Affinity purification coupled with mass spectrometry (AP-MS) is now a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (e.g. proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. While the standard approach is to identify nonspecific interactions using one or more negative controls, most small-scale AP-MS studies do not capture a complete, accurate background protein set. Fortunately, negative controls are largely bait-independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the Contaminant Repository for Affinity Purification (the CRAPome) and describe the use of this resource to score protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely available online at www.crapome.org.

Polycomb group (PcG) proteins are major determinants of gene silencing and epigenetic memory in higher eukaryotes. Here, we systematically mapped the human PcG complexome using a robust affinity purification mass spectrometry approach. Our high-density protein interaction network uncovered a diverse range of PcG complexes. Moreover, our analysis identified PcG interactors linking them to the PcG system, thus providing insight into the molecular function of PcG complexes and mechanisms of recruitment to target genes. We identified two human PRC2 complexes and two PR-DUB deubiquitination complexes, which contain the O-linked N-acetylglucosamine transferase OGT1 and several transcription factors. Finally, genome-wide profiling of PR-DUB components indicated that the human PR-DUB and PRC1 complexes bind distinct sets of target genes, suggesting differential impact on cellular processes in mammals.

The characterization of all protein complexes of human cells under defined physiological conditions using affinity purification-mass spectrometry (AP-MS) is a highly desirable step in the quest to understand the phenotypic effects of genomic information. However, such a challenging goal has not yet been achieved, as it requires reproducibility of the experimental workflow and high data consistency across different studies and laboratories. We systematically investigated the reproducibility of a standardized AP-MS workflow by performing a rigorous interlaboratory comparative analysis of the interactomes of 32 human kinases. We show that it is possible to achieve high interlaboratory reproducibility of this standardized workflow despite differences in mass spectrometry configurations and subtle sample preparation-related variations and that combination of independent data sets improves the approach sensitivity, resulting in even more-detailed networks. Our analysis demonstrates the feasibility of obtaining a high-quality map of the human protein interactome with a multilaboratory project.

T cell antigen receptor (TCR)-mediated T cell activation requires the interaction of dozens of proteins. We used quantitative mass spectrometry and activated primary CD4 + T cells from mice in which a tag for affinity purification was knocked into several genes to determine the composition and dynamics of multiprotein complexes forming around the kinase Zap70 and the adaptors Lat and SLP-76. Most of the 112 high confidence time-resolved protein interactions we observed were novel. The CD6 surface receptor was found capable of initiating its own signaling pathway by recruiting SLP-76 and Vav1, irrespective of the presence of Lat. Our findings provide a more complete model of TCR signaling in which CD6 constitutes a signaling hub contributing to TCR signal diversification.

The understanding of complex biological systems is still hampered by limited knowledge of biologically relevant quaternary protein structures. Here, we demonstrate quaternary structure determination in biological samples using a combination of chemical cross-linking, high-resolution mass spectrometry and high-accuracy protein structure modeling. This approach, termed targeted cross-linking mass spectrometry (TX-MS), relies on computational structural models to score sets of targeted cross-linked peptide signals acquired using a combination of mass spectrometry acquisition techniques. We demonstrate the utility of TX-MS by creating a high-resolution quaternary model of a 1.8 MDa protein complex composed of a pathogen surface protein and ten human plasma proteins. The model is based on a dense network of cross-link distance constraints obtained directly in a mixture of human plasma and live bacteria. These results demonstrate that TX-MS can increase the applicability of flexible backbone docking algorithms to large protein complexes by providing rich cross-link distance information from complex biological samples.