Protein phosphorylation mediates cellular responses to growth factors, developmental cues and various stresses by the regulation of protein interactions, enzyme activity or protein localization. However, the protein interactions of kinases, phosphatases, their regulatory subunits and substrates remain sparsely mapped, particularly in high-throughput (HTP) datasets. To chart the budding yeast kinase and phosphatase interaction network, we systematically characterized protein kinase and phosphatase associating proteins by affinity purification coupled to mass spectrometry. We developed an analytical pipeline to perform rapid magnetic bead capture, on-bead protein digestion and mass spectrometric identification of associated proteins, using different epitope tags and expression systems. To analyze the interaction data, a new open source laboratory information management system (LIMS) for interaction proteomics called ProHits and statistical approaches to discriminate true interactors from background noise were developed (Figure 1). In total, 130 protein kinase catalytic subunits, 24 lipid and metabolic kinases, 47 kinase regulatory subunits, 38 protein phosphatases, 32 phosphatase regulatory subunits and 5 metabolic phosphatases were analyzed. We eliminated nonspecific interactions using a statistical model called Significance Analysis of Interactome (SAINT). In contrast to simple threshold models, for each interaction SAINT assigns the number of peptide identifications for each interactor to a probability distribution, which is then used to estimate the likelihood of a true interaction. We validated SAINT by performing multiple independent purifications for several kinases and expression levels.
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