Nature Biotechnology
- 24, 1285 - 1292 (2006)
Published online: 10 September 2006; | doi:10.1038/nbt1240
A probability-based approach for high-throughput protein phosphorylation analysis and site localizationSean A Beausoleil1, Judit Villén1, Scott A Gerber2, John Rush3 & Steven P Gygi11
Department of Cell Biology, Harvard Medical School, 240 Longwood Ave., Boston, Massachusetts 02115, USA. 2
Department of Genetics and Norris Cotton Cancer Center, Lebanon, New Hampshire 03755, USA. 3
Cell Signaling Technology, Inc., Beverley, Massachusetts 01915, USA.
Correspondence should be addressed to Steven P Gygi steven_gygi@hms.harvard.edu 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%.
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