Skip to content
Log in
Open access

M2-PRNet: multi-scale and multi-modal learning for protein–RNA binding affinity prediction

Junkai Wang G. Luo Yun-Song Yang Zhi-Lin Zhu Min Li
Aug 2026 · Bioinformatics · Vol 42 · 0 citations · 49 references
Medicine

TL;DR

The results demonstrate the effectiveness of integrating multi-scale and multi-modal representations with cross-scale alignment for protein–RNA affinity prediction, and 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.

Abstract

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.

DOI PubMed Semantic Scholar
Read PDF

Similar papers

Aug 2026

CoCoBind: Consistency-Contrastive Multitask Learning for RNA–Ligand Interaction and Binding Site Prediction

CoBind is presented, a multitask deep learning framework that jointly predicts RNA–compound interactions and nucleotide-level binding-site probabilities within a unified architecture and provides complementary nucleotide-level binding-site localization, supporting a site-aware view of RNA–ligand recognition under distribution shift.

Shihang Wang, Lin Wang, Wei Zhao et al. · 0 citations
Aug 2026

DiConSite: A Unified Topology-Adaptive Architecture for Protein Binding Site Prediction Across Ligand Modalities.

By combining protein language model embeddings with topology-adaptive geometric reasoning, DiConSite offers a reusable framework for residue-level protein interaction analysis and achieves consistently strong and often best-performing results, while improving robustness to structural uncertainty and cross-modal variation.

Shou-Zhi Chen, Zhenchao Tang, Linlin You et al. · 1 citation
Open access Aug 2026

Interpretable multilevel interaction modeling for robust protein–protein affinity

MIRAGE provides an interpretable and robust framework for structure-aware prediction, with potential applications in protein engineering and drug design, and explicitly modeling multi-level interactions is important for accurately capturing the determinants of binding affinity.

Shiwei Wu, Haoliang Liu, Zepeng Huang et al. · 0 citations
Review Jul 2026

From binary labels to dynamic landscapes: The evolving computational prediction of protein-RNA interactions through tasks and deep learning paradigms.

Protein-RNA interactions (RPIs) stand for the central process in post-transcriptional regulation and have catalyzed a fast proliferation of computational approaches in recent years. Adopting a task-oriented classification method, RPIs calculation prediction schemes proposed over the period 2010-2025 fall into five primary categories: RNA-binding protein (RBP) classification, RPIs prediction, binding site and binding profile modeling on RNA, residue-level RNA-binding interface prediction on proteins, and quantitative estimation of binding affinity and mutation effects. This study reviews the methodological evolution from conventional machine learning to deep learning, graph neural networks and large-scale pre-trained language models, and compares their differences in data preparation, evaluation protocols and generalization behavior. Particular emphasis is placed on recent advances in structure-aware and condition-aware models, as well as learning in low-data regimes. Finally, the study outlines practical recommendations for field-wide benchmarking and looks ahead to the integration with spatial omics and the development of dynamic, generative landscapes of RPIs to better empower biomedical research.

Xinyu Li, Qianmao Wen, Zilong Zhang et al. · 0 citations
Aug 2026

MSIGR-PLA: Integrating Multi-Scale Interaction and Global Representations for Protein-Ligand Affinity Prediction.

The proposed MSIGR-PLA is an integrative framework that integrates local multi-scale interaction features with global protein-ligand representations to improve the accuracy of PLA prediction and consistently outperforms existing methods on four benchmark datasets.

Hangchen Zhang, Haoran Chen, Chang Liu et al. · 0 citations