To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein–protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein–protein interfaces and explore their impact on disease prognosis and drug responses. We identify 586 significant protein–protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from analysis of approximately 11,000 whole exomes across 33 cancer types and show significant associations of oncoPPIs with patient survival and drug responses. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

为了帮助将遗传发现转化为疾病病理学和治疗学发现，我们提出了一个集成深度学习框架，称为 PIONEER （Protein-protein InteractiOn iNtErfacE pRediction），该框架预测人类和其他七种常见模式生物中所有已知蛋白质相互作用的蛋白质结合伴侣特异性界面，以生成全面的结构知情蛋白质相互作用组。我们证明 PIONEER 优于现有的最先进方法，并通过实验验证了其预测。我们表明疾病相关突变在 PIONEER 预测的蛋白质-蛋白质界面中富集，并探索它们对疾病预后和药物反应的影响。我们从对 33 种癌症类型的大约 11,000 个全外显子组的分析中确定了 586 个富含 PIONEER 预测界面体细胞突变（称为 oncoPPI）的重要蛋白质-蛋白质相互作用 （PPI），并显示了 oncoPPI 与患者生存和药物反应的显着关联。PIONEER 作为 Web 服务器平台和软件包实现，可识别疾病相关等位基因的功能后果，并为多尺度交互组网络水平的精准医疗提供深度学习工具。