The Contaminant Repository for Affinity Purification (CRAPome) is a database of annotated negative control-data that can be used for filtering out nonspecific interactions in affinity purification-mass spectrometry experiments. Affinity purification coupled with mass spectrometry (AP-MS) is 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 (for example, proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. The standard approach is to identify nonspecific interactions using one or more negative-control purifications, but many small-scale AP-MS studies do not capture a complete, accurate background protein set when available controls are limited. 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 its use for scoring protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely accessible at http://www.crapome.org/ .

A statistical framework for assigning confidence scores for protein-protein interaction data generated via affinity purification–mass spectrometry, called significance analysis of interactome (SAINT) is described. We present 'significance analysis of interactome' (SAINT), a computational tool that assigns confidence scores to protein-protein interaction data generated using affinity purification–mass spectrometry (AP-MS). The method uses label-free quantitative data and constructs separate distributions for true and false interactions to derive the probability of a bona fide protein-protein interaction. We show that SAINT is applicable to data of different scales and protein connectivity and allows transparent analysis of AP-MS data.

Budding Yeast Kinome Revealed: Covalent modification of proteins by phosphorylation is a primary means by which cells control the biochemical activities and functions of proteins. To better understand the full spectrum of cellular control mechanisms mediated by phosphorylation, Breitkreutz et al. (p. 1043 ; see the Perspective by Levy et al. ) used mass spectrometry to identify proteins that interacted with the complete set of protein kinases from budding yeast and with other molecules, including phosphatases, which influence phosphorylation reactions. The results reveal a network of interacting protein kinases and phosphatases, and analysis of other interacting proteins suggests previously undiscovered roles for many of these enzymes.