Unambiguous Identification of Serine and Threonine Pyrophosphorylation Using Neutral-Loss-Triggered Electron-Transfer/Higher-Energy Collision Dissociation.
Martin PenkertLisa M YatesMichael SchümannDavid PerlmanDorothea FiedlerEberhard KrausePublished in: Analytical chemistry (2017)
Tandem mass spectrometry (MS/MS) has emerged as the core technology for identification of post-translational modifications (PTMs). Here, we report the mass spectrometry analysis of serine and threonine pyrophosphorylation, a protein modification that has eluded detection by conventional MS/MS methods. Analysis of a set of synthesized, site-specifically modified peptides by different fragmentation techniques shows that pyrophosphorylated peptides exhibit a characteristic neutral loss pattern of 98, 178, and 196 Da, which enables the distinction between isobaric pyro- and diphosphorylated peptides. In addition, electron-transfer dissociation combined with higher energy collision dissociation (EThcD) provides exceptional data-rich MS/MS spectra for direct and unambiguous pyrophosphosite assignment. Remarkably, sufficient fragmentation of doubly charged precursors could be achieved by electron-transfer dissociation (ETD) with increased supplemental activation, without losing the labile modification. By exploiting the specific fragmentation behavior of pyrophosphorylated peptides during collision-induced dissociation (CID), a data dependent neutral-loss-triggered EThcD acquisition method was developed. This strategy enables reliable pyrophosphopeptide identification in complex samples, without compromising speed and sensitivity.
Keyphrases
- electron transfer
- ms ms
- tandem mass spectrometry
- high performance liquid chromatography
- ultra high performance liquid chromatography
- liquid chromatography
- mass spectrometry
- protein kinase
- amino acid
- gas chromatography
- liquid chromatography tandem mass spectrometry
- simultaneous determination
- electronic health record
- bioinformatics analysis
- solid phase extraction
- big data
- high resolution mass spectrometry
- machine learning
- oxidative stress
- diabetic rats
- capillary electrophoresis
- label free
- binding protein
- sensitive detection