The application of new software tools to quantitative protein profiling via ICAT and tandem mass spectrometry: I. Statistically annotated data sets for peptide sequences and proteins identified via the application of ICAT and tandem mass spectrometry to proteins co-purifying with T cell lipid rafts

doi:10.1074/mcp.D300002-MCP200

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The application of new software tools to quantitative protein profiling via ICAT and tandem mass spectrometry: I. Statistically annotated data sets for peptide sequences and proteins identified via the application of ICAT and tandem mass spectrometry to proteins co-purifying with T cell lipid rafts

Priska D. von Haller, Eugene Yi, Samuel Donohoe, Kelly Vaughn, Andrew Keller, Alexey I Nesvizhskii, Jimmy Eng, Xiao-jun Li, David R. Goodlett, Ruedi Aebersold, and Julian D. Watts

Insitute for Systems Biology, Seattle, WA 98103

Corresponding Author: jwatts{at}systemsbiology.org

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 (mLC-MS/MS) was performed on both avidin-affinity (ICAT-labeled) and flow-through (unlabeled) fractions. Initial peptide sequence identification was by searching recorded MS/MS spectra against a human sequence database 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 data sets. In total, these data sets contained 7,667 individual peptide identifications, which represented 2,669 unique peptide sequences, corresponding to 685 proteins and related protein groups.

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