MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics

Andy T Kong; Felipe V Leprevost; Dmitry M Avtonomov; Dattatreya Mellacheruvu; Alexey I Nesvizhskii

doi:10.1038/nmeth.4256

journal article Open Access Apr 10, 2017

MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics

Andy T Kong Felipe V Leprevost

Dmitry M Avtonomov Dattatreya Mellacheruvu

Alexey I Nesvizhskii

Nature Methods Vol. 14 No. 5 pp. 513-520 · Springer Science and Business Media LLC

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References

58

[1]

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

Alexey I. Nesvizhskii

Journal of Proteomics 2010 10.1016/j.jprot.2010.08.009

[2]

Eng, J.K., Searle, B.C., Clauser, K.R. & Tabb, D.L. A face in the crowd: recognizing peptides through database search. Mol. Cel. Proteomics 10, R111.009522 (2011). 10.1074/mcp.r111.009522

[3]

Skinner, O.S. & Kelleher, N.L. Illuminating the dark matter of shotgun proteomics. Nat. Biotechnol. 33, 717–718 (2015). 10.1038/nbt.3287

[4]

A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides

Joel M Chick, Deepak Kolippakkam, David P Nusinow et al.

Nature Biotechnology 2015 10.1038/nbt.3267

[5]

Griss, J. et al. Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets. Nat. Methods 13, 651–656 (2016). 10.1038/nmeth.3902

[6]

Nesvizhskii, A.I. et al. 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 5, 652–670 (2006). 10.1074/mcp.m500319-mcp200

[7]

Nielsen, M.L., Savitski, M.M. & Zubarev, R.A. Extent of modifications in human proteome samples and their effect on dynamic range of analysis in shotgun proteomics. Mol. Cell. Proteomics 5, 2384–2391 (2006). 10.1074/mcp.m600248-mcp200

[8]

Ning, K., Fermin, D. & Nesvizhskii, A.I. Computational analysis of unassigned high-quality MS/MS spectra in proteomic data sets. Proteomics 10, 2712–2718 (2010). 10.1002/pmic.200900473

[9]

Craig, R. & Beavis, R.C. TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20, 1466–1467 (2004). 10.1093/bioinformatics/bth092

[10]

Creasy, D.M. & Cottrell, J.S. Error tolerant searching of uninterpreted tandem mass spectrometry data. Proteomics 2, 1426–1434 (2002). 10.1002/1615-9861(200210)2:10<1426::aid-prot1426>3.0.co;2-5

[11]

Shortreed, M.R. et al. Global Identification of Protein Post-translational Modifications in a Single-Pass Database Search. J. Proteome Res. 14, 4714–4720 (2015). 10.1021/acs.jproteome.5b00599

[12]

Ahrné, E., Nikitin, F., Lisacek, F. & Müller, M. QuickMod: A tool for open modification spectrum library searches. J. Proteome Res. 10, 2913–2921 (2011). 10.1021/pr200152g

[13]

Bandeira, N., Tsur, D., Frank, A. & Pevzner, P.A. Protein identification by spectral networks analysis. Proc. Natl. Acad. Sci. USA 104, 6140–6145 (2007). 10.1073/pnas.0701130104

[14]

Savitski, M.M., Nielsen, M.L. & Zubarev, R.A. ModifiComb, a new proteomic tool for mapping substoichiometric post-translational modifications, finding novel types of modifications, and fingerprinting complex protein mixtures. Mol. Cell. Proteomics 5, 935–948 (2006). 10.1074/mcp.t500034-mcp200

[15]

Ma, C.W. & Lam, H. Hunting for unexpected post-translational modifications by spectral library searching with tier-wise scoring. J. Proteome Res. 13, 2262–2271 (2014). 10.1021/pr401006g

[16]

Tabb, D.L., Saraf, A. & Yates, J.R. III. GutenTag: high-throughput sequence tagging via an empirically derived fragmentation model. Anal. Chem. 75, 6415–6421 (2003). 10.1021/ac0347462

[17]

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. 79, 1393–1400 (2007). 10.1021/ac0617013

[18]

Dasari, S. et al. Sequence tagging reveals unexpected modifications in toxicoproteomics. Chem. Res. Toxicol. 24, 204–216 (2011). 10.1021/tx100275t

[19]

Na, S., Bandeira, N. & Paek, E. Fast multi-blind modification search through tandem mass spectrometry. Mol. Cell. Proteomics 11, M111.010199 (2012). 10.1074/mcp.m111.010199

[20]

Searle, B.C. et al. Identification of protein modifications using MS/MS de novo sequencing and the OpenSea alignment algorithm. J. Proteome Res. 4, 546–554 (2005). 10.1021/pr049781j

[21]

Chen, Y., Chen, W., Cobb, M.H. & Zhao, Y. PTMap--a sequence alignment software for unrestricted, accurate, and full-spectrum identification of post-translational modification sites. Proc. Natl. Acad. Sci. USA 106, 761–766 (2009). 10.1073/pnas.0811739106

[22]

Tanner, S., Pevzner, P.A. & Bafna, V. Unrestrictive identification of post-translational modifications through peptide mass spectrometry. Nat. Protoc. 1, 67–72 (2006). 10.1038/nprot.2006.10

[23]

Fu, Y. in Statistical Analysis in Proteomics (ed. K. Jung) 265–275 (Springer New York, New York, NY, 2016). 10.1007/978-1-4939-3106-4_17

[24]

Chi, H. et al. pFind-Alioth: A novel unrestricted database search algorithm to improve the interpretation of high-resolution MS/MS data. J. Proteomics 125, 89–97 (2015). 10.1016/j.jprot.2015.05.009

[25]

Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment

Jürgen Cox, Nadin Neuhauser, Annette Michalski et al.

Journal of Proteome Research 2011 10.1021/pr101065j

[26]

McIlwain, S. et al. Crux: rapid open source protein tandem mass spectrometry analysis. J. Proteome Res. 13, 4488–4491 (2014). 10.1021/pr500741y

[27]

Eng, J.K., Jahan, T.A. & Hoopmann, M.R. Comet: an open-source MS/MS sequence database search tool. Proteomics 13, 22–24 (2013). 10.1002/pmic.201200439

[28]

Fu, Y. & Qian, X. Transferred subgroup false discovery rate for rare post-translational modifications detected by mass spectrometry. Mol. Cell. Proteomics 13, 1359–1368 (2014). 10.1074/mcp.o113.030189

[29]

Vaudel, M. et al. PeptideShaker enables reanalysis of MS-derived proteomics data sets. Nat. Biotechnol. 33, 22–24 (2015). 10.1038/nbt.3109

[30]

Diament, B.J. & Noble, W.S. Faster SEQUEST searching for peptide identification from tandem mass spectra. J. Proteome Res. 10, 3871–3879 (2011). 10.1021/pr101196n

[31]

Tsou, C.C. et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat. Methods 12, 258–264, 7, 264 (2015). 10.1038/nmeth.3255

[32]

Houel, S. et al. Quantifying the impact of chimera MS/MS spectra on peptide identification in large-scale proteomics studies. J. Proteome Res. 9, 4152–4160 (2010). 10.1021/pr1003856

[33]

Avtonomov, D.M., Raskind, A. & Nesvizhskii, A.I. BatMass: a Java software platform for LC-MS data visualization in proteomics and metabolomics. J. Proteome Res. 15, 2500–2509 (2016). 10.1021/acs.jproteome.6b00021

[34]

Zhang, B., Pirmoradian, M., Chernobrovkin, A. & Zubarev, R.A. DeMix workflow for efficient identification of cofragmented peptides in high resolution data-dependent tandem mass spectrometry. Mol. Cell. Proteomics 13, 3211–3223 (2014). 10.1074/mcp.o114.038877

[35]

Bogdanow, B., Zauber, H. & Selbach, M. Systematic errors in peptide and protein identification and quantification by modified peptides. Mol. Cell. Proteomics 15, 2791–2801 (2016). 10.1074/mcp.m115.055103

[36]

Nesvizhskii, A.I. Proteogenomics: concepts, applications and computational strategies. Nat. Methods 11, 1114–1125 (2014). 10.1038/nmeth.3144

[37]

Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep. 8, 1583–1594 (2014). 10.1016/j.celrep.2014.07.036

[38]

Lawrence, R.T. et al. The proteomic landscape of triple-negative breast cancer. Cell Rep. 11, 630–644 (2015). 10.1016/j.celrep.2015.03.050

[39]

Pozniak, Y. et al. System-wide clinical proteomics of breast cancer reveals global remodeling of tissue homeostasis. Cell Syst. 2, 172–184 (2016). 10.1016/j.cels.2016.02.001

[40]

Metz, B. et al. Identification of formaldehyde-induced modifications in proteins: reactions with model peptides. J. Biol. Chem. 279, 6235–6243 (2004). 10.1074/jbc.m310752200

[41]

The BioPlex Network: A Systematic Exploration of the Human Interactome

Edward L. Huttlin, Lily Ting, Raphael J. Bruckner et al.

Cell 2015 10.1016/j.cell.2015.06.043

[42]

Kabil, O. & Banerjee, R. Enzymology of H2S biogenesis, decay and signaling. Antioxid. Redox Signal. 20, 770–782 (2014). 10.1089/ars.2013.5339

[43]

SAINT: probabilistic scoring of affinity purification–mass spectrometry data

Hyungwon Choi, Brett Larsen, Zhen-Yuan Lin et al.

Nature Methods 2011 10.1038/nmeth.1541

[44]

Sardiu, M.E. & Washburn, M.P. Construction of protein interaction networks based on the label-free quantitative proteomics. Methods Mol. Biol. 781, 71–85 (2011). 10.1007/978-1-61779-276-2_5

[45]

Kramer, K. et al. Photo-cross-linking and high-resolution mass spectrometry for assignment of RNA-binding sites in RNA-binding proteins. Nat. Methods 11, 1064–1070 (2014). 10.1038/nmeth.3092

[46]

Making proteomics data accessible and reusable: Current state of proteomics databases and repositories

Yasset Perez‐Riverol, Emanuele Alpi, Rui Wang et al.

PROTEOMICS 2015 10.1002/pmic.201400302

[47]

Tan, M. et al. Identification of 67 histone marks and histone lysine crotonylation as a new type of histone modification. Cell 146, 1016–1028 (2011). 10.1016/j.cell.2011.08.008

[48]

Yadav, M. et al. Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515, 572–576 (2014). 10.1038/nature14001

[49]

Mommen, G.P.M. et al. Expanding the detectable HLA peptide repertoire using electron-transfer/higher-energy collision dissociation (EThcD). Proc. Natl. Acad. Sci. USA 111, 4507–4512 (2014). 10.1073/pnas.1321458111

[50]

van den Broek, I. et al. Quantifying protein measurands by peptide measurements: where do errors arise? J. Proteome Res. 14, 928–942 (2015). 10.1021/pr5011179

Showing 50 of 58 references

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Published: Apr 10, 2017
Vol/Issue: 14(5)
Pages: 513-520
License: View

Authors

Dattatreya Mellacheruvu

A

Alexey I Nesvizhskii

University of Michigan

Cite This Article

Andy T Kong, Felipe V Leprevost, Dmitry M Avtonomov, et al. (2017). MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics. Nature Methods, 14(5), 513-520. https://doi.org/10.1038/nmeth.4256

MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics

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