Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense. As the accuracy of multi-chain protein complex structure prediction improves, the challenge has shifted towards effectively navigating the vast complex universe to identify potential PPIs. Herein, we propose PPIretrieval, the first deep learning-based model for protein-protein interaction exploration, which leverages existing PPI data to effectively search for potential PPIs in an embedding space, capturing rich geometric and chemical information of protein surfaces. When provided with an unseen query protein with its associated binding site, PPIretrieval effectively identifies a potential binding partner along with its corresponding binding site in an embedding space, facilitating the formation of protein-protein complexes.

翻译：蛋白质-蛋白质相互作用（PPIs）在调控众多细胞功能中至关重要，包括信号转导、运输和免疫防御。随着多链蛋白质复合物结构预测准确性的提升，挑战已转向如何有效导航庞大的复合物空间以识别潜在的PPIs。在此，我们提出PPIretrieval，这是首个基于深度学习的蛋白质-蛋白质相互作用探索模型，它利用现有PPI数据在嵌入空间中有效搜索潜在PPIs，捕捉蛋白质表面丰富的几何和化学信息。当提供一个未见过的查询蛋白质及其相关结合位点时，PPIretrieval能有效在嵌入空间中识别潜在的结合伙伴及其对应的结合位点，从而促进蛋白质-蛋白质复合物的形成。