Protein–protein interactions play an important role in various biological processes. Interaction among proteins has a wide range of applications. Therefore, the correct identification of protein–protein interactions sites is crucial. In this paper, we propose a novel predictor for protein–protein interactions sites, AGF-PPIS, where we utilize a multi-head self-attention mechanism (introducing a graph structure), graph convolutional network, and feed-forward neural network. We use the Euclidean distance between each protein residue to generate the corresponding protein graph as the input of AGF-PPIS. On the independent test dataset Test_60, AGF-PPIS achieves superior performance over comparative methods in terms of seven different evaluation metrics (ACC, precision, recall, F1-score, MCC, AUROC, AUPRC), which fully demonstrates the validity and superiority of the proposed AGF-PPIS model. The source codes and the steps for usage of AGF-PPIS are available at https://github.com/fxh1001/AGF-PPIS.

中文翻译：

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AGF-PPIS：基于注意力机制和图卷积网络的蛋白质-蛋白质相互作用位点预测器

蛋白质-蛋白质相互作用在各种生物过程中发挥着重要作用。蛋白质之间的相互作用具有广泛的应用。因此，正确识别蛋白质-蛋白质相互作用位点至关重要。在本文中，我们提出了一种新型的蛋白质-蛋白质相互作用位点预测器 AGF-PPIS，其中我们利用多头自注意力机制（引入图结构）、图卷积网络和前馈神经网络。我们利用每个蛋白质残基之间的欧氏距离生成相应的蛋白质图作为AGF-PPIS的输入。在独立测试数据集Test_60上，AGF-PPIS在七个不同的评价指标（ACC、精度、召回率、F1-score、MCC、AUROC、AUPRC）方面均取得了优于对比方法的性能，充分证明了该方法的有效性和优越性。提出了AGF-PPIS模型。 AGF-PPIS 的源代码和使用步骤可在 https://github.com/fxh1001/AGF-PPIS 获取。