Phosphoproteome analysis by mass spectrometry and its application to Saccharomyces cerevisiae

TLDR: In this article, a methodology was proposed to characterize most, if not all, phosphoproteins from a whole-cell lysate in a single experiment, and a total of 216 peptide sequences defining 383 sites of phosphorylation were determined.

Abstract: Protein kinases are coded by more than 2,000 genes and thus constitute the largest single enzyme family in the human genome. Most cellular processes are in fact regulated by the reversible phosphorylation of proteins on serine, threonine, and tyrosine residues. At least 30% of all proteins are thought to contain covalently bound phosphate. Despite the importance and widespread occurrence of this modification, identification of sites of protein phosphorylation is still a challenge, even when performed on highly purified protein. Reported here is methodology that should make it possible to characterize most, if not all, phosphoproteins from a whole-cell lysate in a single experiment. Proteins are digested with trypsin and the resulting peptides are then converted to methyl esters, enriched for phosphopeptides by immobilized metal-affinity chromatography (IMAC), and analyzed by nanoflow HPLC/electrospray ionization mass spectrometry. More than 1,000 phosphopeptides were detected when the methodology was applied to the analysis of a whole-cell lysate from Saccharomyces cerevisiae. A total of 216 peptide sequences defining 383 sites of phosphorylation were determined. Of these, 60 were singly phosphorylated, 145 doubly phosphorylated, and 11 triply phosphorylated. Comparison with the literature revealed that 18 of these sites were previously identified, including the doubly phosphorylated motif pTXpY derived from the activation loop of two mitogen-activated protein (MAP) kinases. We note that the methodology can easily be extended to display and quantify differential expression of phosphoproteins in two different cell systems, and therefore demonstrates an approach for "phosphoprofiling" as a measure of cellular states.