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Cell Biology
New
Tools and Applications for Cellular Proteomics
J.R. Yates III, G.T.
Cantin, D. Cociorva, C. Delahunty, M.Q. Dong, L. Florens, J. Hewel, J.R. Johnson, M.J. MacCoss,
I. MacLeod, W.H. McDonald, N. Muster, S. Niessen, C.I. Ruse, R. Sadygov, D.L. Tabb, J. Venable,
J. Wohlschlegel, C. Wu, W.H. Zhu
Several
advances in high-throughput technologies have created the foundation for the global proteomic
analysis of complex mixtures and cellular lysates. The success and popularity of proteomics applications
in studies of complex protein interactions in biology have created a demand for more powerful tools
to analyze increasingly complex systems. Mass spectrometry is a key technology for realizing
these goals, and we are leaders in (1) developing mass spectrometry–based methods that allow
comprehensive analysis of very complex mixtures and (2) applying these emerging technologies
to studies of existing biological problems.
Multidimensional protein identification
technology (MudPIT) is a tool that we use extensively to analyze a wide range of samples. MudPIT
involves the direct coupling of a multidimensional chromatographic separation system to a tandem
mass spectrometer, providing data we term MS/MS spectra. Hundreds of thousands of MS/MS spectra
can be collected on an automated MudPIT system in a 24-hour period, and sophisticated software
is required to match spectra with peptide sequences in a database.
Recently, we developed a new algorithm
to complement the existing Sequest algorithm for protein identification from the MS/MS data.
Use of the new algorithm increases the confidence that the peptide indicated by a spectrum is correct
by providing an independent measure of the quality of spectrum-peptide matches. In addition,
de novo sequencing of the spectra can be used to extract partial peptide sequences so that less stringent
searches against a sequence database are required, aiding in identifying posttranslationally
modified peptides. As the proteomics field shifts toward methods that can provide a quantitative
measure of protein expression, software tools for analyzing quantitative MS/MS data have also
been developed to streamline and simplify data analysis.
We use MudPIT in various applications.
We developed methods to optimize the analysis of membrane proteins, considered by many a major
limitation of proteomics technologies, and applied the methods to studies of the proteome of the
Golgi apparatus. We identified 41 previously uncharacterized proteins and characterized posttranslational
modifications that had not been detected before.
Continuing with the large-scale proteomic
analysis of Plasmodium falciparum, the parasite that causes malaria, we selected candidate
antigens for vaccine development from proteomic data sets to observe the efficacy of the antigens
in generating an immune response. We found that a much wider diversity of antigens than predicted
can elicit a response. These results suggest that our understanding of antigenic immunodominance
in the host response to complex pathogens is incomplete.
Finally, we used a subtractive proteomics
strategy to identify integral membrane proteins of the nuclear envelope. All known components
of the nuclear envelope were identified, and 67 uncharacterized open reading frames were detected.
A total of 23 of the proteins identified mapped to chromosome regions that are linked to a variety
of dystrophies. Approximately 300 dystrophies remain to be linked to a responsible gene, and the
proportion of genes localized to disease loci was greater than what would be expected to occur randomly,
suggesting that many of the 67 identified proteins are good candidates for disease links.
Publications
Doolan, D.L., Southwood, S., Freilich,
D.A., Sidney, J., Graber, N.L., Shatney, L., Bebris, L., Florens, L., Dobano, C., Witney, A.A.,
Appella, E., Hoffman, S.L., Yates, J.R. III, Carucci, D.J., Sette, A.
Identification of Plasmodium falciparum antigens by antigenic analysis of genomic and
proteomic data. Proc. Natl. Acad. Sci. U. S. A. 100:9952, 2003.
MacCoss, M.J., Wu, C.C., Liu, H.,
Sadygov, R., Yates, J.R. III. A correlation algorithm for
the automated quantitative analysis of shotgun proteomics data. Anal. Chem. 75:6912, 2003.
Sadygov, R.G., Liu, H., Yates, J.R.
Statistical models for protein validation using tandem mass spectral data and protein amino acid
sequence databases. Anal. Chem. 76:1664, 2004.
Schirmer, E.C., Florens, L., Guan,
T., Yates, J.R. III, Gerace, L. Nuclear membrane proteins
with potential disease links found by subtractive proteomics. Science 301:1380, 2003.
Tabb, D.L., Saraf, A., Yates, J.R.
III. GutenTag: high-throughput sequence tagging via an
empirically derived fragmentation model. Anal. Chem. 75:6415, 2003.
Wu, C.C., MacCoss, M.J., Howell,
K.E., Yates, J.R. III. A method for the comprehensive proteomic
analysis of membrane proteins. Nat. Biotechnol. 21:532, 2003.
Wu, C.C., MacCoss, M.J., Mardones,
G., Finnigan, C., Mogelsvang, S., Yates, J.R. III, Howell, K.E.
Organellar proteomics reveals Golgi arginine dimethylation. Mol. Biol Cell 15:2907, 2004.
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