TY - JOUR T1 - Interactome INSIDER: a multi-scale structural interactome browser for genomic studies JF - bioRxiv DO - 10.1101/126862 SP - 126862 AU - Michael J. Meyer AU - Juan Felipe Beltrán AU - Siqi Liang AU - Robert Fragoza AU - Aaron Rumack AU - Jin Liang AU - Xiaomu Wei AU - Haiyuan Yu Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/04/12/126862.abstract N2 - Protein interactions underlie nearly all known cellular function, making knowledge of their binding conformations paramount to understanding the physical workings of the cell. Studying binding conformations has allowed scientists to explore some of the mechanistic underpinnings of disease caused by disruption of protein interactions. However, since experimentally determined interaction structures are only available for a small fraction of the known interactome such inquiry has largely excluded functional genomic studies of the human interactome and broad observations of the inner workings of disease. Here we present Interactome INSIDER, an information center for genomic studies using the first full-interactome map of human interaction interfaces. We applied a new, unified framework to predict protein interaction interfaces for 184,605 protein interactions with previously unresolved interfaces in human and 7 model organisms, including the entire experimentally determined human binary interactome. We find that predicted interfaces share several known functional properties of interfaces, including an enrichment for disease mutations and recurrent cancer mutations, suggesting their applicability to functional genomic studies. We also performed 2,164 de novo mutagenesis experiments and show that mutations of predicted interface residues disrupt interactions at a similar rate to known interface residues and at a much higher rate than mutations outside of predicted interfaces. To spur functional genomic studies in the human interactome, Interactome INSIDER (http://interactomeinsider.yulab.org) allows users to explore known population variants, disease mutations, and somatic cancer mutations, or upload their own set of mutations to find enrichment at the level of protein domains, residues, and 3D atomic clustering in known and predicted interaction interfaces. ER -