Eukaryotic translation initiation factor 4F (eIF4F) is a protein complex that mediates recruitment of ribosomes to mRNA. This event is the rate-limiting step for translation under most circumstances and a primary target for translational control. Functions of the constituent proteins of eIF4F include recognition of the mRNA 5' cap structure (eIF4E), delivery of an RNA helicase to the 5' region (eIF4A), bridging of the mRNA and the ribosome (eIF4G), and circularization of the mRNA via interaction with poly(A)-binding protein (eIF4G). eIF4 activity is regulated by transcription, phosphorylation, inhibitory proteins, and proteolytic cleavage. Extracellular stimuli evoke changes in phosphorylation that influence eIF4F activity, especially through the phosphoinositide 3-kinase (PI3K) and Ras signaling pathways. Viral infection and cellular stresses also affect eIF4F function. The recent determination of the structure of eIF4E at atomic resolution has provided insight about how translation is initiated and regulated. Evidence suggests that eIF4F is also implicated in malignancy and apoptosis.

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing over 23 million double mutants, identifying ~550,000 negative and ~350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

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.