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/ .

Zeroing in on essential human genes: More powerful genetic techniques are helping to define the list of genes required for the life of a human cell. Two papers used the CRISPR genome editing system and a gene trap method in haploid human cells to screen for essential genes (see the Perspective by Boone and Andrews). Wang et al. 's analysis of multiple cell lines indicates that it may be possible to find tumor-specific dependencies on particular genes. Blomen et al. investigate the phenomenon in which nonessential genes are required for fitness in the absence of another gene. Hence, complexity rather than robustness is the human strategy. Science , this issue p. 1096 and p. 1092 ; see also p. 1028

The BCR-ABL tyrosine kinase inhibitor imatinib represents the current frontline therapy in chronic myeloid leukemia. Because many patients develop imatinib resistance, 2 second-generation drugs, nilotinib and dasatinib, displaying increased potency against BCR-ABL were developed. To predict potential side effects and novel medical uses, we generated comprehensive drug-protein interaction profiles by chemical proteomics for all 3 drugs. Our studies yielded 4 major findings: (1) The interaction profiles of the 3 drugs displayed strong differences and only a small overlap covering the ABL kinases. (2) Dasatinib bound in excess of 30 Tyr and Ser/Thr kinases, including major regulators of the immune system, suggesting that dasatinib might have a particular impact on immune function. (3) Despite the high specificity of nilotinib, the receptor tyrosine kinase DDR1 was identified and validated as an additional major target. (4) The oxidoreductase NQO2 was bound and inhibited by imatinib and nilotinib at physiologically relevant drug concentrations, representing the first nonkinase target of these drugs.

Single-cell transcriptomics offers unprecedented opportunities to infer the ligand–receptor (LR) interactions underlying cellular networks. We introduce a new, curated LR database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted LR interactions and show that our regularized score outperforms other scoring schemes while controlling false positives. SingleCellSignalR is implemented as an open-access R package accessible to entry-level users and available from https://github.com/SCA-IRCM. Analysis results come in a variety of tabular and graphical formats. For instance, we provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways. A detailed comparison of related tools is conducted. Among various examples, we demonstrate SingleCellSignalR on mouse epidermis data and discover an oriented communication structure from external to basal layers.