Protein Identification Using Sorcerer 2 and SEQUEST

Deborah H. Lundgren1, Harryl Martinez2, Michael E. Wright2, David K. Han1

1 Department of Cell Biology, Center for Vascular Biology, University of Connecticut Health Center, Farmington, Connecticut, 2 Department of Molecular Physiology and Biophysics, University of Iowa Carver College of Medicine, Iowa City, Iowa
Publication Name:  Current Protocols in Bioinformatics
Unit Number:  Unit 13.3
DOI:  10.1002/0471250953.bi1303s28
Online Posting Date:  December, 2009
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Sage‐N's Sorcerer 2 provides an integrated data analysis system for comprehensive protein identification and characterization. It runs on a proprietary version of SEQUESTR, the most widely used search engine for identifying proteins in complex mixtures. The protocol presented here describes the basic steps performed to process mass spectrometric data with Sorcerer 2 and how to analyze results using TPP and Scaffold. The unit also provides an overview of the SEQUESTR algorithm, along with Sorcerer‐SEQUESTR enhancements, and a discussion of data filtering methods, important considerations in data interpretation, and additional resources that can be of assistance to users running Sorcerer and interpreting SEQUESTR results. Curr. Protoc. Bioinform. 28:13.3.1‐13.3.21. © 2009 by John Wiley & Sons, Inc.

Keywords: SEQUEST; Sorcerer; Scaffold; Ascore; TPP; proteomics; post‐translational modifications; false discovery rate; quantification

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Table of Contents

  • Basic Protocol 1: Using Sorcerer 2 to Analyze a Complex Mixture of Proteins
  • Guidelines for Understanding Search Results
  • Commentary
  • Literature Cited
  • Figures
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Literature Cited

   Aebersold, R. and Goodlett, D.R. 2001. Mass spectrometry in proteomics. Chem. Rev. 101:269‐295.
   Baldwin, M.A. 2004. Protein identification by mass spectrometry: Issues to be considered. Mol. Cell. Proteomics 3:1‐9.
   Beausoleil, S.A., Villén, J., Gerber, S.A., Rush, J. and Gygi, S.P. 2006. A probability‐based approach for high‐throughput protein phosphorylation analysis and site localization. Nat. Biotechnol. 24:1285‐1292.
   Choi, H., Fermin, D., and Nesvizhskii, A.I. 2008. Significance analysis of spectral count data in label‐free shotgun proteomics. Mol. Cell. Proteomics 7:2373‐2385.
   Elias, J.E. and Gygi, S.P. 2007. Target‐decoy search strategy for increased confidence in large‐scale protein identifications by mass spectrometry. Nat. Methods 2007. 4:207‐214.
   Eng, J., McCormack, A.L., and Yates, J.R. 1994. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5:976‐989.
   Han, D.K., Eng, J., Zhou, H., and Aebersold, R. 2001. Quantitative profiling of differentiation‐induced microsomal proteins using isotope‐coded affinity tags and mass spectrometry. Nat. Biotechnol. 19:946‐951.
   Hochstrasser, D.F., Sanchez, J., and Appel, R.D. 2002. Proteomics and its trends facing nature's complexity. Proteomics 2:807‐812.
   Käll, L., Storey, J.D., MacCoss, M.J., and Noble, W.S. 2008a. Assigning significance to peptides identified by tandem mass spectrometry using decoy databases. J. Proteome Res. 7:29‐34.
   Käll, L., Storey, J.D., MacCoss, M.J., and Noble, W.S. 2008b. Posterior error probabilities and false discovery rates: Two sides of the same coin. J. Proteome Res. 7:40‐44.
   Keller, A., Nesvizhskii, A.I., Kolker, E., and Aebersold, E. 2002. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74:5383‐5392.
   Mayya, V., Rezaul, K., Cong, Y., and Han, D. 2005. Systematic comparison of a two‐dimensional ion trap and a three‐dimensional ion trap mass spectrometer in proteomics. Mol. Cell Proteomics 4:214‐223.
   Mitchell, P. 2003. In the pursuit of industrial proteomics. Nat. Biotechnol. 21:233‐237.
   Nesvizhskii, A.I., Keller, A., Kolker, E., and Aebersold, R. 2003. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75:4646‐4658.
   Pavelka, N.M., Fournier, M.L., Swanson, S.K., Pelizzola, M., Ricciardi‐Castagnoli, P., Florens, L., and Washburn, M.P. 2008. Statistical similarities between transcriptomics and quantitative shotgun proteomics data. Mol. Cell. Proteomics 7:631‐644.
   Peng, J., Elias, J.E., Thoreen, C.C., Licklider, L.F., and Gygi, S.P. 2003. Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC‐MS/MS) for large‐scale protein analysis: The yeast proteome. J. Proteome Res. 2:43‐50.
   Rezaul, K., Linfeng, W., Mayya, V., Hwang, S., and Han, D. 2005. A systematic characterization of mitochondrial proteome from human T leukemia cells. Mol. Cell Proteomics 4:169‐181.
   Washburn, M.P., Wolters, D., and Yates, J.R. 2001. Large‐scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 19:242‐247.
   Zhang, B., VerBerkmoes, N.C., Langston, M.A., Uberbacher, E., Hettich, R.L., Samatova, N.F. 2006. Detecting differential and correlated protein expression in label‐free shotgun proteomics. J. Proteome Res. 5:2909‐2918.
Key References
   Beausoleil et al., . See above
  Description of Ascore algorithm for phosphorylation site localization.
  Eng et al., . See above.
  The original description of the SEQUEST algorithm.
  Käll et al., . See above.
  Good overview of methods associating statistical scores with results of MS/MS experiments.
  Peng et al., . See above.
  Proposes new criteria for decreasing false‐positive results in SEQUEST‐based peptide identification.
  Washburn et al., . See above.
  Widely used criteria for SEQUEST‐based peptide identification.
Internet Resources
  Portal for support information on using Sorcerer and for general information on proteomics.
  Web site for description and downloads of TPP software tools, including pep and prot XML Viewers.‐1.pdf
  Downloadable tutorial on Scaffold.
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