A Dual- Task Hierarchical Graph Attention Network for Protein-Protein interaction sites Prediction

Oras A. Hussein, Department Software, College of Information Technology, University of Babylon, Hilla, IraqFollow
Eman S. Al-Shamery, Department Software, College of Information Technology, University of Babylon, Hilla, IraqFollow

ORCID

Oras A. Hussein: https://orcid.org/0009-0008-7114-0170

Eman S. Al-Shamery: https://orcid.org/0000-0002-4783-2360

Article Type

Original Study

Abstract

In computational structural biology, it is still very hard to accurately find protein-protein interaction (PPI) sites and estimate how strong the interaction would be. In this research, we provide an innovative two-stage deep learning framework that combines residue-level graph representation learning with protein-level regression to achieve a thorough modeling of protein interactions. Protein structures first encoded as residue graphs, with nodes that stand for amino acids and edges that show how close they are to each other in space. To find binding residues, a deep residual Graph Attention Network v2 (GATv2) uses multi-head attention, residual connections, and Jumping Knowledge aggregation to collect long-range relationships and structural information at different scales. Using residue-level predictions, protein embeddings are created and put together to provide paired representations that show how similar and different two interacting proteins are. After that, these representations utilized to train a regression model that can predict continuous interaction strength ratings. The proposed model tested using a huge human PPI dataset that has 2,242 complexes. The proposed model performs very well at the residue level, with an AUROC of 0.9625, an AUPRC of 0.9149, an F1-score of 0.8192, and an MCC of 0.7674. The protein-level regression model also does a great job of predicting, with an RMSE of 0.2806, an MAE of 0.1635, and a R² of 0.6500 on the test set. It also has high correlation coefficients (Pearson = 0.8069, Spearman = 0.7500), which means that the predicted and true interaction strengths are very similar. In general, the proposed model is a single, scalable approach that connects predicting binding sites at the residue level with estimating interaction strength at the protein level. This gives us a better understanding of the structural processes that control PPI.

Keywords

Protein-protein interaction prediction, Graph neural networks, Graph attention networks, Residue graph, structural bioinformatics, Protein language models

How to Cite This Article

Hussein, Oras A. and Al-Shamery, Eman S. (2026) "A Dual- Task Hierarchical Graph Attention Network for Protein-Protein interaction sites Prediction," Journal of Intelligent Informatics, Networking, and Cybersecurity: Vol. 2 : Iss. 2 , Article 1.
Available at: https://doi.org/10.65445/3106-1192.1012