Precise prediction of hotspot residues in protein–RNA complexes using graph attention networks and pretrained protein language models

• Published in: Bioinformatics (Oxford, England) Vol. 41; no. Supplement_1; pp. i466 - i474
• Main Authors: Shen, Siyuan, Chen, Jie, Huang, Zhijian, Zhang, Yuanpeng, Fan, Ziyu, Kong, Yuting, Deng, Lei
• Format: Journal Article
• Language: English
• Published: England Oxford Publishing Limited (England) 01.07.2025; Oxford University Press
• Subjects: Algorithms; Amino acids; Availability; Biodiversity hot spots; Biological activity; Computational Biology - methods; Computer applications; Disease hot spots; Drug development; Experimental methods; Macromolecular Sequence, Structure, and Function; Models, Molecular; Modules; Nucleotide sequence; Predictions; Protein Binding; Protein structure; Proteins; Recombination hot spots; Residues; Ribonucleic acid; RNA; RNA - chemistry; RNA - metabolism; RNA-Binding Proteins - chemistry; RNA-Binding Proteins - metabolism; Software; Source code
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/btaf197

Protein-RNA interactions play a pivotal role in biological processes and disease mechanisms, with hotspot residues being critical for targeted drug design. Traditional experimental methods for identifying hotspot residues are often inefficient and expensive. Moreover, many existing prediction methods rely heavily on high-resolution structural data, which may not always be available. Consequently, there is an urgent need for an accurate and efficient sequence-based computational approach for predicting hotspot residues in protein-RNA complexes. In this study, we introduce DeepHotResi, a sequence-based computational method designed to predict hotspot residues in protein-RNA complexes. DeepHotResi leverages a pretrained protein language model to predict protein structure and generate an amino acid contact map. To enhance feature representation, DeepHotResi integrates the Squeeze-and-Excitation (SE) module, which processes diverse amino acid-level features. Next, it constructs an amino acid feature network from the contact map and SE-module-derived features. Finally, DeepHotResi employs a graph attention network to model hotspot residue prediction as a graph node classification task. Experimental results demonstrate that DeepHotResi outperforms state-of-the-art methods, effectively identifying hotspot residues in protein-RNA complexes with superior accuracy on the test set. The source code and dataset are available at https://github.com/Q1DT/DeepHotResi.