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Proteomics pp 143-153 | Cite as

Multidimensional Protein Identification Technology

  • Katharina Lohrig
  • Dirk WoltersEmail author
Protocol
Part of the Methods in Molecular Biology™ book series (MIMB, volume 564)

Summary

Over the past years, large-scale analysis of proteomes gained increased interest to obtain a fast but nevertheless comprehensive overview about cellular protein content. While a complete proteome cannot be covered using current technologies because of its enormous diversity, subfractionation to reduce the complexity has become mandatory. While 2D-PAGE is well established as a high-resolution protein separation technique, it suffers from drawbacks, which can be overcome by using peptide separation methods based on multidimensional liquid chromatography. One of these technologies is multidimensional protein identification technology (MudPIT). It consists of two orthogonal separation systems – strong cation exchange (SCX) and reversed phase (RP) – coupled online in an automated fashion to mass spectrometric detection. This method offers the possibility to analyze high-complex peptide mixtures in a single experiment.

Key words

MudPIT Liquid chromatography Peptide separation Mass spectrometry 

References

  1. 1.
    Gorg, A., Weiss, W., and Dunn, M. J. (2004) Current two-dimensional electrophoresis technology for proteomics. Proteomics 4, 3665–85.PubMedCrossRefGoogle Scholar
  2. 2.
    Rabilloud, T., Adessi, C., Giraudel, A., and Lunardi, J. (1997) Improvement of the solubilization of proteins in two-dimensional electrophoresis with immobilized pH gradients. Electrophoresis 18, 307–16.PubMedCrossRefGoogle Scholar
  3. 3.
    Righetti, P. G., Bossi, A., Gorg, A., Obermaier, C., and Boguth, G. (1996) Steady-state two-dimensional maps of very alkaline proteins in an immobilized pH 10-12 gradient, as exemplified by histone types. J Biochem Biophys Methods 31, 81–91.PubMedCrossRefGoogle Scholar
  4. 4.
    Harder, A., Wildgruber, R., Nawrocki, A., Fey, S. J., Larsen, P. M., and Gorg, A. (1999) Comparison of yeast cell protein solubilization procedures for two-dimensional electrophoresis. Electrophoresis 20, 826–9.PubMedCrossRefGoogle Scholar
  5. 5.
    Davis, M. T., Beierle, J., Bures, E. T., McGinley, M. D., Mort, J., Robinson, J. H., Spahr, C. S., Yu, W., Luethy, R., and Patterson, S. D. (2001) Automated LC–LC–MS–MS platform using binary ion-exchange and gradient reversed-phase chromatography for improved proteomic analyses. J Chromatogr B Biomed Sci Appl 752, 281–91.PubMedCrossRefGoogle Scholar
  6. 6.
    Wagner, Y., Sickmann, A., Meyer, H. E., and Daum, G. (2003) Multidimensional nano-HPLC for analysis of protein complexes. J Am Soc Mass Spectrom 14, 1003–11.PubMedCrossRefGoogle Scholar
  7. 7.
    Washburn, M. P., Wolters, D., and Yates, J. R., 3rd (2001) Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol 19, 242–7.PubMedCrossRefGoogle Scholar
  8. 8.
    Hunter, T. C., Andon, N. L., Koller, A., Yates, J. R., and Haynes, P. A. (2002) The functional proteomics toolbox: methods and applications. J Chromatogr B 782, 165–81.CrossRefGoogle Scholar
  9. 9.
    Liu, H., Sadygov, R. G., and Yates, J. R., 3rd (2004) A model for random sampling and estimation of relative protein abundance in shotgun proteomics Anal Chem 76, 4193–201.PubMedCrossRefGoogle Scholar
  10. 10.
    Sadygov, R. G., Eng, J., Durr, E., Saraf, A., McDonald, H., MacCoss, M. J., Yates, J. R. (2002) Code developments to improve the efficiency of automated MS/MS spectra interpretation. J Proteome Res 1, 211–5.PubMedCrossRefGoogle Scholar
  11. 11.
    Bachelot, C., Cano, E., Grelac, F., Saleun, S., Druker, B. J., Levy-Toledano, S., Fischer, S., and Rendu, F. (1992) Functional implications of tyrosine protein phosphorylation in platelets Simultaneous studies with different agonists and inhibitors. Biochem J 284 (Pt 3), 923–8.PubMedGoogle Scholar
  12. 12.
    Maurya, P., Meleady, P., Dowling, P., and Clynes, M. (2007) Proteomic approaches for serum biomarker discovery in cancer. Anticancer Res 27, 1247–55.PubMedGoogle Scholar
  13. 13.
    Washburn, M. P., Koller, A., Oshiro, G., Ulaszek, R. R., Plouffe, D., Deciu, C., Winzeler, E., and Yates, J. R., 3rd (2003) Protein pathway and complex clustering of correlated mRNA and protein expression analyses in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A 100, 3107–12.PubMedCrossRefGoogle Scholar
  14. 14.
    Bridges, S. M., Magee, G. B., Wang, N., Williams, W. P., Burgess, S. C., and Nanduri, B. (2007) ProtQuant: a tool for the label-free quantification of MudPIT proteomics data. BMC Bioinformatics 8 Suppl 7, S24.PubMedCrossRefGoogle Scholar
  15. 15.
    MacCoss, M. J., McDonald, W. H., Saraf, A., Sadygov, R., Clark, J. M., Tasto, J. J., Gould, K. L., Wolters, D., Washburn, M., Weiss, A., Clark, J. I., and Yates, J. R., 3rd (2002) Shotgun identification of protein modifications from protein complexes and lens tissue. Proc Natl Acad Sci U S A 99, 7900–5.PubMedCrossRefGoogle Scholar
  16. 16.
    Tabb, D. L., McDonald, W. H., and Yates, J. R., 3rd (2002) DTASelect and contrast: tools for assembling and comparing protein identifications from shotgun proteomics. J Proteome Res 1, 21– 6.PubMedCrossRefGoogle Scholar
  17. 17.
    Delahunty, C. and Yates, J. R., 3rd (2005) in Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics (Dunn, M. J., Ed.), Wiley.Google Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Analytical ChemistryRuhr-University BochumBochumGermany

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