Journal Article

RMDNet: RNA-aware dung beetle optimization-based multi-branch integration network for RNA–protein binding sites prediction

Cui, Feifei, Peng, Yunhui, Zhang, Jiangbo, Zhang, Qingchen, Yan, Shankai, Zhang, Zilong

2025

Overview

RNA-binding proteins (RBPs) play crucial roles in gene regulation. Their dysregulation has been increasingly linked to neurodegenerative diseases, liver cancer, and lung cancer. Although experimental methods like CLIP-seq accurately identify RNA–protein binding sites, they are time-consuming and costly. To address this, we propose RMDNet—a deep learning framework that integrates CNN, CNN-Transformer, and ResNet branches to capture features at multiple sequence scales. These features are fused with structural representations derived from RNA secondary structure graphs. The graphs are processed using a graph neural network with DiffPool. To optimize feature integration, we incorporate an improved dung beetle optimization algorithm, which adaptively assigns fusion weights during inference. Evaluations on the RBP-24 benchmark show that RMDNet outperforms state-of-the-art models including GraphProt, DeepRKE, and DeepDW across multiple metrics. On the RBP-31 dataset, it demonstrates strong generalization ability, while ablation studies on RBPsuite2.0 validate the contributions of individual modules. We assess biological interpretability by extracting candidate binding motifs from the first-layer CNN kernels. Several motifs closely match experimentally validated RBP motifs, confirming the model’s capacity to learn biologically meaningful patterns. A downstream case study on YTHDF1 focuses on analyzing interpretable spatial binding patterns, using a large-scale prediction dataset and CLIP-seq peak alignment. The results confirm that the model captures localized binding signals and spatial consistency with experimental annotations. Overall, RMDNet is a robust and interpretable tool for predicting RNA–protein binding sites. It has broad potential in disease mechanism research and therapeutic target discovery. The source code is available https://github.com/cskyan/RMDNet .

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Ablation / Algorithms / Animals / Annotations / Artificial neural networks / Beetles / Binding proteins / Binding Sites / Bioinformatics / Biomedical and Life Sciences / Case studies / Coleoptera / Computational biology / Computational Biology - methods / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Convolutional neural network / Datasets / Deep Learning / Dung / Dung beetle optimizer / Dung beetles / Experimental methods / Experiments / Feature fusion strategy / Gene regulation / Genetic aspects / Graph neural network / Graph neural networks / Graphs / Humans / Life Sciences / Liver cancer / Liver diseases / Lung cancer / Machine learning / Methods / Microarrays / Multi-branch deep learning network / Neural networks / Neural Networks, Computer / Neurodegenerative diseases / Nucleotide sequence / Optimization / Physiological aspects / Protein Binding / Protein structure / Proteins / Reproducibility / Ribonucleic acid / RNA / RNA - chemistry / RNA - metabolism / RNA sequencing / RNA-binding protein / RNA-Binding Proteins - chemistry / RNA-Binding Proteins - genetics / RNA-Binding Proteins - metabolism / RNA–protein binding sites / Secondary structure / Software / Source code / Therapeutic targets