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Simple, efficient and thorough shotgun proteomic analysis with PatternLab V

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

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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.

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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.

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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

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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
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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.

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The authors declare no competing interests.

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

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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

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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

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