Mining Phosphopeptide Signals in Liquid Chromatography−Mass Spectrometry Data for Protein Phosphorylation Analysis

Protein phosphorylation is a key post-translational modification that governs biological processes. Despite the fact that a number of analytical strategies have been exploited for the characterization of protein phosphorylation, the identification of protein phosphorylation sites is still challenging. We proposed here an alternative approach to mine phosphopeptide signals generated from a mixture of proteins when liquid chromatography−tandem mass spectrometry (LC−MS/MS) analysis is involved. The approach combined dephosphorylation reaction, accurate mass measurements from a quadrupole/time-of-flight mass spectrometer, and a computing algorithm to differentiate possible phosphopeptide signals obtained from the LC−MS analyses by taking advantage of the mass shift generated by alkaline phosphatase treatment. The retention times and <i>m</i>/<i>z</i> values of these selected LC−MS signals were used to facilitate subsequent LC−MS/MS experiments for phosphorylation site determination. Unlike commonly used neutral loss scan experiments for phosphopeptide detection, this strategy may not bias against tyrosine-phosphorylated peptides. We have demonstrated the applicability of this strategy to sequence more, in comparison with conventional data-dependent LC−MS/MS experiments, phosphopeptides in a mixture of α- and β-caseins. The analytical scheme was applied to characterize the nasopharyngeal carcinoma (NPC) cellular phosphoproteome and yielded 221 distinct phosphorylation sites. Our data presented in this paper demonstrated the merits of computation in mining phosphopeptide signals from a complex mass spectrometric data set. Keywords: protein phosphorylation • mass spectrometry • computing algorithm • alkaline phosphatase • mass shift • phosphoproteome