Maximizing Quantitative Phosphoproteomics of Kinase Signaling Expands the Mec1 and Tel1 Networks

Abstract

Global phosphoproteome analysis is crucial for comprehensive and unbiased investigation of kinase-mediated signaling. However, since each phosphopeptide represents a unique entity for defining identity, site-localization, and quantitative changes, phosphoproteomics often suffers from lack of redundancy and statistical power for generating high confidence datasets. Here we developed a phosphoproteomic approach in which data consistency among experiments using reciprocal stable isotope labeling defines a central filtering rule for achieving reliability in phosphopeptide identification and quantitation. We find that most experimental error or biological variation in phosphopeptide quantitation does not revert in quantitation once light and heavy media are swapped between two experimental conditions. Exclusion of non-reverting data-points from the dataset not only reduces quantitation error and variation, but also drastically reduces false positive identifications. Application of our approach in combination with extensive fractionation of phosphopeptides by HILIC identifies new substrates of the Mec1 and Tel1 kinases, expanding our understanding of the DNA damage signaling network regulated by these kinases. Overall, the proposed quantitative phosphoproteomic approach should be generally applicable for investigating kinase signaling networks with high confidence and depth.