Improved sequence tag generation method for peptide identification in tandem mass spectrometry

Journal of Proteome Research

Volumn 7, Issue 10, 2008, Pages 4422-4434

  Cao, Xia ^a   Nesvizhskii, Alexey I ^a,b  

^a UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Algorithms; Database searching; De novo sequencing; Peptide identification; Proteomics; Statistical analysis; Tandem mass spectrometry

Indexed keywords

PHOSPHOPEPTIDE; DROSOPHILA PROTEIN; PEPTIDE;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; NONHUMAN; PRIORITY JOURNAL; PROTEIN ANALYSIS; SENSITIVITY ANALYSIS; SEQUENCE TAGGED SITE; TANDEM MASS SPECTROMETRY; ANIMAL; DROSOPHILA MELANOGASTER; EVALUATION; GENETICS; METHODOLOGY; MOLECULAR WEIGHT; NUCLEOTIDE SEQUENCE; PROTEIN DATABASE; PROTEOMICS; SENSITIVITY AND SPECIFICITY;

AMINO ACID SEQUENCE; ANIMALS; BASE SEQUENCE; DATABASES, PROTEIN; DROSOPHILA MELANOGASTER; DROSOPHILA PROTEINS; MOLECULAR WEIGHT; PEPTIDES; PROTEOMICS; SENSITIVITY AND SPECIFICITY; TANDEM MASS SPECTROMETRY;

EID: 55949128226     PISSN: 15353893     EISSN: None     Source Type: Journal    
DOI: 10.1021/pr800400q     Document Type: Article

References (41)

1. Hernandez, P.; Müller, M.; Appel, R. D. Automated protein identification by tandem mass spectrometry: Issues and strategies. Mass Spectrom. Rev. 2006, 25 (2), 235-254.
2. Nesvizhskii, A. I. Protein identification by tandem mass spectrometry and sequence database searching. Methods Mol. Biol. 2007, 367, 87-119.
3. Xu, C; Ma, B. Software for computational peptide identification from MS-MS data. Drug Discovery Today 2006, 11 (13-14), 595- 600.
4. Keller, A.; Nesvizhskii, A. I.; Kolker, E.; Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 2002, 74 (20), 5383-5392.
5. Bern, M.; Cai, Y.; Goldberg, D. Lookup peaks: a hybrid of de novo sequencing and database search for protein identification by tandem mass spectrometry. Anal. Chem. 2007, 79 (4), 1393-1400.
6. Craig, R.; Beavis, R. C. A method for reducing the time required to match protein sequences with tandem mass spectra. Rapid Commun. Mass Spectrom. 2003, 17 (20), 2310-2316.
7. David, N. Perkins; Pappin, D. J. C; Creasy, D. M.; Cottrell, J. S., Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999, 20 (18), 3551-3567.
8. Eng, J. K.; McCormack, A. L.; Yates, 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. 1994, 5 (11), 976- 989.
9. Frank, A.; Pevzner, P. PepNovo: de novo peptide sequencing via probabilistic network modeling. Anal. Chem. 2005, 77 (4), 964- 973.
10. Geer, L. Y.; Markey, S. P.; Kowalak, J. A.; Wagner, L.; Xu, M.; Maynard, D. M.; Yang, X.; Shi, W.; Bryant, S. H. Open mass spectrometry search algorithm. J. Proteome Res. 2004, 3 (5), 958- 964.
11. Ma, B.; Zhang, K.; Hendrie, C; Liang, C; Li, M.; Doherty-Kirby, A.; Lajoie, G. PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Commun. Mass Spectrom. 2003, 17(20), 2337-2342.
12. Tabb, D. L.; Saraf, A.; Yates, J. R. GutenTag: high-throughput sequence tagging via an empirically derived fragmentation model. Anal. Chem. 2003, 75 (23), 6415-6421.
13. Tanner, S.; Shu, H.; Frank, A.; Wang, L. C; Zandi, E.; Mumby, M.; Pevzner, P. A.; Bafna, V. InsPecT: identification of posttranslationally modified peptides from tandem mass spectra. Anal. Chem. 2005, 77 (14), 4626-4639.
14. Nesvizhskii, A. I.; Vitek, O.; Aebersold, R. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 2007, 4 (10), 787-797.
15. Taylor, J. A.; Johnson, R. S. Sequence database searches via de novo peptide sequencing by tandem mass spectrometry. Rapid Commun. Mass Spectrom. 1997, 11 (9), 1067-1075.
16. Taylor, J. A.; Johnson, R. S. Implementation and uses of automated de novo peptide sequencing by tandem mass spectrometry. Anal. Chem. 2001, 73 (11), 2594-2604.
17. Dancik, V.; Addona, T. A.; Clauser, K. R.; Vath, J. E.; Pevzner, P. A. De novo peptide sequencing via tandem mass spectrometry. J. Comput. Biol. 1999, 6 (3-4), 327-342.
18. Mann, M.; Wilm, M. Error-tolerant identification of peptides in sequence databases by peptide sequence tags. Anal. Chem. 1994, 66 (24), 4390-4399.
19. Han, Y.; Ma, B.; Zhang, K. SPIDER: software for protein identification from sequence tags with de novo sequencing error. J. Bioinf. Comput. Biol. 2005, 3 (3), 697-716.
20. Searle, B. C; Dasari, S.; Wilmarth, P. A.; Turner, M.; Reddy, A. P.; David, L. L.; Nagalla, S. R. Identification of protein modifications using MS/MS de novo sequencing and the OpenSea alignment algorithm. J. Proteome Res. 2005, 4 (2), 546-554.
21. Sunyaev, S.; Liska, A. J.; Golod, A.; Shevchenko, A.; Shevchenko, A. MultiTag: multiple error-tolerant sequence tag search for the sequence-similarity identification of proteins by mass spectrometry. Anal. Chem. 2003, 75 (6), 1307-1315.
22. Savitski, M. M.; Nielsen, M. L.; Zubarev, R. A. New data base- independent, sequence tag-based scoring of peptide MS/MS data validates Mowse scores, recovers below threshold data, singles out modified peptides, and assesses the quality of MS/MS techniques. Mol. Cell. Proteomics 2005, 4 (8), 1180-1188.
23. Shilov, I. V.; Seymour, S. L.; Patel, A. A.; Loboda, A.; Tang, W. H.; Keating, S. P.; Hunter, C. L.; Nuwaysir, L. M.; Schaeffer, D. A. The paragon algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Mol. Cell. Proteomics 2007, 6 (9), 1638-1655.
24. Frank, A.; Tanner, S.; Bafna, V.; Pevzner, P. Peptide sequence tags for fast database search in mass-spectrometry. J. Proteome Res. 2005, 4 (4), 1287-1295.
25. Chong, K. F.; Ning, K.; Leong, H. W.; Pevzner, P. Modeling and characterization of multi-charge mass spectra for Peptide sequencing. J. Bioinf. Comput. Biol. 2006, 4 (6), 1329-52.
26. Ning, K.; Chong, K. F.; Leong, H. W. In A Database Search Algorithm for Identification of Peptides with Multiple Charges Using Tandem Mass Spectrometry, BioDM, Singapore; Li, J., Yang, Q., Tan, A.-H., Eds.; Springer-Verlag Berlin Heidelberg: Singapore, 2006; pp 2- 13.
27. Choi, H.; Ghosh, D.; Nesvizhskii, A. I. Statistical validation of peptide identifications in large-scale proteomics using target-decoy database search strategy and flexible mixture modeling. J. Proteome Res. 2008, 7, 286-292.
28. Klimek, J.; Eddes, J. S.; Hohmann, L.; Jackson, J.; Peterson, A.; Letarte, S.; Gafken, P. R.; Katz, J. E.; Mallick, P.; Lee, H.; Schmidt, A.; Ossola, R.; Eng, J. K.; Aebersold, R.; Martin, D. B. The standard protein mix database: a diverse data set to assist in the production of improved peptide and protein identification software tools. J. Proteome Res. 2008, 7, 96-103.
29. Ulintz, P. J.; Bodenmiller, B.; Andrews, P. C; Aebersold, R.; Nesvizhskii, A. I. Investigating MS2-MS3 matching statistics: a model for coupling consecutive stage mass spectrometry data for increased peptide identification confidence. Mol. Cell. Proteomics 2008, 7, 71-87.
30. Colinge, J.; Magnin, J.; Dessingy, T.; Giron, M.; Masselot, A. Improved peptide charge state assignment. Proteomics 2003, 3 (8), 1434-1440.
31. Sadygov, R. G.; Eng, J.; Durr, E.; Saraf, A.; McDonald, H.; MacCoss, M. J.; Yates, J. R. Code developments to improve the efficiency of automated MS/MS spectra interpretation. J. Proteome Res. 2002, 1 (3), 211-215.
32. Nesvizhskii, A. I.; Keller, A.; Kolker, E.; Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 2003, 75 (17), 4646-4658.
33. Choi, H.; Nesvizhskii, A. I. Semi-supervised model-based validation of peptide identifications in mass spectrometry-based proteomics. J. Proteome Res. 2008, 7, 254-265.
34. Martin, D. B.; Eng, J. K.; Nesvizhskii, A. I.; Gemmill, A.; Aebersold, R. Investigation of neutral loss during collision-induced dissociation of peptide ions. Anal. Chem. 2005, 77 (15), 4870-4882.
35. Huang, Y. Y.; Triscari, J. M.; Tseng, G. C; Pasa-Tolic, L.; Lipton, M. S.; Smith, R. D.; Wysocki, V. H. Statistical characterization of the charge state and residue dependence of low-energy CID peptide dissociation patterns. Anal. Chem. 2005, 77 (18), 5800- 5813.
36. Nesvizhskii, A. I.; Roos, F. F.; Grossmann, J.; Vogelzang, M.; Eddes, J. S.; Gruissem, W.; Baginsky, S.; Aebersold, R. Dynamic spectrum quality assessment and iterative computational analysis of shotgun proteomic data: toward more efficient identification of post- translational modifications, sequence polymorphisms, and novel peptides. Mol. Cell. Proteomics 2006, 5 (4), 652-670.
37. Tanner, S. W. Efficient and Accurate Bioinformatics Algorithms for Peptide Mass Spectrometry. Ph.D. Dissertation, University of California, San Diego, CA, 2007.
38. Gentzel, M.; Kocher, T.; Ponnusamy, S.; Wilm, M. Preprocessing of tandem mass spectrometric data to support automatic protein identification. Proteomics 2003, 3 (8), 1597-1610.
39. Mujezinovic, N.; Raldl, G.; Hutchins, J. R. A.; Peters, J. M.; Mechtler, K.; Eisenhaber, F. Cleaning of raw peptide MS/MS spectra: Improved protein identification following deconvolution of multiply charged peaks, isotope clusters, and removal of background noise. Proteomics 2006, 6 (19), 5117-5131.
40. Aho, A.; Corasick, M. Efficient string matching: an aid to bibliographic search. Commun. ACM 1975, 18, 333-340.
41. Payne, S. H.; Yau, M.; Smolka, M. B.; Tanner, S.; Zhou, H.; Bafna, V. Phosphorylation-specific MS/MS scoring for rapid and accurate phosphoproteome analysis.J. Proteome Res. 2008, 78, 3373-3381.