The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses.

Significance Analysis of INTeractome (SAINT) is a statistical method for probabilistically scoring protein-protein interaction data from affinity purification-mass spectrometry (AP-MS) experiments. The utility of the software has been demonstrated in many protein-protein interaction mapping studies, yet the extensive testing also revealed some practical drawbacks. In this paper, we present a new implementation, SAINTexpress, with a simpler statistical model and a quicker scoring algorithm, leading to significant improvements in computational speed and sensitivity of scoring. SAINTexpress also incorporates external interaction data to compute a supplemental topology-based score to improve the likelihood of identifying co-purifying protein complexes in a probabilistically objective manner. Overall, these changes are expected to improve the performance and user experience of SAINT across various types of high quality datasets.

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Significance Analysis of INTeractome (SAINT) is a software package for scoring protein‐protein interactions based on label‐free quantitative proteomics data (e.g., spectral count or intensity) in affinity purification–mass spectrometry (AP‐MS) experiments. SAINT allows bench scientists to select bona fide interactions and remove nonspecific interactions in an unbiased manner. However, there is no ‘one‐size‐fits‐all’ statistical model for every dataset, since the experimental design varies across studies. Key variables include the number of baits, the number of biological replicates per bait, and control purifications. Here we give a detailed account of input data format, control data, selection of high‐confidence interactions, and visualization of filtered data. We explain additional options for customizing the statistical model for optimal filtering in specific datasets. We also discuss a graphical user interface of SAINT in connection to the LIMS system ProHits, which can be installed as a virtual machine on Mac OS X or Windows computers. Curr. Protoc. Bioinform. 39:8.15.1‐8.15.23. © 2012 by John Wiley & Sons, Inc.