Cited by 1,577Advanced Proteomics Techniques and ApplicationsMass Spectrometry Techniques and ApplicationsMachine Learning in Bioinformatics

A probability-based approach for high-throughput protein phosphorylation analysis and site localization

Beausoleil, Sean A, Villén, Judit, Gerber, Scott A, Rush, John, Gygi, Steven P·2006·Nature Biotechnology

Abstract

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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Crossref · 10.1038/nbt1240

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21 of 21 references verified0 citation errors · 0 not checkable

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Authors
Beausoleil, S. A., Villén, J., Gerber, S. A., Rush, J., & Gygi, S. P.
Affiliations
  • Harvard University (US)
  • Dartmouth Cancer Center (US)
Corresponding author
Gygi, Steven P
Venue
Nature Biotechnology
Published
2006-09-10
Volume
24
Issue
10
Pages
1285-1292
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
OpenAlex
W1993450321
Cited by
1,577
References
21
Citations by year
2026: 44 · 2025: 58 · 2024: 74 · 2023: 81 · 2022: 64 · 2021: 81 · 2020: 78 · 2019: 71 · 2018: 75 · 2017: 80 · 2016: 68 · 2015: 99 · 2014: 118 · 2013: 100 · 2012: 112
FWCI
31.16
Type
journal article
Language
en
Open access
closed
Locations
  • Nature Biotechnology (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health
  • National Human Genome Research Institute (HG03456, R01HG003456)
  • National Institute of General Medical Sciences (GM 67945, R01GM067945)
Topics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
protein phosphorylation, phosphorylation site localization, false positive rate, HeLa cells
MeSH terms
Algorithms, HeLa Cells, Humans, Image Processing, Computer-Assisted, Peptide Mapping, Phosphorylation, Probability, Mass Spectrometry, Nocodazole

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