scale alignment for protein-RNA affinity prediction. AVAILABILITY AND IMPLEMENTATION: The source code and datasets for M2-PRNet are freely available at https://github.com/CSUBioGroup/M2-PRNet.
Reference Key
openalex_W7203646611 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Junkai Wang, Gang Luo, Yunsong Yang, Zhilin Zhu, Min Li
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag614
URL
https://doi.org/10.1093/bioinformatics/btag614
Keywords Keywords not found

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