Optimization of TripleTOF spectral simulation and library searching for confident localization of phosphorylation sites
Autoři:
Ayano Takai aff001; Tomoya Tsubosaka aff001; Yasuhiro Hirano aff001; Naoki Hayakawa aff001; Fumitaka Tani aff001; Pekka Haapaniemi aff002; Veronika Suni aff002; Susumu Y. Imanishi aff001
Působiště autorů:
Faculty of Pharmacy, Meijo University, Nagoya, Japan
aff001; Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
aff002; Turku Centre for Computer Science, Turku, Finland
aff003
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0225885
Souhrn
Tandem mass spectrometry (MS/MS) has been used in analysis of proteins and their post-translational modifications. A recently developed data analysis method, which simulates MS/MS spectra of phosphopeptides and performs spectral library searching using SpectraST, facilitates confident localization of phosphorylation sites. However, its performance has been evaluated only on MS/MS spectra acquired using Orbitrap HCD mass spectrometers so far. In this study, we have investigated whether this approach would be applicable to another type of mass spectrometers, and optimized the simulation and search conditions to achieve sensitive and confident site localization. Synthetic phosphopeptides and enriched K562 cell phosphopeptides were analyzed using a TripleTOF 6600 mass spectrometer before and after enzymatic dephosphorylation. Dephosphorylated peptides identified by X!Tandem database searching were subjected to spectral simulation of all possible single phosphorylations using SimPhospho software. Phosphopeptides were identified and localized by SpectraST searching against a library of the simulated spectra. Although no synthetic phosphopeptide was localized at 1% false localization rate under the previous conditions, optimization of the spectral simulation and search conditions for the TripleTOF datasets achieved the localization and improved the sensitivity. Furthermore, the optimized conditions enabled sensitive localization of K562 phosphopeptides at 1% false discovery and localization rates. These results suggest that accurate phosphopeptide simulation of TripleTOF MS/MS spectra is possible and the simulated spectral libraries can be used in SpectraST searching for confident localization of phosphorylation sites.
Klíčová slova:
Data acquisition – Database searching – Phosphorylation – Sequence databases – Serine – Synthetic peptides – Peptide libraries – Mass spectrometers
Zdroje
1. Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature. 2003;422:198–207. doi: 10.1038/nature01511 12634793
2. Steen H, Mann M. The ABC's (and XYZ's) of peptide sequencing. Nat Rev Mol Cell Biol. 2004;5:699–711. doi: 10.1038/nrm1468 15340378
3. Zhang Y, Fonslow BR, Shan B, Baek M-C, Yates JR III. Protein Analysis by Shotgun/Bottom-up Proteomics. Chem Rev. 2013;113:2343–2394. doi: 10.1021/cr3003533 23438204
4. Engholm-Keller K, Larsen MR. Technologies and challenges in large-scale phosphoproteomics. Proteomics. 2013;13:910–931. doi: 10.1002/pmic.201200484 23404676
5. Roux PP, Thibault P. The coming of age of phosphoproteomics—from large data sets to inference of protein functions. Mol Cell Proteomics. 2013;12:3453–3464. doi: 10.1074/mcp.R113.032862 24037665
6. von Stechow L, Francavilla C, Olsen JV. Recent findings and technological advances in phosphoproteomics for cells and tissues. Expert Rev Proteomics. 2015;12:469–487. doi: 10.1586/14789450.2015.1078730 26400465
7. Riley NM, Coon JJ. Phosphoproteomics in the Age of Rapid and Deep Proteome Profiling. Anal Chem. 2016;88:74–94. doi: 10.1021/acs.analchem.5b04123 26539879
8. Potel CM, Lemeer S, Heck AJR. Phosphopeptide fragmentation and site localization by mass spectrometry; an update. Anal Chem. 2018;91:126–141. doi: 10.1021/acs.analchem.8b04746 30457327
9. Beausoleil SA, Villén J, Gerber SA, Rush J, Gygi SP. A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat Biotechnol. 2006;24:1285–1292. doi: 10.1038/nbt1240 16964243
10. Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, et al. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell. 2006;127:635–648. doi: 10.1016/j.cell.2006.09.026 17081983
11. Savitski MM, Lemeer S, Boesche M, Lang M, Mathieson T, Bantscheff M, et al. Confident phosphorylation site localization using the Mascot Delta Score. Mol Cell Proteomics. 2011;10:M110 003830. doi: 10.1074/mcp.M110.003830 21057138
12. Taus T, Köcher T, Pichler P, Paschke C, Schmidt A, Henrich C, et al. Universal and confident phosphorylation site localization using phosphoRS. J Proteome Res. 2011;10:5354–5362. doi: 10.1021/pr200611n 22073976
13. Fermin D, Walmsley SJ, Gingras AC, Choi H, Nesvizhskii AI. LuciPHOr: algorithm for phosphorylation site localization with false localization rate estimation using modified target-decoy approach. Mol Cell Proteomics. 2013;12:3409–3419. doi: 10.1074/mcp.M113.028928 23918812
14. Hunter T. Signaling—2000 and beyond. Cell. 2000;100:113–127. doi: 10.1016/s0092-8674(00)81688-8 10647936
15. de Thonel A, Ferraris SE, Pallari H-M, Imanishi SY, Kochin V, Hosokawa T, et al. Protein Kinase C zeta regulates Cdk5/p25 signaling during myogenesis. Molecular Biology of the Cell. 2010;21:1423–1434. doi: 10.1091/mbc.E09-10-0847 20200223
16. Tyagarajan SK, Ghosh H, Yevenes GE, Imanishi SY, Zeilhofer HU, Gerrits B, et al. Extracellular signal-regulated kinase and glycogen synthase kinase 3 beta regulate gephyrin postsynaptic aggregation and GABAergic synaptic function in a calpain-dependent mechanism. Journal of Biological Chemistry. 2013;288:9634–9647. doi: 10.1074/jbc.M112.442616 23408424
17. Kochin V, Shimi T, Torvaldson E, Adam SA, Goldman A, Pack C-G, et al. Interphase phosphorylation of lamin A. Journal of Cell Science. 2014;127:2683–2696. doi: 10.1242/jcs.141820 24741066
18. Sjoqvist M, Antfolk D, Ferraris S, Rraklli V, Haga C, Antila C, et al. PKC zeta regulates Notch receptor routing and activity in a Notch signaling-dependent manner. Cell Research. 2014;24:433–450. doi: 10.1038/cr.2014.34 24662486
19. Hyder CL, Kemppainen K, Isoniemi KO, Imanishi SY, Goto H, Inagaki M, et al. Sphingolipids inhibit vimentin-dependent cell migration. Journal of Cell Science. 2015;128:2057–2069. doi: 10.1242/jcs.160341 25908861
20. Lindqvist J, Imanishi SY, Torvaldson E, Malinen M, Remes M, Orn F, et al. Cyclin-dependent kinase 5 acts as a critical determinant of AKT-dependent proliferation and regulates differential gene expression by the androgen receptor in prostate cancer cells. Molecular Biology of the Cell. 2015;26:1971–1984. doi: 10.1091/mbc.E14-12-1634 25851605
21. Virtakoivu R, Mai A, Mattila E, De Franceschi N, Imanishi SY, Corthals G, et al. Vimentin-ERK signaling uncouples Slug gene regulatory function. Cancer Research. 2015;75:2349–2362. doi: 10.1158/0008-5472.CAN-14-2842 25855378
22. Santio NM, Landor SK-J, Vahtera L, Ylä-Pelto J, Paloniemi E, Imanishi SY, et al. Phosphorylation of Notch1 by Pim kinases promotes oncogenic signaling in breast and prostate cancer cells. Oncotarget. 2016;7:43220–43238. doi: 10.18632/oncotarget.9215 27281612
23. Kauko O, Laajala TD, Jumppanen M, Hintsanen P, Suni V, Haapaniemi P, et al. Label-free quantitative phosphoproteomics with novel pairwise abundance normalization reveals synergistic RAS and CIP2A signaling. Sci Rep. 2015;5:13099. doi: 10.1038/srep13099 26278961
24. Nguyen EV, Imanishi SY, Haapaniemi P, Yadav A, Saloheimo M, Corthals GL, et al. Quantitative site-specific phosphoproteomics of Trichoderma reesei signaling pathways upon induction of hydrolytic enzyme production. Journal of Proteome Research. 2016;15:457–467. doi: 10.1021/acs.jproteome.5b00796 26689635
25. Soderholm S, Kainov DE, Ohman T, Denisova OV, Schepens B, Kulesskiy E, et al. Phosphoproteomics to characterize host response during influenza A virus infection of human macrophages. Molecular & Cellular Proteomics. 2016;15:3203–3219.
26. Frewen BE, Merrihew GE, Wu CC, Noble WS, MacCoss MJ. Analysis of peptide MS/MS spectra from large-scale proteomics experiments using spectrum libraries. Anal Chem. 2006;78:5678–5684. doi: 10.1021/ac060279n 16906711
27. Craig R, Cortens JC, Fenyo D, Beavis RC. Using annotated peptide mass spectrum libraries for protein identification. J Proteome Res. 2006;5:1843–1849. doi: 10.1021/pr0602085 16889405
28. Lam H, Deutsch EW, Eddes JS, Eng JK, King N, Stein SE, et al. Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics. 2007;7:655–667. doi: 10.1002/pmic.200600625 17295354
29. Griss J. Spectral library searching in proteomics. Proteomics. 2016;16:729–740. doi: 10.1002/pmic.201500296 26616598
30. Ye D, Fu Y, Sun R, Wang H, Yuan Z, Chi H, et al. Open MS/MS spectral library search to identify unanticipated post-translational modifications and increase spectral identification rate. Bioinformatics. 2010;26:i399–406. doi: 10.1093/bioinformatics/btq185 20529934
31. Ahrné E, Nikitin F, Lisacek F, Müller M. QuickMod: A tool for open modification spectrum library searches. J Proteome Res. 2011;10:2913–2921. doi: 10.1021/pr200152g 21500769
32. Ma CWM, Lam H. Hunting for unexpected post-translational modifications by spectral library searching with tier-wise scoring. J Proteome Res. 2014;13:2262–2271. doi: 10.1021/pr401006g 24661115
33. Horlacher O, Lisacek F, Müller M. Mining Large Scale Tandem Mass Spectrometry Data for Protein Modifications Using Spectral Libraries. J Proteome Res. 2016;15:721–731. doi: 10.1021/acs.jproteome.5b00877 26653734
34. Bodenmiller B, Malmstrom J, Gerrits B, Campbell D, Lam H, Schmidt A, et al. PhosphoPep—a phosphoproteome resource for systems biology research in Drosophila Kc167 cells. Mol Syst Biol. 2007;3:139. doi: 10.1038/msb4100182 17940529
35. Bodenmiller B, Campbell D, Gerrits B, Lam H, Jovanovic M, Picotti P, et al. PhosphoPep—a database of protein phosphorylation sites in model organisms. Nat Biotechnol. 2008;26:1339–1340. doi: 10.1038/nbt1208-1339 19060867
36. Hummel J, Niemann M, Wienkoop S, Schulze W, Steinhauser D, Selbig J, et al. ProMEX: a mass spectral reference database for proteins and protein phosphorylation sites. BMC Bioinformatics. 2007;8:216. doi: 10.1186/1471-2105-8-216 17587460
37. Hu Y, Li Y, Lam H. A semi-empirical approach for predicting unobserved peptide MS/MS spectra from spectral libraries. Proteomics. 2011;11:4702–4711. doi: 10.1002/pmic.201100316 22038894
38. Hu Y, Lam H. Expanding tandem mass spectral libraries of phosphorylated peptides: advances and applications. J Proteome Res. 2013;12:5971–5977. doi: 10.1021/pr4007443 24125593
39. Suni V, Imanishi SY, Maiolica A, Aebersold R, Corthals GL. Confident site localization using a simulated phosphopeptide spectral library. J Proteome Res. 2015;14:2348–2359. doi: 10.1021/acs.jproteome.5b00050 25774671
40. Imanishi SY, Kochin V, Ferraris SE, de Thonel A, Pallari HM, Corthals GL, et al. Reference-facilitated phosphoproteomics: fast and reliable phosphopeptide validation by microLC-ESI-Q-TOF MS/MS. Mol Cell Proteomics. 2007;6:1380–1391. 17510049
41. Suni V, Suomi T, Tsubosaka T, Imanishi SY, Elo LL, Corthals GL. SimPhospho: a software tool enabling confident phosphosite assignment. Bioinformatics. 2018;34:2690–2692. doi: 10.1093/bioinformatics/bty151 29596608
42. Gillet LC, Navarro P, Tate S, Röst H, Selevsek N, Reiter L, et al. 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. 2012;11:O111 016717. doi: 10.1074/mcp.O111.016717 22261725
43. Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26:1367–1372. doi: 10.1038/nbt.1511 19029910
44. Rappsilber J, Ishihama Y, Mann M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal Chem. 2003;75:663–670. doi: 10.1021/ac026117i 12585499
45. Keller A, Eng J, Zhang N, Li XJ, Aebersold R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol Syst Biol. 2005;1:2005.0017. doi: 10.1038/msb4100024 16729052
46. Deutsch EW, Mendoza L, Shteynberg D, Farrah T, Lam H, Tasman N, et al. A guided tour of the Trans-Proteomic Pipeline. Proteomics. 2010;10:1150–1159. doi: 10.1002/pmic.200900375 20101611
47. Craig R, Beavis RC. TANDEM: matching proteins with tandem mass spectra. Bioinformatics. 2004;20:1466–1467. doi: 10.1093/bioinformatics/bth092 14976030
48. 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;74:5383–5392. doi: 10.1021/ac025747h 12403597
49. Lam H, Deutsch EW, Eddes JS, Eng JK, Stein SE, Aebersold R. Building consensus spectral libraries for peptide identification in proteomics. Nat Methods. 2008;5:873–875. doi: 10.1038/nmeth.1254 18806791
50. Lam H, Deutsch EW, Aebersold R. Artificial decoy spectral libraries for false discovery rate estimation in spectral library searching in proteomics. J Proteome Res. 2010;9:605–610. doi: 10.1021/pr900947u 19916561
51. Baumgardner LA, Shanmugam AK, Lam H, Eng JK, Martin DB. Fast parallel tandem mass spectral library searching using GPU hardware acceleration. J Proteome Res. 2011;10:2882–2888. doi: 10.1021/pr200074h 21545112
52. Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011;10:1794–1805. doi: 10.1021/pr101065j 21254760
53. Marx H, Lemeer S, Schliep JE, Matheron L, Mohammed S, Cox J, et al. A large synthetic peptide and phosphopeptide reference library for mass spectrometry-based proteomics. Nat Biotechnol. 2013;31:557–564. doi: 10.1038/nbt.2585 23685481
54. Desiere F, Deutsch EW, King NL, Nesvizhskii AI, Mallick P, Eng J, et al. The PeptideAtlas project. Nucleic Acids Res. 2006;34:D655–658. doi: 10.1093/nar/gkj040 16381952
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