[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol Update
  • Published:

Simple, efficient and thorough shotgun proteomic analysis with PatternLab V

Nature Protocols volume 17, pages 1553–1578 (2022)Cite this article

Subjects

Abstract

Shotgun proteomics aims to identify and quantify the thousands of proteins in complex mixtures such as cell and tissue lysates and biological fluids. This approach uses liquid chromatography coupled with tandem mass spectrometry and typically generates hundreds of thousands of mass spectra that require specialized computational environments for data analysis. PatternLab for proteomics is a unified computational environment for analyzing shotgun proteomic data. PatternLab V (PLV) is the most comprehensive and crucial update so far, the result of intensive interaction with the proteomics community over several years. All PLV modules have been optimized and its graphical user interface has been completely updated for improved user experience. Major improvements were made to all aspects of the software, ranging from boosting the number of protein identifications to faster extraction of ion chromatograms. PLV provides modules for preparing sequence databases, protein identification, statistical filtering and in-depth result browsing for both labeled and label-free quantitation. The PepExplorer module can even pinpoint de novo sequenced peptides not already present in the database. PLV is of broad applicability and therefore suitable for challenging experimental setups, such as time-course experiments and data handling from unsequenced organisms. PLV interfaces with widely adopted software and community initiatives, e.g., Comet, Skyline, PEAKS and PRIDE. It is freely available at http://www.patternlabforproteomics.org.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the PLV workflow.
Fig. 2: PSM.
Fig. 3: PTM library.
Fig. 4: Filtered results.
Fig. 5: SEPro’s result browser.
Fig. 6: Double-clicking on a protein result.
Fig. 7: Annotated mass spectrum.
Fig. 8: Project Organization.
Fig. 9: Isobaric Analyzer.
Fig. 10: XIC Browser.
Fig. 11: Center panel (Buzios).
Fig. 12: MS Browser.
Fig. 13: XIC Browser’s Mixture Analysis module.

Similar content being viewed by others

Data availability

All data associated with this protocol are provided within the paper or the supporting primary research papers, e.g., refs. 34,58.

Code availability

The software used in this protocol can be found at http://patternlabforproteomics.org

References

  1. Washburn, M. P., Wolters, D. & Yates, J. R. III Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 19, 242–247 (2001).

    Article  CAS  PubMed  Google Scholar 

  2. 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. 5, 976–989 (1994).

    Article  CAS  PubMed  Google Scholar 

  3. Zhang, B., Chambers, M. C. & Tabb, D. L. Proteomic parsimony through bipartite graph analysis improves accuracy and transparency. J. Proteome Res. 6, 3549–3557 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Elias, J. E. & Gygi, S. P. Target–decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).

    Article  CAS  PubMed  Google Scholar 

  5. Yates, J. R. III et al. Toward objective evaluation of proteomic algorithms. Nat. Methods 9, 455–456 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Barboza, R. et al. Can the false-discovery rate be misleading? Proteomics 11, 4105–4108 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Carvalho, P. C. et al. Search engine processor: filtering and organizing peptide spectrum matches. Proteomics 12, 944–949 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Moosa, J. M., Guan, S., Moran, M. F. & Ma, B. Repeat-preserving decoy database for false discovery rate estimation in peptide identification. J. Proteome Res. 19, 1029–1036 (2020).

    Article  CAS  PubMed  Google Scholar 

  9. Ma, B. et al. PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Commun. Mass Spectrom. 17, 2337–2342 (2003).

    Article  CAS  PubMed  Google Scholar 

  10. Perkins, D. N., Pappin, D. J., Creasy, D. M. & Cottrell, J. S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999).

    Article  CAS  PubMed  Google Scholar 

  11. Keller, A., Eng, J., Zhang, N., Li, X. & Aebersold, R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol. Syst. Biol. 1, 0017 (2005).

    Article  PubMed  CAS  Google Scholar 

  12. Kohlbacher, O. et al. TOPP—the OpenMS proteomics pipeline. Bioinformatics 23, e191–e197 (2007).

    Article  CAS  PubMed  Google Scholar 

  13. McDonald, W. H. et al. MS1, MS2, and SQT-three unified, compact, and easily parsed file formats for the storage of shotgun proteomic spectra and identifications. Rapid Commun. Mass Spectrom. 18, 2162–2168 (2004).

    Article  CAS  PubMed  Google Scholar 

  14. Xu, T. et al. ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity. J. Proteom. 129, 16–24 (2015).

    Article  CAS  Google Scholar 

  15. Carvalho, P. C., Fischer, J. S. G., Chen, E. I., Yates, J. R. & Barbosa, V. C. PatternLab for proteomics: a tool for differential shotgun proteomics. BMC Bioinform. 9, 316 (2008).

    Article  CAS  Google Scholar 

  16. Carvalho, P. C., Hewel, J., Barbosa, V. C. & Yates, J. R. III Identifying differences in protein expression levels by spectral counting and feature selection. Genet. Mol. Res. 7, 342–356 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Liu, H., Sadygov, R. G. & Yates, J. R. III A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 76, 4193–4201 (2004).

    Article  CAS  PubMed  Google Scholar 

  18. Carvalho, P. C., Yates Iii, J. R. & Barbosa, V. C. Analyzing shotgun proteomic data with PatternLab for proteomics. Curr. Protoc. Bioinform. Chapter 13, Unit 13.13.1–15 (2010).

  19. Zhang, S.-R. et al. The Null-Test for peptide identification algorithm in Shotgun proteomics. J. Proteom. 163, 118–125 (2017).

    Article  CAS  Google Scholar 

  20. Carvalho, P. C., Fischer, J. S. G., Xu, T., Yates, J. R., III & Barbosa, V. C. PatternLab: from mass spectra to label-free differential shotgun proteomics. Curr. Protoc. Bioinform. Chapter 13, Unit13.19 (2012).

  21. Carvalho, P. C., Yates, J. R. III & Barbosa, V. C. Improving the TFold test for differential shotgun proteomics. Bioinformatics 28, 1652–1654 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Carvalho, P. C. et al. Analyzing marginal cases in differential shotgun proteomics. Bioinformatics 27, 275–276 (2011).

    Article  CAS  PubMed  Google Scholar 

  23. de Saldanha da Gama Fischer, J. et al. Chemo-resistant protein expression pattern of glioblastoma cells (A172) to perillyl alcohol. J. Proteome Res. 10, 153–160 (2011).

    Article  PubMed  CAS  Google Scholar 

  24. Leprevost, F. V. et al. PepExplorer: a similarity-driven tool for analyzing de novo sequencing results. Mol. Cell Proteom. https://doi.org/10.1074/mcp.M113.037002 (2014).

    Article  Google Scholar 

  25. Fischer, J. et al. A scoring model for phosphopeptide site localization and its impact on the question of whether to use MSA. J. Proteom. https://doi.org/10.1016/j.jprot.2015.01.008 (2015).

    Article  Google Scholar 

  26. Eng, J. K. et al. A deeper look into Comet–implementation and features. J. Am. Soc. Mass Spectrom. 26, 1865–1874 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Carvalho, P. C. et al. Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0. Nat. Protoc. 11, 102–117 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Santos, M. D. M. et al. Mixed-data acquisition: next-generation quantitative proteomics data acquisition. J. Proteom. 222, 103803 (2020).

    Article  CAS  Google Scholar 

  29. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Gatchalian, J. et al. A non-canonical BRD9-containing BAF chromatin remodeling complex regulates naive pluripotency in mouse embryonic stem cells. Nat. Commun. 9, 5139 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Prieto, D. et al. S100-A9 protein in exosomes from chronic lymphocytic leukemia cells promotes NF-κB activity during disease progression. Blood 130, 777–788 (2017).

    Article  CAS  PubMed  Google Scholar 

  32. Sogues, A. et al. Essential dynamic interdependence of FtsZ and SepF for Z-ring and septum formation in Corynebacterium glutamicum. Nat. Commun. 11, 1641 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Horstmann, J. A. et al. Methylation of Salmonella typhimurium flagella promotes bacterial adhesion and host cell invasion. Nat. Commun. 11, 2013 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Camillo-Andrade, A. C. et al. Proteomics reveals that quinoa bioester promotes replenishing effects in epidermal tissue. Sci. Rep. 10, 19392 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Richards, A. L. et al. One-hour proteome analysis in yeast. Nat. Protoc. 10, 701–714 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. UniProt Consortium. Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acids Res. 41, D43–D47 (2013).

    Article  CAS  Google Scholar 

  37. Zahn-Zabal, M. et al. The neXtProt knowledgebase in 2020: data, tools and usability improvements. Nucleic Acids Res. 48, D328–D334 (2020).

    CAS  PubMed  Google Scholar 

  38. Li, H. et al. Evaluating the effect of database inflation in proteogenomic search on sensitive and reliable peptide identification. BMC Genomics 17, 1031 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Ma, B. Novor: real-time peptide de novo sequencing software. J. Am. Soc. Mass Spectrom. 26, 1885–1894 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Thompson, A. et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895–1904 (2003).

    Article  CAS  PubMed  Google Scholar 

  41. Ong, S.-E. et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell Proteom. 1, 376–386 (2002).

    Article  CAS  Google Scholar 

  42. Santos, M. D. M. et al. A quantitation module for isotope-labeled peptides integrated into PatternLab for proteomics. J. Proteom. 202, 103371 (2019).

    Article  CAS  Google Scholar 

  43. Vizcaíno, J. A. et al. The mzIdentML data standard version 1.2, supporting advances in proteome informatics. Mol. Cell Proteom. 16, 1275–1285 (2017).

    Article  Google Scholar 

  44. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    Article  CAS  PubMed  Google Scholar 

  45. Martens, L. et al. mzML—a community standard for mass spectrometry data. Mol. Cell Proteom. 10, R110.000133–R110.000133 (2011).

    Article  Google Scholar 

  46. Eng, J. K., Searle, B. C., Clauser, K. R. & Tabb, D. L. A face in the crowd: recognizing peptides through database search. Mol. Cell Proteom. 10, R111.009522 (2011).

    Article  CAS  Google Scholar 

  47. Eng, J. K. & Deutsch, E. W. Extending Comet for global amino acid variant and post‐translational modification analysis using the PSI extended FASTA format. Proteomics 20, 1900362 (2020).

    Article  CAS  Google Scholar 

  48. Wippel, H. H. et al. Comparing intestinal versus diffuse gastric cancer using a PEFF-oriented proteomic pipeline. J. Proteom. https://doi.org/10.1016/j.jprot.2017.10.005 (2017).

    Article  Google Scholar 

  49. Punta, M. et al. The Pfam protein families database. Nucleic Acids Res. 40, D290–D301 (2012).

    Article  CAS  PubMed  Google Scholar 

  50. Pandurangan, A. P., Stahlhacke, J., Oates, M. E., Smithers, B. & Gough, J. The SUPERFAMILY 2.0 database: a significant proteome update and a new webserver. Nucleic Acids Res. 47, D490–D494 (2019).

    Article  CAS  PubMed  Google Scholar 

  51. Zybailov, B. et al. Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. J. Proteome Res. 5, 2339–2347 (2006).

    Article  CAS  PubMed  Google Scholar 

  52. Brunoro, G. V. F. et al. Reevaluating the Trypanosoma cruzi proteomic map: the shotgun description of bloodstream trypomastigotes. J. Proteom. 115, 58–65 (2015).

    Article  CAS  Google Scholar 

  53. Benjamini, Yoav & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).

    Google Scholar 

  54. Kurt, L. U. et al. RawVegetable—a data assessment tool for proteomics and cross-linking mass spectrometry experiments. J. Proteom. 225, 103864 (2020).

    Article  CAS  Google Scholar 

  55. Bonilauri, B. et al. Proteogenomic analysis reveals proteins involved in the first step of adipogenesis in human adipose-derived stem cells. Stem Cells Int. 2021, 1–14 (2021).

    Article  CAS  Google Scholar 

  56. Leprevost, F. et al. On best practices in the development of bioinformatics software. Front. Genet. 5, 199 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Shalit, T., Elinger, D., Savidor, A., Gabashvili, A. & Levin, Y. MS1-based label-free proteomics using a quadrupole orbitrap mass spectrometer. J. Proteome Res. 14, 1979–1986 (2015).

    Article  CAS  PubMed  Google Scholar 

  58. Keshishian, H. et al. Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry. Nat. Protoc. 12, 1683–1701 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank W. Nagib, from Fiocruz, for creating the new PatternLab logo and entrance screen and J. Eng, from the University of Washington, for all the support and adaptations in the Comet search engine. We thank Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), Fiocruz, and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for financial support. R.H.V. (grant 304523/2019-4), V.C.B. (grant 300987/2019-6) and P.C.C. (grant 308930/2020-7) are CNPq research fellows. J.R.Y. acknowledges NIH P41 GM103533.

Author information

Authors and Affiliations

  1. Laboratory for Structural and Computational Proteomics, Carlos Chagas Institute, Fiocruz Paraná, Curitiba, Brazil

    Marlon D. M. Santos, Juliana S. G. Fischer, Milan A. Clasen, Louise U. Kurt, Amanda Caroline Camillo-Andrade, Leandro C. Monteiro, Tatiana A. C. B. Souza, Renata M. Santos & Paulo C. Carvalho

  2. Department of Structural Biology, Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), Berlin, Germany

    Diogo B. Lima

  3. Leonidas and Maria Deane Institute, Fiocruz Amazonas, Manaus, Brazil

    Priscila F. de Aquino

  4. Laboratory of Toxinology, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, Brazil

    Ana G. C. Neves-Ferreira, Richard H. Valente & Monique R. O. Trugilho

  5. Center for Technological Development in Health, Fiocruz, Rio de Janeiro, Brazil

    Monique R. O. Trugilho

  6. Research and Development in Biotechnology, Blau Farmacêutica S/A, Cotia, Brazil

    Giselle V. F. Brunoro

  7. Chemistry Institute, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Renata M. Santos

  8. Mass spectrometry facility, Carlos Chagas Institute, Fiocruz Paraná, Curitiba, Brazil

    Michel Batista

  9. Dalton Mass Spectrometry Laboratory, University of Campinas, São Paulo, Brazil

    Fabio C. Gozzo

  10. Analytical Biochemistry and Proteomics Unit, Instituto de Investigaciones Biológicas Clemente Estable/Institut Pasteur, Montevideo, Uruguay

    Rosario Durán

  11. Laboratory for Biological Mass Spectrometry, The Scripps Research Institute, La Jolla, CA, USA

    John R. Yates III

  12. Systems Engineering and Computer Science Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Valmir C. Barbosa

Authors
  1. Marlon D. M. Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diogo B. Lima
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juliana S. G. Fischer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Milan A. Clasen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Louise U. Kurt
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amanda Caroline Camillo-Andrade
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Leandro C. Monteiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Priscila F. de Aquino
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ana G. C. Neves-Ferreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Richard H. Valente
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Monique R. O. Trugilho
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Giselle V. F. Brunoro
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Tatiana A. C. B. Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Renata M. Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Michel Batista
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Fabio C. Gozzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Rosario Durán
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. John R. Yates III
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Valmir C. Barbosa
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Paulo C. Carvalho
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.C.C., J.R.Y. and V.C.B. have participated since the initial version of PatternLab, published in 2008. M.D.M.S., D.B.L., M.A.C., L.U.K., L.C.M. and P.C.C. served as developers, implementing the many features that enabled the transition from PL4 to PLV. J.S.G.F., P.F.d.A., A.G.C.N.F., R.H.V., M.O.T., G.V.F.B., T.A.C.B.S., R.M.S., A.C.C.-A., M.B., F.C.G. and R.D. are all experts in proteomics and worked closely with the computational team in developing new features, improving user experience and performing in-depth testing.

Corresponding authors

Correspondence to Valmir C. Barbosa or Paulo C. Carvalho.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Protocols thanks Annalisa Santucci, Yafeng Zhu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Gatchalian, J. et al. Nat. Commun. 9, 5139 (2018): https://doi.org/10.1038/s41467-018-07528-9

Prieto, D. et al. Blood 130, 777–788 (2017): https://doi.org/10.1182/blood-2017-02-769851

Sogues, A. et al. Nat. Commun. 11, 1641 (2020): https://doi.org/10.1038/s41467-020-15490-8

Horstmann, J. A. et al. Nat. Commun. 11, 2013 (2020): https://doi.org/10.1038/s41467-020-15738-3

Key data used in this protocol

Camillo-Andrade, A. C. et al. Sci. Rep. 10, 19392 (2020): https://doi.org/10.1038/s41598-020-76325-6

Shalit, T. et al. Proteome Res. 14, 1979–1986 (2015): https://doi.org/10.1021/pr501045t

This protocol is an update to Nat. Protoc. 11, 102–117 (2015): https://doi.org/10.1038/nprot.2015.133

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Santos, M.D.M., Lima, D.B., Fischer, J.S.G. et al. Simple, efficient and thorough shotgun proteomic analysis with PatternLab V. Nat Protoc 17, 1553–1578 (2022). https://doi.org/10.1038/s41596-022-00690-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-022-00690-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research