A high dimensional map of phosphorylation‐dependent signaling in budding yeast

Abstract

Development of mass spectrometry‐based phosphoproteomics has substantially expanded our understanding of cellular processes regulated by protein phosphorylation. However, most phosphoproteomic studies are low dimensional (<5 conditions) due to analytical limitations. Phosphoproteomic profiling at much higher dimensionality is required to obtain systematic insight into the architecture and function of phosphorylation‐dependent signaling networks.Here we present a high‐dimensional quantitative phosphoproteomic perturbation atlas in Saccharomyces cerevisiae at large scale (>100 perturbations, ~600 samples). We applied a combination of novel methods for automated sample‐preparation, prioritization of functional phosphosites by machine learning and advanced data‐analysis to overcome current limitations in large‐scale phosphoproteomics. Integrating this massive quantitative resource gave us the unique opportunity to systematically elucidate signaling modules, profile kinase activities, and predict functional phosphorylation sites at the organismal scale.