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Submitted on May 8, 2003
Insitute for Systems Biology, Seattle, WA 98103
Corresponding Author: jwatts{at}systemsbiology.org
Proteomic approaches to biological research that will prove the most useful and productive require robust, sensitive and reproducible technologies for both the qualitative and quantitative analysis of complex protein mixtures. Here we applied the isotope coded affinity tag (ICAT) approach to quantitative protein profiling, in this case, proteins that co-purified with lipid raft plasma membrane domains isolated from control and stimulated Jurkat human T cells. With the ICAT approach, cysteine residues of the two related protein isolates were covalently labeled with isotopically normal and heavy versions of the same reagent, respectively. Following proteolytic cleavage of combined labeled proteins, peptides were fractionated by multidimensional chromatography and subsequently analyzed via automated tandem mass spectrometry (MS/MS). Individual MS/MS spectra were searched against a human sequence database, and a variety of recently developed, publicly available software applications were used to sort, filter, analyze and compare the results of two repetitions of the same experiment. In particular, robust statistical modeling algorithms were used to assign measures of confidence to both peptide sequences and the proteins from which they were likely derived, identified via the database searches. We show that by applying such statistical tools to the identification of T cell lipid raft-associated proteins, we were able to estimate the accuracy of peptide and protein identifications made. These tools also allow for determination of the false-positive rate as a function of user-defined data filtering parameters, thus giving the user significant control over and information about the final output of large-scale proteomic experiments. With the ability to assign probabilities to all identifications, the need for manual verification of results is substantially reduced, thus making the rapid evaluation of large proteomic datasets possible. Finally, by repeating the experiment, information relating to the general reproducibility and validity of this approach to large-scale proteomic analyses was also obtained.
Revised on June 24, 2003
Accepted on June 24, 2003
The application of new software tools to quantitative protein profiling via ICAT and tandem mass spectrometry: II. Evaluation of tandem mass spectrometry methodologies for large-scale protein analysis and the application of statistical tools for data analysis and interpretation
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