Tandem mass spectral libraries of peptides and their roles in proteomics research

Mass Spectrometry Reviews

Volumn 36, Issue 5, 2017, Pages 634-648

  Shao, Wenguang ^a,b   Lam, Henry ^b  

^a ETH ZURICH   (Switzerland)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

proteomics; spectral library; tandem mass spectrometry

Indexed keywords

LIBRARIES; MASS SPECTROMETRY; MOLECULAR BIOLOGY; PEPTIDES; SPECTROMETRY;

COMPUTATIONAL PROCEDURES; FRAGMENTATION PATTERNS; HISTORICAL DEVELOPMENT; PROTEOMIC TECHNOLOGIES; PROTEOMICS; SPECTRAL LIBRARIES; TANDEM MASS SPECTRA; TANDEM MASS SPECTROMETRY;

PROTEINS;

PEPTIDE;

ALGORITHM; ANIMAL; CHEMISTRY; COMPUTER INTERFACE; DATA MINING; HISTORY; METABOLISM; PHYLOGENY; PROCEDURES; PROTEIN DATABASE; PROTEIN PROCESSING; PROTEOMICS; SOFTWARE; STATISTICS AND NUMERICAL DATA; TANDEM MASS SPECTROMETRY;

ALGORITHMS; ANIMALS; DATA MINING; DATABASES, PROTEIN; HISTORY, 20TH CENTURY; HISTORY, 21ST CENTURY; PEPTIDES; PHYLOGENY; PROTEIN PROCESSING, POST-TRANSLATIONAL; PROTEOMICS; SOFTWARE; TANDEM MASS SPECTROMETRY; USER-COMPUTER INTERFACE;

EID: 84979234626     PISSN: 02777037     EISSN: 10982787     Source Type: Journal    
DOI: 10.1002/mas.21512     Document Type: Review

References (102)

1. Ahrné E, Masselot A, Binz PA, Müller M, Lisacek F. 2009. A simple workflow to increase MS2 identification rate by subsequent spectral library search. Proteomics 9:1731–1736.
2. Ahrné E, Nikitin F, Lisacek F, Müller M. 2011a. QuickMod: A tool for open modification spectrum library searches. J Proteome Res 10:2913–2921.
3. Ahrné E, Ohta Y, Nikitin F, Scherl A, Lisacek F, Müller M. 2011b. An improved method for the construction of decoy peptide MS/MS spectra suitable for the accurate estimation of false discovery rates. Proteomics 11:4085–4095.
4. Arnold RJ, Jayasankar N, Aggarwal D, Tang H, Radivojac P. in press. A machine learning approach to predicting peptide fragmentation spectra. Pac Symp Biocomput 2006:219–230.
5. Askenazi M, Linial M. 2012. Implicit biology in peptide spectral libraries. Anal Chem 84:7919–7925.
6. Bandeira N, Tsur D, Frank A, Pevzner PA. 2007. Protein identification by spectral networks analysis. Proc Natl Acad Sci USA 104:6140–6145.
7. Baumgardner LA, Shanmugam AK, Lam H, Eng JK, Martin DB. 2011. Fast parallel tandem mass spectral library searching using GPU hardware acceleration. J Proteome Res 10:2882–2888.
8. Beer I, Barnea E, Ziv T, Admon A. 2004. Improving large-scale proteomics by clustering of mass spectrometry data. Proteomics 960:950.
9. Bruderer R, Bernhardt OM, Gandhi T, Miladinović SM, Cheng L-Y, Messner S, Ehrenberger T, Zanotelli V, Butscheid Y, Escher C. 2015. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol Cell Proteomics 14:1400–1410.
10. Caron E, Espona L, Kowalewski DJ, Schuster H, Ternette N, Alpízar A, Schittenhelm RB, Ramarathinam SH, Arlehamn CSL, Koh CC. 2015. An open-source computational and data resource to analyze digital maps of immunopeptidomes. Elife 4:e07661.
11. Cheng C-Y, Tsai C-F, Chen Y-J, Sung T-Y, Hsu W-L. 2013. Spectrum-based method to generate good decoy libraries for spectral library searching in peptide identifications. J Proteome Res 12:2305–2310.
12. Cho J-Y, et al. 2015. Combination of multiple spectral libraries improves the current search methods used to identify missing proteins in the chromosome-centric human proteome project. J Proteome Res 14:4959–4966.
13. Choi H, Nesvizhskii AI. 2007. Semisupervised model-based validation of peptide identifications in mass spectrometry-based proteomics. J Proteome Res 7:254–265.
14. Craig R, Cortens JP, Beavis RC. 2004. Open source system for analyzing, validating, and storing protein identification data. J Proteome Res 3:1234–1242.
15. Craig R, Cortens J, Fenyo D, Beavis RC. 2006. Using annotated peptide mass spectrum libraries for protein identification. J Proteome Res 5:1843–1849.
16. Dasari S, Chambers MC, Martinez MA, Carpenter KL, Ham A-JL, Vega-Montoto LJ, Tabb DL. 2012. Pepitome: Evaluating improved spectral library search for identification complementarity and quality assessment. J Proteome Res 11:1686–1695.
17. Desiere F, et al. 2006. The peptideatlas project. Nucleic Acids Res 34:D655–D658.
18. Deutsch EW, Lam H, Aebersold R. 2008. PeptideAtlas: A resource for target selection for emerging targeted proteomics workflows. EMBO Rep 434:429.
19. Deutsch EW. 2010. Trans-proteomic pipeline supports and improves analysis of electron transfer dissociation datasets. Proteomics 10:1190–1195.
20. Egertson JD, Kuehn A, Merrihew GE, Bateman NW, MacLean BX, Ting YS, Canterbury JD, Marsh DM, Kellmann M, Zabrouskov V. 2013. Multiplexed MS/MS for improved data-independent acquisition. Nat Methods 10:744–746.
21. Elias JE, Gygi SP. 2007. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods 4:207–214.
22. Elias JE, Gibbons FD, King OD, Roth FP, Gygi SP. 2004. Intensity-based protein identification by machine learning from a library of tandem mass spectra. Nat Biotechnol 22:214–219.
23. Falkner JA, Falkner JW, Yocum AK, Andrews PC. 2008. A spectral clustering approach to MS/MS identification of post-translational modifications. J Proteome Res 7:4614–4622.
24. Frank R. 2002. The SPOT-synthesis technique: Synthetic peptide arrays on membrane supports—Principles and applications. J Immunol Methods 267:13–26.
25. Frank AM. 2009. A ranking-based scoring function for peptide—Spectrum matches. J Proteome Res 8:2241–2252.
26. Frank AM, Monroe ME, Shah AR, Carver JJ, Bandeira N, Moore RJ, Anderson GA, Smith RD, Pevzner PA. 2011. Spectral archives: Extending spectral libraries to analyze both identified and unidentified spectra. Nat Methods 8:587–591.
27. Fremout W, Dhaenens M, Saverwyns S, Sanyova J, Vandenabeele P, Deforce D, Moens L. 2012. Development of a dedicated peptide tandem mass spectral library for conservation science. Anal Chim Acta 728:39–48.
28. Frewen BE, Merrihew GE, Wu CC, Noble WS, MacCoss MJ. 2006. Analysis of peptide MS/MS spectra from large-scale proteomics experiments using spectrum libraries. Anal Chem 78:5678–5684.
29. Geiger T, Cox J, Mann M. 2010. Proteomics on an Orbitrap benchtop mass spectrometer using all-ion fragmentation. Mol Cell Proteomics 9:2252–2261.
30. Gillet LC, Navarro P, Tate S, Rost H, Selevsek N, Reiter L, Bonner R, Aebersold R. 2012. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: A new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 11:O111.
31. Griss J, Foster JM, Hermjakob H, Vizcaíno JA. 2013. PRIDE Cluster: Building a consensus of proteomics data. Nat Methods 96:95.
32. Gunaratne J, Schmidt A, Quandt A, Neo SP, Saraç ÖS, Gracia T, Loguercio S, Ahrné E, Xia RLH, Tan KH. 2013. Extensive mass spectrometry-based analysis of the fission yeast proteome the schizosaccharomyces pombe peptideAtlas*. Mol Cell Proteomics 12:1741–1751.
33. Heller SR. 1972. Conversational mass spectral retrieval system and its use as an aid in structure determination. Anal Chem 44:1951–1961.
34. Hu Y, Lam H. 2013. Expanding tandem mass spectral libraries of phosphorylated peptides: Advances and applications. J Proteome Res 12:5971–5977.
35. Hu Y, Li Y, Lam H. 2011. A semi-empirical approach for predicting unobserved peptide MS/MS spectra from spectral libraries. Proteomics 4711:4702.
36. Hu Y, Pai P, Lam H. 2014. A new N-linked glycopeptides identification tool for large-scale glycoproteomics analysis by spectral library searching. 62nd Annual Conference of the American Society for Mass Spectrometry (ASMS).
37. Ji C, Arnold RJ, Sokoloski KJ, Hardy RW, Tang H, Radivojac P. 2013. Extending the coverage of spectral libraries: A neighbor-based approach to predicting intensities of peptide fragmentation spectra. Proteomics 765:756.
38. Jones AR, Eisenacher M, Mayer G, Kohlbacher O, Siepen J, Hubbard SJ, Selley JN, Searle BC, Shofstahl J, Seymour SL. 2012. The mzIdentML data standard for mass spectrometry-based proteomics results. Mol Cell Proteomics 11:M111.
39. Käll L, Canterbury JD, Weston J, Noble WS, MacCoss MJ. 2007. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods 4:923–925.
40. Keller A, Nesvizhskii AI, Kolker E, Aebersold R. 2002. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem 74:5383–5392.
41. Lam H, Deutsch EW, Aebersold R. 2009. Artificial decoy spectral libraries for false discovery rate estimation in spectral library searching in proteomics. J Proteome Res 9:605–610.
42. Lam H, Deutsch EW, Eddes JS, Eng JK, King N, Stein SE, Aebersold R. 2007. Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics 7:655–667.
43. Lam H, Deutsch EW, Eddes JS, Eng JK, Stein SE, Aebersold R. 2008. Building consensus spectral libraries for peptide identification in proteomics. Nat Methods 5:873–875.
44. Lee S, Kwon M-S, Lee H-J, Paik Y-K, Tang H, Lee JK, Park T. 2011. Enhanced peptide quantification using spectral count clustering and cluster abundance. BMC Bioinformatics 12:423.
45. Li H, Zong NC, Liang X, Kim AK, Choi JH, Deng N, Zelaya I, Lam M, Duan H, Ping P. 2013. A novel spectral library workflow to enhance protein identifications. J Proteomics Res 81:173–184.
46. Ma CWM, Lam H. 2014. Hunting for unexpected post-translational modifications by spectral library searching with tier-wise scoring. J Proteome Res 13:2262–2271.
47. Ma Z-Q, Dasari S, Chambers MC, Litton MD, Sobecki SM, Zimmerman LJ, Halvey PJ, Schilling B, Drake PM, Gibson BW. 2009. IDPicker 2.0: Improved protein assembly with high discrimination peptide identification filtering. J Proteome Res 8:3872–3881.
48. MacLean B, et al. 2010. Skyline: An open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26:966–968.
49. Martens L, Chambers M, Sturm M, Kessner D, Levander F, Shofstahl J, Tang WH, Römpp A, Neumann S, Pizarro AD. 2011. MzML—A community standard for mass spectrometry data. Mol Cell Proteomics 10:R110.
50. Marx V. 2013. Targeted proteomics. Nat Methods 10:19–22.
51. Marx H, Lemeer S, Schliep JE, Matheron L, Mohammed S, Cox J, Mann M, Heck AJ, Kuster B. 2013. A large synthetic peptide and phosphopeptide reference library for mass spectrometry-based proteomics. Nat Biotechnol 31:557–564.
52. Mead JA, Bianco L, Ottone V, Barton C, Kay RG, Lilley KS, Bond NJ, Bessant C. 2009. MRMaid, the web-based tool for designing multiple reaction monitoring (MRM) transitions. Mol Cell Proteomics 8:696–705.
53. Menschaert G, Hayakawa E, Schoofs L, Van Criekinge W, Baggerman G. 2012. Spectral clustering in peptidomics studies allows homology searching and modification profiling: HomClus, a versatile tool. J Proteome Res 11:2774–2785.
54. Muntel J, Xuan Y, Berger ST, Reiter L, Bachur R, Kentsis A, Steen H. 2015. Advancing urinary protein biomarker discovery by data-independent acquisition on a quadrupole-orbitrap mass spectrometer. J Proteome Res 14:4752–4762.
55. Nesvizhskii AI, Vitek O, Aebersold R. 2007. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat Methods 4:787–797.
56. Omenn GS. 2014. The strategy, organization, and progress of the HUPO Human Proteome Project. J Proteome 100:3–7.
57. Önder Ö, Shao W, Kemps BD, Lam H, Brisson D. 2013. Identifying sources of tick blood meals using unidentified tandem mass spectral libraries. Nat Commun 4:1746.
58. Önder Ö, Shao W, Lam H, Brisson D. 2014. Tracking the sources of blood meals of parasitic arthropods using shotgun proteomics and unidentified tandem mass spectral libraries. Nat Protoc 850:842.
59. Palmblad M, Deelder AM. 2012. Molecular phylogenetics by direct comparison of tandem mass spectra. Rapid Commun Mass Spectrom 26:728–732.
60. Panchaud A, Scherl A, Shaffer SA, von Haller PD, Kulasekara HD, Miller SI, Goodlett DR. 2009. Precursor acquisition independent from ion count: How to dive deeper into the proteomics ocean. Anal Chem 81:6481–6488.
61. Panse C, Trachsel C, Grossmann J. 2015. R. specL-An R/Bioconductor package to prepare peptide spectrum matches for use in targeted proteomics. Bioinformatics 31:2228–2231.
62. Pedrioli PG, Eng JK, Hubley R, Vogelzang M, Deutsch EW, Raught B, Pratt B, Nilsson E, Angeletti RH, Apweiler R. 2004. A common open representation of mass spectrometry data and its application to proteomics research. Nat Biotechnol 22:1459–1466.
63. Picotti P, Aebersold R. 2012. Selected reaction monitoring-based proteomics: Workflows, potential, pitfalls and future directions. Nat Methods 9:555–566.
64. Picotti P, Clément-Ziza M, Lam H, Campbell DS, Schmidt A, Deutsch EW, Röst H, Sun Z, Rinner O, Reiter L. 2013. A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature 494:266–270.
65. Röst HL, Rosenberger G, Navarro P, Gillet L, Miladinović SM, Schubert OT, Wolski W, Collins BC, Malmström J, Malmström L. 2014. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat Biotechnol 32:219–223.
66. Reiter L, Rinner O, Picotti P, Hüttenhain R, Beck M, Brusniak M-Y, Hengartner MO, Aebersold R. 2011. MProphet: Automated data processing and statistical validation for large-scale SRM experiments. Nat Methods 8:430–435.
67. Rosenberger G, et al. 2014. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci Data 1:140031.
68. Schubert OT, Gillet LC, Collins BC, Navarro P, Rosenberger G, Wolski WE, Lam H, Amodei D, Mallick P, MacLean B. 2015. Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat Protoc 10:426–441.
69. Shao W, Lam H. 2013. Denoising peptide tandem mass spectra for spectral libraries: A Bayesian approach. J Proteome Res 12:3223–3232.
70. Shao W, Zhu K, Lam H. 2013. Refining similarity scoring to enable decoy-free validation in spectral library searching. Proteomics 3283:3273.
71. Shao W, Zhang M, Lam H, Lau SC. 2015. A peptide identification-free, genome sequence-independent shotgun proteomics workflow for strain-level bacterial differentiation. Sci Rep 5:14337.
72. Sherwood CA, Eastham A, Lee LW, Peterson A, Eng JK, Shteynberg D, Mendoza L, Deutsch EW, Risler J, Tasman N. 2009. MaRiMba: A software application for spectral library-based MRM transition list assembly. J Proteome Res 8:4396–4405.
73. Silva JC, Gorenstein MV, Li G-Z, Vissers JP, Geromanos SJ. 2006. Absolute quantification of proteins by LCMSE a virtue of parallel MS acquisition. Mol Cell Proteomics 5:144–156.
74. Srikumar T, Jeram SM, Lam H, Raught B. 2010. A ubiquitin and ubiquitin-like protein spectral library. Proteomics 342:337.
75. Stein S, Rudnick P. 2009. (NIST, Gaithers-burg, MD, 2009).
76. Stein SE, Scott DR. 1994. Optimization and testing of mass spectral library search algorithms for compound identification. J Am Soc Mass Spectrom 5:859–866.
77. Tabb DL, MacCoss MJ, Wu CC, Anderson SD, Yates JR. 2003. Similarity among tandem mass spectra from proteomic experiments: Detection, significance, and utility. Anal Chem 75:2470–2477.
78. Toghi Eshghi S, Shah P, Yang W, Li X, Zhang H. 2015. GPQuest: A spectral library matching algorithm for site-specific assignment of tandem mass spectra to intact N-glycopeptides. Anal Chem 87:5181–5188.
79. Van Steendam K, De Wulf O, Dhaenens M, Deforce D. 2014. Species identification from hair by means of spectral library searches. Int J Legal Med 1–6.
80. Venable JD, Dong M-Q, Wohlschlegel J, Dillin A, Yates JR. 2004. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods 1:39–45.
81. Ventosa M, et al. 2013. Farm animal proteomics. Springer: Wageningen Academic Publishers. pp. 67–70
82. Vu TN, Bittremieux W, Valkenborg D, Goethals B, Lemiere F, Laukens K. 2014. Efficient reduction of candidate matches in peptide spectrum library searching using the top k most intense peaks. J Proteome Res 13:4175–4183.
83. Wang M, Bandeira N. 2013. Spectral library generating function for assessing spectrum-spectrum match significance. J Proteome Res 12:3944–3951.
84. Wang J, Tucholska M, Knight JD, Lambert J-P, Tate S, Larsen B, Gingras A-C, Bandeira N. 2015. MSPLIT-DIA: Sensitive peptide identification for data-independent acquisition. Nat Methods 12:1106–1108.
85. Weisbrod CR, Eng JK, Hoopmann MR, Baker T, Bruce JE. 2012. Accurate peptide fragment mass analysis: Multiplexed peptide identification and quantification. J Proteome Res 11:1621–1632.
86. Wu X, Tseng C-W, Edwards N. 2007. HMMatch: Peptide identification by spectral matching of tandem mass spectra using hidden Markov models. J Comput Biol 14:1025–1043.
87. Wulff T, Nielsen ME, Deelder AM, Jessen F, Palmblad M. 2013a. Authentication of fish products by large-scale comparison of tandem mass spectra. J Proteome Res 12:5253–5259.
88. Wulff T, Jessen F, Palmblad M, Nielsen ME. 2013b. Farm animal proteomics. Springer: Wageningen Academic Publishers. pp.71–74.
89. Wysocki VH, Tsaprailis G, Smith LL, Breci LA. 2000. Special feature: Commentary—Mobile and localized protons: A framework for understanding peptide dissociation. J Mass Spectrom 35:1399–1406.
90. Xu M, Li L. 2011. Validation of peptide MS/MS spectra using metabolic isotope labeling for spectral matching-based shotgun proteome analysis. J Proteome Res 10:3632–3641.
91. Yang X, Neta P, Stein SE. 2014. Quality control for building libraries from electrospray ionization tandem mass spectra. Anal Chem 86:6393–6400.
92. Yates JR, Morgan SF, Gatlin CL, Griffin PR, Eng JK. 1998. Method to compare collision-induced dissociation spectra of peptides: Potential for library searching and subtractive analysis. Anal Chem 70:3557–3565.
93. Ye D, Fu Y, Sun R-X, Wang H-P, Yuan Z-F, Chi H, He S-M. 2010. Open MS/MS spectral library search to identify unanticipated post-translational modifications and increase spectral identification rate. Bioinformatics 26:i399–i406.
94. Yen C-Y, Houel S, Ahn NG, Old WM. 2011. Spectrum-to-spectrum searching using a proteome-wide spectral library. Mol Cell Proteomics 10:M111.
95. Yen C-Y. 2009. A simulated MS/MS library for spectrum-to-spectrum searching in large scale identification of proteins. Mol Cell Proteomics 8:857–869.
96. Zhang Z, Shah B. 2010. Prediction of collision-induced dissociation spectra of common N-glycopeptides for glycoform identification. Anal Chem 82:10194–10202.
97. Zhang X, Li Y, Shao W, Lam H. 2011. Understanding the improved sensitivity of spectral library searching over sequence database searching in proteomics data analysis. Proteomics 11:1075–1085.
98. Zhang Z. 2004. Prediction of low-energy collision-induced dissociation spectra of peptides. Anal Chem 76:3908–3922.
99. Zhang Z. 2005. Prediction of low-energy collision-induced dissociation spectra of peptides with three or more charges. Anal Chem 77:6364–6373.
100. Zhang Z. 2010. Prediction of electron-transfer/capture dissociation spectra of peptides. Anal Chem 82:1990–2005.
101. Zhang Z. 2011. Prediction of collision-induced-dissociation spectra of peptides with post-translational or process-induced modifications. Anal Chem 83:8642–8651.
102. van der Plas-Duivesteijn SJ. 2014. Identifying proteins in zebrafish embryos using spectral libraries generated from dissected adult organs and tissues. J Proteome Res 1544:1537.