Efficient discovery of abundant post-translational modifications and spectral pairs using peptide mass and retention time differences

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• 作者：Yan Fu (1) (3)
  Wei Jia (2)
  Zhuang Lu (2)
  Haipeng Wang (1)
  Zuofei Yuan (1)
  Hao Chi (1)
  You Li (1)
  Liyun Xiu (1)
  Wenping Wang (1)
  Chao Liu (1)
  Leheng Wang (1)
  Ruixiang Sun (1)
  Wen Gao (1)
  Xiaohong Qian (2)
  Si-Min He (1) (3)
  
• 刊名：BMC Bioinformatics
• 出版年：2009
• 出版时间：January 2009
• 年：2009
• 卷：10
• 期：1-supp
• 全文大小：1372KB
• 参考文献：1. Aebersold R, Mann M: Mass spectrometry-based proteomics. / Nature 2003, 422:198鈥?07. CrossRef
  2. Mann M, Jensen ON: Proteomic analysis of post-translational modifications. / Nat Biotechnol 2003,21(3):255鈥?61. CrossRef
  3. Witze ES, Old WM, Resing KA, Ahn NG: Mapping protein post-translational modifications with mass spectrometry. / Nat Methods 2007,4(10):798鈥?06. CrossRef
  4. Eng JK, McCormack AL, Yates JR III: An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. / J Am Soc Mass Spectrom 1994, 5:976鈥?89. CrossRef
  5. Perkins DN, Pappin DJ, Creasy DM, Cottrell JS: Probability-based protein identification by searching sequence databases using mass spectrometry data. / Electrophoresis 1999, 20:3551鈥?567. CrossRef
  6. Craig R, Beavis RC: TANDEM: matching proteins with tandem mass spectra. / Bioinformatics 2004, 20:1466鈥?467. CrossRef
  7. Fu Y, Yang Q, Sun R, Li D, Zeng R, Ling CX, Gao W: Exploiting the kernel trick to correlate fragment ions for peptide identification via tandem mass spectrometry. / Bioinformatics 2004, 20:1948鈥?954. CrossRef
  8. Colinge J, Masselot A, Giron M, Dessingy T, Magnin J: OLAV: towards high-throughput tandem mass spectrometry data identification. / Proteomics 2003, 3:1454鈥?463. CrossRef
  9. Geer LY, Markey SP, Kowalak JA, Wagner L, Xu M, Maynard DM, Yang X, Shi W, Bryant SH: Open mass spectrometry search algorithm. / J Proteome Res 2004,3(5):958鈥?64. CrossRef
  10. Dancik V, Addona TA, Clauser KR, Vath JE, Pevzner PA: De novo peptide sequencing via tandem mass spectrometry. / J Comput Biol 1999, 6:327鈥?42. CrossRef
  11. Frank A, Pevzner P: PepNovo: de novo peptide sequencing via probabilistic network modeling. / Anal Chem 2005, 77:964鈥?73. CrossRef
  12. Ma B, Zhang KZ, Hendrie C, Liang CZ, Li M, Doherty-Kirby A, Lajoie G: PEAKS: powerful software for peptide de novo sequencing by MS/MS. / Rapid Commun Mass Spectrom 2003, 17:2337鈥?342. CrossRef
  13. Taylor JA, Johnson RS: Implementation and uses of automated de novo peptide sequencing by tandem mass spectrometry. / Anal Chem 2001, 73:2594鈥?604. CrossRef
  14. Hernandez P, Gras R, Frey J, Appel RD: Popitam: towards new heuristic strategies to improve protein identification from tandem mass spectrometry data. / Proteomics 2003, 3:870鈥?78. CrossRef
  15. Mann M, Wilm M: Error-tolerant identification of peptides in sequence databases by peptide sequence tags. / Anal Chem 1994, 66:4390鈥?399. CrossRef
  16. Tabb DL, Saraf A, Yates JR III: GutenTag: high-throughput sequence tagging via an empirically derived fragmentation model. / Anal Chem 2003, 75:6415鈥?421. CrossRef
  17. Tanner S, Shu H, Frank A, Wang LC, Zandi E, Mumby M, Pevzner PA, Bafna V: InsPecT: identification of posttranslationally modified peptides from tandem mass spectra. / Anal Chem 2005,77(14):4626鈥?639. CrossRef
  18. Keller A, Nesvizhskii AI, Kolker E, Aebersold R: Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database Search. / Anal Chem 2002, 74:5383鈥?392. CrossRef
  19. Savitski MM, Nielsen ML, Zubarev RA: ModifiComb, a new proteomic tool for mapping substoichiometric post-translational modifications, finding novel types of modifications, and fingerprinting complex protein mixtures. / Mol Cell Proteomics 2006,5(5):935鈥?48. CrossRef
  20. Bandeira N: Spectral networks: a new approach to de novo discovery of protein sequences and posttranslational modifications. / Biotechniques 2007.,42(6): 687, 689, 691 passim. CrossRef
  21. UNIMOD[http://www.unimod.org/]
  22. Tsur D, Tanner S, Zandi E, Bafna V, Pevzner PA: Identification of post-translational modifications by blind search of mass spectra. / Nat Biotechnol 2005,23(12):1562鈥?567. CrossRef
  23. Tanner S, Pevzner PA, Bafna V: Unrestrictive identification of post-translational modifications through peptide mass spectrometry. / Nat Protoc 2006,1(1):67鈥?2. CrossRef
  24. Searle BC, Dasari S, Turner M, Reddy AP, Choi D, Wilmarth PA, McCormack AL, David LL, Nagalla SR: High-throughput identification of proteins and unanticipated sequence modifications using a mass-based alignment algorithm for MS/MS de novo sequencing results. / Anal Chem 2004,76(8):2220鈥?230. CrossRef
  25. Han Y, Ma B, Zhang K: SPIDER: software for protein identification from sequence tags containing de Novo sequencing error. / IEEE 2004 Computational Systems Bioinformatics Conference: 2004 2004, 206鈥?15.
  26. Pevzner PA, Mulyukov Z, Dancik V, Tang CL: Efficiency of database search for identification of mutated and modified proteins via mass spectrometry. / Genome Res 2001, 11:290鈥?99. CrossRef
  27. Potthast F, Gerrits B, Hakkinen J, Rutishauser D, Ahrens CH, Roschitzki B, Baerenfaller K, Munton RP, Walther P, Gehrig P, / et al.: The Mass Distance Fingerprint: a statistical framework for de novo detection of predominant modifications using high-accuracy mass spectrometry. / J Chromatogr B Analyt Technol Biomed Life Sci 2007,854(1鈥?):173鈥?82.
  28. Bandeira N, Tsur D, Frank A, Pevzner P: A new approach to protein identification. / 10th Annual International Conference on Research in Computational Molecular Biology 2006.
  29. Jia W, Lu Z, Fu Y, / et al.: A Strategy for Precise and Large-Scale Identification of Core Fucosylated Glycoproteins. 2008, / in press.
  30. Medzihradszky KF, Spencer DI, Sharma SK, Bhatia J, Pedley RB, Read DA, Begent RH, Chester KA: Glycoforms obtained by expression in Pichia pastoris improve cancer targeting potential of a recombinant antibody-enzyme fusion protein. / Glycobiology 2004,14(1):27鈥?7. CrossRef
  31. Wang LH, Li DQ, Fu Y, Wang HP, Zhang JF, Yuan ZF, Sun RX, Zeng R, He SM, Gao W: pFind 2.0: a software package for peptide and protein identification via tandem mass spectrometry. / Rapid Commun Mass Spectrom 2007,21(18):2985鈥?991. CrossRef
  32. Elias JE, Gygi SP: Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. / Nat Methods 2007,4(3):207鈥?14. CrossRef
  
• 作者单位：Yan Fu (1) (3)
  Wei Jia (2)
  Zhuang Lu (2)
  Haipeng Wang (1)
  Zuofei Yuan (1)
  Hao Chi (1)
  You Li (1)
  Liyun Xiu (1)
  Wenping Wang (1)
  Chao Liu (1)
  Leheng Wang (1)
  Ruixiang Sun (1)
  Wen Gao (1)
  Xiaohong Qian (2)
  Si-Min He (1) (3)
  
  1. Institute of Computing Technology,Chinese Academy of Sciences, Beijing, 100190, PR China
  3. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing, 100190, PR China
  2. State Key Laboratory of Proteomics-Beijing Proteome Research Center-Beijing, Institute of Radiation Medicine, Beijing, 102206, PR China
  
• ISSN：1471-2105

文摘

Background Peptide identification via tandem mass spectrometry is the basic task of current proteomics research. Due to the complexity of mass spectra, the majority of mass spectra cannot be interpreted at present. The existence of unexpected or unknown protein post-translational modifications is a major reason. Results This paper describes an efficient and sequence database-independent approach to detecting abundant post-translational modifications in high-accuracy peptide mass spectra. The approach is based on the observation that the spectra of a modified peptide and its unmodified counterpart are correlated with each other in their peptide masses and retention time. Frequently occurring peptide mass differences in a data set imply possible modifications, while small and consistent retention time differences provide orthogonal supporting evidence. We propose to use a bivariate Gaussian mixture model to discriminate modification-related spectral pairs from random ones. Due to the use of two-dimensional information, accurate modification masses and confident spectral pairs can be determined as well as the quantitative influences of modifications on peptide retention time. Conclusion Experiments on two glycoprotein data sets demonstrate that our method can effectively detect abundant modifications and spectral pairs. By including the discovered modifications into database search or by propagating peptide assignments between paired spectra, an average of 10% more spectra are interpreted.