Effectiveness and efficiency: label-aware hierarchical subgraph learning for protein-protein interaction

Abstract

AbstractProtein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis. Existing deep learning methods for PPIs prediction, including graph neural networks (GNNs), have been widely employed as the solutions, while they usually suffer from performance degradation under complex real-world scenarios. We claim that the topological shortcut is one of the key problems contributing negatively to the performance, according to our analysis. By modeling the PPIs as a graph with protein as nodes and interactions as edge types, the prevailing models tend to learn the pattern of nodes’ degrees rather than intrinsic sequence-structure profiles, leading to the problem termed topological shortcut. In addition, with the emergence of high-throughput experimental methods such as mass spectrometry and protein chip technology, the amount of available PPI data is exploding. The huge data growth leads to intensive computational costs and challenges computing devices, causing infeasibility in practice. To address the discussed problems, we propose alabel-aware hierarchical subgraphlearning method (laruGL-PPI) that can effectively infer PPIs while being both interpretable and generalizable. Specifically, we introduced edge-based subgraph sampling to effectively alleviate the problems of topological shortcuts and high computing costs. Besides, the inner-outer connections of PPIs are modeled as a hierarchical graph, together with the dependencies between interaction types constructed by a label graph. Extensive experiments on PPI datasets of different scales demonstrate that laruGL-PPI outperforms state-of-the-art PPI prediction methods, particularly in the testing of unseen proteins. Also, our model can recognize crucial sites of proteins, such as surface sites for binding and active sites for catalysis.