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Get Free AccessMass spectrometry has played a significant role in the identification of unknown phosphoproteins and sites of phosphorylation in biological samples. Analyses of protein phosphorylation, particularly large scale phosphoproteomic experiments, have recently been enhanced by efficient enrichment, fast and accurate instrumentation, and better software, but challenges remain because of the low stoichiometry of phosphorylation and poor phosphopeptide ionization efficiency and fragmentation due to neutral loss. Phosphoproteomics has become an important dimension in systems biology studies, and it is essential to have efficient analytical tools to cover a broad range of signaling events. To evaluate current mass spectrometric performance, we present here a novel method to estimate the efficiency of phosphopeptide identification by tandem mass spectrometry. Phosphopeptides were directly isolated from whole plant cell extracts, dephosphorylated, and then incubated with one of three purified kinases-casein kinase II, mitogen-activated protein kinase 6, and SNF-related protein kinase 2.6-along with 16O4- and 18O4-ATP separately for in vitro kinase reactions. Phosphopeptides were enriched and analyzed by LC-MS. The phosphopeptide identification rate was estimated by comparing phosphopeptides identified by tandem mass spectrometry with phosphopeptide pairs generated by stable isotope labeled kinase reactions. Overall, we found that current high speed and high accuracy mass spectrometers can only identify 20%-40% of total phosphopeptides primarily due to relatively poor fragmentation, additional modifications, and low abundance, highlighting the urgent need for continuous efforts to improve phosphopeptide identification efficiency. Graphical Abstract ᅟ.
Sebastian Juan Paez, Yuan Zhou, Jian Kang Zhu, W. Andy Tao, Chuan‐Chih Hsu, Liang Xue, Justine Arrington, Pengcheng Wang (2017). Estimating the Efficiency of Phosphopeptide Identification by Tandem Mass Spectrometry. , 28(6), DOI: https://doi.org/10.1007/s13361-017-1603-5.
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Type
Article
Year
2017
Authors
8
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1007/s13361-017-1603-5
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