Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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使用深度图卷积网络进行结构感知蛋白质-蛋白质相互作用位点预测

动机 蛋白质-蛋白质相互作用 (PPI) 在许多生物过程中起着至关重要的作用，识别 PPI 位点是疾病机理理解和新药设计的重要一步。由于 PPI 站点识别的实验方法既昂贵又耗时，因此开发了许多计算方法作为筛选工具。然而，这些方法大多基于序列中的相邻特征，因此仅限于捕获空间信息。结果 我们提出了一种基于深度图的框架深度图卷积网络，用于 PPI 位点预测的蛋白质-蛋白质相互作用位点预测 (GraphPPIS)，其中 PPI 位点预测问题被转换为图节点分类任务，并通过深度学习使用初始残差和身份映射技术。我们表明，更深层次的架构（最多八层）与其他基于序列和基于结构的方法相比，在 AUPRC 和 MCC 上的性能分别提高了 12.5% 和 10.5% 以上。进一步的分析表明，即使做出假阳性预测，GraphPPIS 预测的相互作用位点在空间上更加聚集并且更接近本地相互作用位点。结果突出了捕获空间相邻残基对于相互作用位点预测的重要性。可用性和实施​​数据集、预计算特征、源代码以及 GraphPPIS 的预训练模型可在 https://github.com/biomed-AI/GraphPPIS 获得。GraphPPIS Web 服务器可在 https://biomed.nscc-gz.cn/apps/GraphPPIS 免费获得。