We present Interactome INSIDER, a tool to link genomic variant information with structural protein–protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.

我们介绍了 Interactome INSIDER，这是一种将基因组变异信息与结构蛋白质-蛋白质相互作用组联系起来的工具。该工具的基础是应用机器学习来预测 185,957 种蛋白质相互作用的蛋白质相互作用界面，这些蛋白质与人类和七种模型生物中以前未解决的界面相互作用，包括整个实验确定的人类二元相互作用组。预测的界面表现出与已知界面相似的功能特性，包括疾病突变和复发性癌症突变的富集。通过 2,164从头通过诱变实验，我们表明预测和已知界面残基的突变以相似的速度破坏相互作用，并且比预测界面之外的突变更频繁。为促进功能基因组研究，Interactome INSIDER (http://interactomeinsider.yulab.org) 使用户能够确定变体或疾病突变是否在各种分辨率的已知和预测交互界面中丰富。用户可以探索已知的人群变异、疾病突变和体细胞癌突变，或者他们可以为此目的上传他们自己的一组突变。