Wang Xun, Han Tongyu, Feng Runqiu, Xia Zhijun, Wang Hanyu, Yu Wenqian, Dai Huanhuan, Song Haonan, Song Tao
Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, Shandong, China.
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf290.
Protein-protein interactions (PPIs) play a critical role in cellular functions, which are essential for maintaining the proper physiological state of organisms. Therefore, identifying PPI sites with high accuracy is crucial. Recently, graph neural networks (GNNs) have achieved significant progress in predicting PPI sites, but there is still potential for further enhancement. In this study, we introduce GTE-PPIS, an innovative PPI site predictor that utilizes two components: a graph transformer and an equivariant GNN, to collaboratively extract features. These extracted features are subsequently processed through a multilayer perceptron to generate the final predictions. Our experimental results show that GTE-PPIS consistently outperforms existing methods on multiple evaluation metrics across benchmark datasets, strongly supporting the effectiveness of our approach.
蛋白质-蛋白质相互作用（PPIs）在细胞功能中起着关键作用，而细胞功能对于维持生物体的正常生理状态至关重要。因此，高精度识别PPIs位点至关重要。最近，图神经网络（GNNs）在预测PPIs位点方面取得了显著进展，但仍有进一步提升的潜力。在本研究中，我们引入了GTE-PPIS，这是一种创新的PPIs位点预测器，它利用两个组件：图变换器和等变GNN，来协同提取特征。随后，这些提取的特征通过多层感知器进行处理以生成最终预测。我们的实验结果表明，在基准数据集的多个评估指标上，GTE-PPIS始终优于现有方法，有力地支持了我们方法的有效性。
