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. von Haller, P., Yi, E, Donohoe, S, Vaughn, K, Keller, A, Nesvizhskii, A., Eng, J, Li, X., Goodlett, D., Aebersold, R, & Watts, J. MOLECULAR \& CELLULAR PROTEOMICS, 2(7):426-427, JUL, 2003.
doi  abstract   bibtex   
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.
@article{ ISI:000184902000002,
  author = {von Haller, PD and Yi, E and Donohoe, S and Vaughn, K and Keller, A and
   Nesvizhskii, AI and Eng, J and Li, XJ and Goodlett, DR and Aebersold, R
   and Watts, JD},
  title = {{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}},
  journal = {{MOLECULAR \& CELLULAR PROTEOMICS}},
  year = {{2003}},
  volume = {{2}},
  number = {{7}},
  pages = {{426-427}},
  month = {{JUL}},
  abstract = {{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.}},
  doi = {{10.1074/mcp/D300002-MCP200}},
  issn = {{1535-9476}},
  researcherid-numbers = {{Nesvizhskii, Alexey/A-5410-2012
   Eng, Jimmy/I-4202-2012}},
  orcid-numbers = {{Eng, Jimmy/0000-0001-6352-6737}},
  unique-id = {{ISI:000184902000002}}
}

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