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Dual-protein embedding-based graph model with dynamic attention for interaction prediction.

Shunpeng Pang¹, Mingjian Jiang², Shugang Zhang³

¹School of Computer Engineering, WeiFang University, 5147 East Dongfeng Road, Kuiwen District, Weifang, Shandong 261061, China.

Briefings in Bioinformatics

|October 1, 2025

Summary

This summary is machine-generated.

We developed DPEG, a novel computational method for predicting protein-protein interactions using only amino acid sequences. This approach effectively captures complex patterns, advancing biological research and drug discovery.

Keywords:

deep learning graph neural network protein–protein interactions

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Area of Science:

Bioinformatics
Computational Biology
Molecular Biology

Background:

Protein-protein interactions (PPIs) are crucial for biological functions.
Experimental PPI determination is expensive and time-consuming.
Computational methods, especially sequence-based ones, struggle with long-range dependencies and efficiency.

Purpose of the Study:

To introduce a novel sequence-based computational method for predicting PPIs.
To overcome limitations of existing sequence-based PPI prediction approaches.
To provide a scalable framework for proteome-wide PPI studies.

Main Methods:

Developed the Dual Protein Embedding-based Graph Model (DPEG).
Utilized ESM-2 for transforming protein sequences into residue-level graphs.
Implemented a module for handling variable sequence lengths and a gated attention mechanism for refining residue representations.
Employed a dynamic attention mechanism to prioritize critical motifs.

Main Results:

DPEG achieves state-of-the-art performance on four diverse PPI datasets.
Demonstrated strong cross-dataset generalizability.
Successfully predicted PPIs using only sequence data, without structural information.

Conclusions:

DPEG offers a robust and efficient solution for sequence-driven PPI prediction.
The model integrates deep sequence semantics with graph-based interaction modeling.
DPEG provides a biologically plausible and scalable framework for large-scale PPI analysis.