A probability-based approach for high-throughput protein phosphorylation analysis and site localization.
Data analysis and interpretation remain major logistical challenges when attempting to identify large numbers of protein phosphorylation sites by nanoscale reverse-phase liquid chromatography/tandem mass spectrometry (LC-MS/MS) (Supplementary Figure 1 online). In this report we address challenges that are often only addressable by laborious manual validation, including data set error, data set sensitivity and phosphorylation site localization. We provide a large-scale phosphorylation data set with a measured error rate as determined by the target-decoy approach, we demonstrate an approach to maximize data set sensitivity by efficiently distracting incorrect peptide spectral matches (PSMs), and we present a probability-based score, the Ascore, that measures the probability of correct phosphorylation site localization based on the presence and intensity of site-determining ions in MS/MS spectra. We applied our methods in a fully automated fashion to nocodazole-arrested HeLa cell lysate where we identified 1,761 nonredundant phosphorylation sites from 491 proteins with a peptide false-positive rate of 1.3%.
Beausoleil
Sean A
SA
Department of Cell Biology, Harvard Medical School, 240 Longwood Ave., Boston, Massachusetts 02115, USA.
Villén
Judit
J
Gerber
Scott A
SA
Rush
John
J
Gygi
Steven P
SP
D000465:Algorithms; D006367:HeLa Cells / Q000187:drug effects; D006801:Humans; D007091:Image Processing, Computer-Assisted; D013058:Mass Spectrometry / Q000379:methods*; D015739:Nocodazole / Q000494:pharmacology; D010449:Peptide Mapping / Q000379:methods*; D010766:Phosphorylation; D011336:Probability
D015739:Nocodazole