Abstract
Tandem mass spectrometry has become central in proteomics projects. In particular, it is of prime importance to design sensitive and selective score functions to reliably identify peptides in databases. By using a huge collection of 140 000+ peptide MS/MS spectra, we systematically study the importance of many characteristics of a match (peptide sequence/spectrum) to include in a score function. Besides classical match characteristics, we investigate the value of new characteristics such as amino acid dependence and consecutive fragment matches. We finally select a combination of promising characteristics and show that the corresponding score function achieves very low false positive rates while being very sensitive, thereby enabling highly automated peptide identification in large proteomics projects. We compare our results to widely used protein identification systems and show a significant reduction in false positives.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Anderson, D.C., Li, W., Payan, D.G., Noble, W.S.: A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: support vector machine classification of peptide MS/MS spectra and SEQUEST scores. J. Proteome Res. 2, 137–146 (2003)
Bafna, V., Edwards, N.: SCOPE: a probabilistic model for scoring tandem mass spectra against a peptide database. Bioinformatics 17, S13–S21 (2001)
Colinge, J., Masselot, A., Giron, M., Dessingy, T., Magnin, J.: OLAV: Towards high-throughput MS/MS data identification. Proteomics (August 2003) (to appear)
Dancik, V., Addona, T.A., Clauser, K.R., Vath, J.E., Pevzner, P.A.: De novo peptide sequencing via tandem mass spectrometry: a graph-theoretical approach. J. Comp. Biol. 6, 327–342 (1999)
Durbin, R., et al.: Biological sequence analysis. Cambridge University Press, Cambridge (1998)
Eng, J.K., McCormack, A.J., Yates III, J.R.: 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 (1994)
Field, H.L., Fenyö, D., Beavis, R.C.: RADARS, a bioinformatics solution that automates proteome mass spectral analysis, optimises protein identifications, and archives data in a relational database. Proteomics 2, 36–47 (2002)
Havilio, M., Haddad, Y., Smilansky, Z.: Intensity-based statistical scorer for tandem mass spectrometry. Anal. Chem. 75, 435–444 (2003)
Henzel, W.J., et al.: Identifying protein from two-dimensional gels by molecular mass searching of peptide fragments in protein sequence databases. Proc. Natl. Acad. Sci. USA 90, 5011–5015 (1993)
James, P.: Mass Spectrometry. Proteome Research. Springer, Berlin (2000)
Johnson, R.S., et al.: Collision-induced fragmentation of (m + h) + ions of peptides. Side chain specific sequence ions. Intl. J. Mass Spectrom. and Ion Processes 86, 137–154 (1988)
Keller, A., Nesvizhskii, A.I., Kolker, E., Aebersold, R.: Empirical statistical model to estimate the accuracy of peptide identification made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002)
Keller, A., Purvine, S., Nesvizhskii, A.I., Stolyar, S., Goodlett, D.R., Kolker, E.: Experimental protein mixture for validating tandem mass spectral analysis. OMICS 6, 207–212 (2002)
Liebler, D.C., Hansen, B.T., Davey, S.W., Tiscareno, L., Mason, D.E.: Peptide sequence motif analysis of tandem MS data with the SALSA algorithm. Anal. Chem. 74, 203–210 (2002)
Masselot, A., Magnin, J., Giron, M., Dessingy, T., Ferrer, D., Colinge, J.: OLAV: General applicability of model-based MS/MS peptide score functions. In: Proc. 51st Am. Soc. Mass Spectrom., Montreal (2003)
McCormack, A.L., et al.: Direct analysis and identification of proteins in mixture by LC/MS/MS and database searching at the low-femtomole level. Anal. Chem. 69, 767–776 (1997)
Moore, R.E., Young, M.K., Lee, T.D.: Qscore: An algorithm for evaluating sequest database search results. J. Am. Soc. Mass Spectrom. 13, 378–386 (2002)
Papayannopoulos, I.A.: The interpretation of collision-induced dissociation mass spectra of peptides. Mass Spectrometry Review 14, 49–73 (1995)
Papin, D.J., Hojrup, P., Bleasby, A.J.: Rapid identification of proteins by peptide-mass fingerprinting. Curr. Biol. 3, 327–332 (1993)
Perkins, D.N., Pappin, D.J., Creasy, D.M., Cottrell, J.S.: Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999)
Petritis, K., Kangas, L.J., Fergusson, P.L., Anderson, G.A., Paša-Tolić, L., Lipton, M.S., Auberry, K.J., Strittmatter, E.F., Shen, Y., Zhao, R., Smith, R.D.: Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analysis. Anal. Chem. 75, 1039–1048 (2003)
Poor, H.V.: An Introduction to Signal Detection and Estimation. Springer, New York (1994)
Sadygov, R.G., Eng, J., Durr, E., Saraf, A., McDonald, H., MacCoss, M.J., Yates, J.: Code development to improve the efficiency of automated MS/MS spectra interpretation. J. Proteome Res. 1, 211–215 (2002)
Schütz, F., Kapp, E.A., Eddes, J.E., Simpson, R.J., Speed, T.P., Speed, T.P.: Deriving statistical models for predicting fragment ion intensities. In: Proc. 51st Am. Soc. Mass Spectrom., Montreal (2003)
Skilling, J.K.: Improved methods of identifying peptides and protein by mass spectrometry. European Patent Application EP 1,047,107,A2 (1999)
Snyder, P.: Interpreting Protein Mass Spectra. Oxford University Press, Washington (2000)
Tabb, D.L., Smith, L.L., Breci, L.A., Wysocki, V.H., Lin, D., Yates, J.: Statistical characterization of ion trap tandem mass spectra from doubly charged tryptic peptides. Anal. Chem. 75, 1155–1163 (2003)
Yates, J., Eng, J.K.: Identification of nucleotides, amino acids, or carbohydrates by mass spectrometry. United States Patent 6,017,693 (1994)
Zhang, N., Aebersold, R., Schwikowski, B.: ProbId: A probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data. Proteomics 2, 1406–1412 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Colinge, J., Masselot, A., Magnin, J. (2003). A Systematic Statistical Analysis of Ion Trap Tandem Mass Spectra in View of Peptide Scoring. In: Benson, G., Page, R.D.M. (eds) Algorithms in Bioinformatics. WABI 2003. Lecture Notes in Computer Science(), vol 2812. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39763-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-39763-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20076-5
Online ISBN: 978-3-540-39763-2
eBook Packages: Springer Book Archive