4.7 Article

The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry - I. Statistically annotated datasets for peptide sequences and proteins identified via the application of ICAT and tandem mass spectrometry to proteins copurifying with T cell lipid rafts

期刊

MOLECULAR & CELLULAR PROTEOMICS
卷 2, 期 7, 页码 426-427

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AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC
DOI: 10.1074/mcp.D300002-MCP200

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资金

  1. NHLBI NIH HHS [N01 HV 28179] Funding Source: Medline
  2. NIAID NIH HHS [R01 AI 41109-01, R01 AI 51344-01] Funding Source: Medline

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Lipid rafts were prepared according to standard protocols from Jurkat T cells stimulated via T cell receptor/CD28 cross-linking and from control (unstimulated) cells. Co-isolating proteins from the control and stimulated cell preparations were labeled with isotopically normal (d0) and heavy (d8) versions of the same isotope-coded affinity tag ( ICAT) reagent, respectively. Samples were combined, proteolyzed, and resultant peptides fractionated via cation exchange chromatography. Cysteine-containing (ICAT-labeled) peptides were recovered via the biotin tag component of the ICAT reagents by avidin-affinity chromatography. On-line micro-capillary liquid chromatography tandem mass spectrometry was performed on both avidin-affinity (ICAT-labeled) and flow-through ( unlabeled) fractions. Initial peptide sequence identification was by searching recorded tandem mass spectrometry spectra against a human sequence data base using SEQUEST(TM) software. New statistical data modeling algorithms were then applied to the SEQUEST(TM) search results. These allowed for discrimination between likely correct and incorrect peptide assignments, and from these the inferred proteins that they collectively represented, by calculating estimated probabilities that each peptide assignment and subsequent protein identification was a member of the correct population. For convenience, the resultant lists of peptide sequences assigned and the proteins to which they corresponded were filtered at an arbitrarily set cut-off of 0.5 (i.e. 50% likely to be correct) and above and compiled into two separate datasets. In total, these data sets contained 7667 individual peptide identifications, which represented 2669 unique peptide sequences, corresponding to 685 proteins and related protein groups.

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