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Protein Identification Using Sorcerer 2 and SEQUEST

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

¹Department of Cell Biology, Center for Vascular Biology, University of Connecticut Health Center, Farmington, Connecticut
²Department 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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Abstract

Sage-N's Sorcerer 2 provides an integrated data analysis system for comprehensive protein identification and characterization. It runs on a proprietary version of SEQUEST^R, 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 SEQUEST^R algorithm, along with Sorcerer-SEQUEST^R 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 SEQUEST^R 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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Basic Protocol: Using Sorcerer 2 to Analyze a Complex Mixture of Proteins
Guidelines for Understanding Search Results
Commentary
Literature Cited
Figures

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Figures

Figure 13.3.1

Begin a search: Sorcerer's initial screen.

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Figure 13.3.2

Sorcerer's Spectra screen, showing the drop-down list of data formats uploadable in Sorcerer.

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Figure 13.3.3

Sorcerer's Manage Profiles screen, where a search profile can be set up or modified.

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Figure 13.3.4

Sorcerer's Database screen, where a database can be modified or a new database defined.

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Figure 13.3.5

(A) Customizing enzymes in Sorcerer's Customize screen. (B) Customizing modifications in Sorcerer's Customize screen.

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Figure 13.3.6

Viewing jobs and results in Sorcerer's Queue screen.

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Figure 13.3.7

Selecting the TPP Analysis tab in the Queue screen.

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Figure 13.3.8

(A) TPP's PepXML Viewer, providing links to details of peptide-spectrum matches. (B) TPP's ProtXML Viewer, provide a ProteinProphet view of protein identifications. (C) ProteinProphet's Sensitivity/error information, accessible from a link in the ProtXML screen.

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Figure 13.3.9

(A) Samples view: an overview of identified proteins. (B) Similarity view: where protein ambiguities can be explored. (C) Protein view: providing details of protein identifications. (D) Quantify view: where different conditions can be compared quantitatively. (E) Statistics view: exploring the statistical basis of peptide identifications. (F) Publish view: a template for Methods and supplementary table construction.

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Figure 13.3.10

Peptide ion fragmentation tables from Scaffold's Protein view are shown, with experimentally matched fragment ions highlighted. The top panel illustrates a high-probability peptide-spectrum match, showing long runs of matching ions. The bottom panel illustrates a low-probability identification, with a low number of scattered matches.

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Literature Cited

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., 2006. See above
Description of Ascore algorithm for phosphorylation site localization.
	Eng et al., 1994. See above.
The original description of the SEQUEST algorithm.
	Käll et al., 2008b. See above.
Good overview of methods associating statistical scores with results of MS/MS experiments.
	Peng et al., 2003. See above.
Proposes new criteria for decreasing false-positive results in SEQUEST-based peptide identification.
	Washburn et al., 2001. See above.
Widely used criteria for SEQUEST-based peptide identification.
Internet Resources
	http://proteomics2.com
Portal for support information on using Sorcerer and for general information on proteomics.
	http://tools.proteomecenter.org/software.php
Web site for description and downloads of TPP software tools, including pep and prot XML Viewers.
	http://www.proteomesoftware.com/tutorial/scaffold_users_guide_2-1.pdf
Downloadable tutorial on Scaffold.