标题： 通过多任务图结构学习提取蛋白质间相互作用 显示英文标题

标题： Extracting Inter-Protein Interactions Via Multitasking Graph Structure Learning

摘要： 识别蛋白质-蛋白质相互作用（PPI）对于深入了解细胞内的许多生物过程具有重要意义，并在药物开发和疾病治疗等领域具有重要的指导价值。 目前，大多数PPI预测方法主要关注蛋白质序列的研究，忽视了蛋白质内部结构的关键作用。 本文提出了一种名为MgslaPPI的新颖PPI预测方法，该方法利用图注意力机制挖掘蛋白质结构信息，并通过多任务学习策略增强蛋白质编码器的表达能力。 具体而言，我们将端到端的PPI预测过程分解为两个阶段：氨基酸残基重建（A2RR）和蛋白质相互作用预测（PIP）。 在A2RR阶段，我们采用基于图注意力的残基重建方法来探索蛋白质的内部关系和特征。 在PIP阶段，除了基本的相互作用预测任务外，我们引入了两个辅助任务，即蛋白质特征重建（PFR）和掩码相互作用预测（MIP）。 PFR任务旨在重建PIP阶段中蛋白质的表示，而MIP任务则使用部分掩码的蛋白质特征进行PPI预测，两者协同工作以促使MgslaPPI捕获更多有用信息。 实验结果表明，在各种数据划分方案下，MgslaPPI显著优于现有的最先进方法。 显示英文摘要

摘要： Identifying protein-protein interactions (PPI) is crucial for gaining in-depth insights into numerous biological processes within cells and holds significant guiding value in areas such as drug development and disease treatment. Currently, most PPI prediction methods focus primarily on the study of protein sequences, neglecting the critical role of the internal structure of proteins. This paper proposes a novel PPI prediction method named MgslaPPI, which utilizes graph attention to mine protein structural information and enhances the expressive power of the protein encoder through multitask learning strategy. Specifically, we decompose the end-to-end PPI prediction process into two stages: amino acid residue reconstruction (A2RR) and protein interaction prediction (PIP). In the A2RR stage, we employ a graph attention-based residue reconstruction method to explore the internal relationships and features of proteins. In the PIP stage, in addition to the basic interaction prediction task, we introduce two auxiliary tasks, i.e., protein feature reconstruction (PFR) and masked interaction prediction (MIP). The PFR task aims to reconstruct the representation of proteins in the PIP stage, while the MIP task uses partially masked protein features for PPI prediction, with both working in concert to prompt MgslaPPI to capture more useful information. Experimental results demonstrate that MgslaPPI significantly outperforms existing state-of-the-art methods under various data partitioning schemes.