Protein Identification Using Sorcerer 2 and SEQUEST
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- Abstract
- Table of Contents
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- Literature Cited
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 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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PDF or HTML at Wiley Online LibraryTable 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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Figure 13.3.1 Begin a search: Sorcerer's initial screen. View Image -
Figure 13.3.2 Sorcerer's Spectra screen, showing the drop‐down list of data formats uploadable in Sorcerer. View Image -
Figure 13.3.3 Sorcerer's Manage Profiles screen, where a search profile can be set up or modified. View Image -
Figure 13.3.4 Sorcerer's Database screen, where a database can be modified or a new database defined. View Image -
Figure 13.3.5 (A ) Customizing enzymes in Sorcerer's Customize screen. (B ) Customizing modifications in Sorcerer's Customize screen. View Image -
Figure 13.3.6 Viewing jobs and results in Sorcerer's Queue screen. View Image -
Figure 13.3.7 Selecting the TPP Analysis tab in the Queue screen. View Image -
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. View Image -
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. View Image -
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. View Image
Videos
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 | |
| 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. |
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