Display options
Cite
Lonsdale A, Davis MJ, Doblin MS, et al. Better Than Nothing? Limitations of the Prediction Tool SecretomeP in the Search for Leaderless Secretory Proteins (LSPs) in Plants. Front Plant Sci. 2016;7:1451doi: 10.3389/fpls.2016.01451.
Lonsdale, A., Davis, M. J., Doblin, M. S., & Bacic, A. (2016). Better Than Nothing? Limitations of the Prediction Tool SecretomeP in the Search for Leaderless Secretory Proteins (LSPs) in Plants. Frontiers in plant science, 71451. https://doi.org/10.3389/fpls.2016.01451
Lonsdale, Andrew, et al. "Better Than Nothing? Limitations of the Prediction Tool SecretomeP in the Search for Leaderless Secretory Proteins (LSPs) in Plants." Frontiers in plant science vol. 7 (2016): 1451. doi: https://doi.org/10.3389/fpls.2016.01451
Lonsdale A, Davis MJ, Doblin MS, Bacic A. Better Than Nothing? Limitations of the Prediction Tool SecretomeP in the Search for Leaderless Secretory Proteins (LSPs) in Plants. Front Plant Sci. 2016 Sep 27;7:1451. doi: 10.3389/fpls.2016.01451. eCollection 2016. PMID: 27729919; PMCID: PMC5037178.
Copy Download .nbib Format: NLM
Share it on

Front Plant Sci. 2016 Sep 27;7:1451. doi: 10.3389/fpls.2016.01451. eCollection 2016.

Better Than Nothing? Limitations of the Prediction Tool SecretomeP in the Search for Leaderless Secretory Proteins (LSPs) in Plants.

Frontiers in plant science

Andrew Lonsdale, Melissa J Davis, Monika S Doblin, Antony Bacic

Affiliations

  1. ARC Centre of Excellence in Plant Cell Walls, School of BioSciences, The University of Melbourne Parkville, VIC, Australia.
  2. The Walter and Eliza Hall Institute of Medical ResearchParkville, VIC, Australia; Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of MelbourneParkville, VIC, Australia.

PMID: 27729919 PMCID: PMC5037178 DOI: 10.3389/fpls.2016.01451

Abstract

In proteomic analyses of the plant secretome, the presence of putative leaderless secretory proteins (LSPs) is difficult to confirm due to the possibility of contamination from other sub-cellular compartments. In the absence of a plant-specific tool for predicting LSPs, the mammalian-trained SecretomeP has been applied to plant proteins in multiple studies to identify the most likely LSPs. This study investigates the effectiveness of using SecretomeP on plant proteins, identifies its limitations and provides a benchmark for its use. In the absence of experimentally verified LSPs we exploit the common-feature hypothesis behind SecretomeP and use known classically secreted proteins (CSPs) of plants as a proxy to evaluate its accuracy. We show that, contrary to the common-feature hypothesis, plant CSPs are a poor proxy for evaluating LSP detection due to variation in the SecretomeP prediction scores when the signal peptide (SP) is modified. Removing the SP region from CSPs and comparing the predictive performance against non-secretory proteins indicates that commonly used threshold scores of 0.5 and 0.6 result in false-positive rates in excess of 0.3 when applied to plants proteins. Setting the false-positive rate to 0.05, consistent with the original mammalian performance of SecretomeP, yields only a marginally higher true positive rate compared to false positives. Therefore the use of SecretomeP on plant proteins is not recommended. This study investigates the trade-offs of using SecretomeP on plant proteins and provides insights into predictive features for future development of plant-specific common-feature tools.

Keywords: SecretomeP; leaderless secretory protein; plant cell wall; protein localisation prediction; secretome; unconventional protein secretion

References

  1. Protein Pept Lett. 2012 Mar;19(3):270-6 - PubMed
  2. Trends Plant Sci. 2012 Oct;17(10):606-15 - PubMed
  3. In Silico Biol. 2008;8(2):129-40 - PubMed
  4. J Mol Biol. 2001 Jan 19;305(3):567-80 - PubMed
  5. Bioinformatics. 2014 Dec 1;30(23):3356-64 - PubMed
  6. Biochim Biophys Acta. 2011 Dec;1814(12):1964-73 - PubMed
  7. Protoplasma. 2017 Jan;254(1):75-94 - PubMed
  8. BMC Microbiol. 2005 Oct 07;5:58 - PubMed
  9. Proteomics. 2008 Feb;8(4):893-908 - PubMed
  10. Protein Eng Des Sel. 2004 Apr;17(4):349-56 - PubMed
  11. Biochem Biophys Res Commun. 2010 Jan 15;391(3):1306-11 - PubMed
  12. Plant Physiol. 2010 Jun;153(2):433-6 - PubMed
  13. Plant Methods. 2015 Jan 16;11(1):2 - PubMed
  14. Comput Biol Med. 2013 Sep;43(9):1177-81 - PubMed
  15. Peptides. 2010 Apr;31(4):574-8 - PubMed
  16. J Theor Biol. 2010 Nov 7;267(1):1-6 - PubMed
  17. Nat Methods. 2011 Sep 29;8(10):785-6 - PubMed
  18. Biochim Biophys Acta. 2013 Nov;1834(11):2429-41 - PubMed
  19. J Proteomics. 2016 Jan 1;130:56-64 - PubMed
  20. Front Plant Sci. 2013 May 01;4:111 - PubMed
  21. Protoplasma. 2016 Jan;253(1):31-43 - PubMed
  22. J Proteome Res. 2009 Jan;8(1):82-93 - PubMed
  23. Methods Enzymol. 1996;266:554-71 - PubMed
  24. Trends Biochem Sci. 1999 Jan;24(1):34-6 - PubMed
  25. J Proteome Res. 2014 Feb 7;13(2):447-59 - PubMed
  26. Proteomics. 2010 Feb;10(4):799-827 - PubMed
  27. Protein Eng Des Sel. 2004 Jan;17 (1):107-12 - PubMed
  28. J Proteomics. 2011 Jun 10;74(7):1045-67 - PubMed
  29. Curr Opin Cell Biol. 2014 Aug;29:107-15 - PubMed
  30. BMC Bioinformatics. 2011 Jan 14;12:21 - PubMed
  31. J Theor Biol. 2012 Nov 7;312:105-13 - PubMed
  32. Nucleic Acids Res. 2013 Jan;41(Database issue):D1185-91 - PubMed

Publication Types