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

Article Abstract:

A large-scale phosphorylation data set with a measured error rate as determined by the target-decoy approach is presented and an approach to maximize data set sensitivity is demonstrated by efficiently distracting incorrect peptide spectral matches (PSMs). The method is applied in a fully automated fashion to nocodazole-arrested HeLa cell lysate where 1,761 nonreductant phosphorylation sites from 491 proteins are identified with a peptide false-positive rate of 1.3%.

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Immunoaffinity profiling of tyrosine phosphorylation in cancer cells

Article Abstract:

Large numbers of tyrosine phosphorylation sites are identified by a simple strategy that uses phosphotyrosine specific antibody to immunoprecipitate phosphotyrosine peptides directly from digested cellular protein extracts and standard LC-MS/MS methods. Protein kinases that are abnormally activated in cancer cells can be identified by this immunoaffinity profiling strategy.

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Intensity-based protein identification by machine learning from a library of tandem mass spectra

Article Abstract:

The intensity pattern modeling that improves peptide and protein identification from Tandem mass spectrometry (MS/MS) spectra is demonstrated. A 'decoy' proteome approach is used to estimate incorrect match frequency and showed that an intensity-based method reduces peptide identification error by 50-96% without any loss in sensitivity.

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