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

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.

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.

The source code and datasets for M2-PRNet are freely available at https://github.com/CSUBioGroup/M2-PRNet.