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<p>Accurate prediction of protein–protein interaction (PPI) sites is fundamental to elucidating cellular mechanisms and advancing genomics. However, prevailing graph neural networks are constrained by two key limitations: they often neglect latent correlations between distinct protein graphs and oversimplify neighborhood feature aggregation using rudimentary statistics, thereby discarding vital distributional information. Here, we present MED-PPIS, a novel framework that addresses these challenges through a synergistic integration of architectural innovations. Our model uniquely combines an mLSTM-based matrix memory for capturing long-range sequential dependencies with a multi-order moment GNN that faithfully characterizes complex feature distributions. This is complemented by a graph external attention mechanism to learn universal structural motifs across proteins and a dual-axis attention architecture for efficient, multi-scale feature extraction. Compared to the strongest baseline on the Test_60 dataset, it achieves significant improvements across key metrics, including a 2.1% increase in the area under the precision-recall curve (AUPRC), 1.2% in the area under the receiver operating characteristic curve (AUROC), and 2.3% in F1-score. By providing superior predictive accuracy, our model offers a powerful transparent tool for dissecting the intricate landscapes of protein interactions, paving the way for new biological insights and therapeutic strategies.</p> Graphical Abstract <p></p>

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MED-PPIS: Multi-order Moments External Graph Attention Network with Dual-Axis Attention for Protein–Protein Interaction Site Prediction

  • 期刊论文

  • 发表日期：

    2025年11月13日

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  • Dangguo Shao,
  • Yuyang Zou,
  • Lei Ma,
  • Sanli Yi

摘要

Accurate prediction of protein–protein interaction (PPI) sites is fundamental to elucidating cellular mechanisms and advancing genomics. However, prevailing graph neural networks are constrained by two key limitations: they often neglect latent correlations between distinct protein graphs and oversimplify neighborhood feature aggregation using rudimentary statistics, thereby discarding vital distributional information. Here, we present MED-PPIS, a novel framework that addresses these challenges through a synergistic integration of architectural innovations. Our model uniquely combines an mLSTM-based matrix memory for capturing long-range sequential dependencies with a multi-order moment GNN that faithfully characterizes complex feature distributions. This is complemented by a graph external attention mechanism to learn universal structural motifs across proteins and a dual-axis attention architecture for efficient, multi-scale feature extraction. Compared to the strongest baseline on the Test_60 dataset, it achieves significant improvements across key metrics, including a 2.1% increase in the area under the precision-recall curve (AUPRC), 1.2% in the area under the receiver operating characteristic curve (AUROC), and 2.3% in F1-score. By providing superior predictive accuracy, our model offers a powerful transparent tool for dissecting the intricate landscapes of protein interactions, paving the way for new biological insights and therapeutic strategies.

Graphical Abstract