Bioinformatic Protocols and the Knowledge-Base for Secretomes in Fungi

Chapter
Part of the Fungal Biology book series (FUNGBIO)

Abstract

Fungal secreted proteins play important roles in cell signaling, metabolism, and regulation of fungal growth and development. The secretome refers to all secreted proteins in a proteome that are identified from completely sequenced genomes. The majority of secreted proteins are classical, signal peptide-dependent proteins that can be predicted using bioinformatics tools. In this chapter, we describe some commonly used tools for secreted protein prediction in fungi and propose a relatively accurate bioinformatic protocol for fungal secretome identification. The protocol combines multiple signal peptide or subcellular location predictors, including SignalP, WoLF PSORT, and Phobius, with TMHMM for removing transmembrane proteins and PROSITE PS-Scan for removing endoplasmic reticulum (ER) proteins. Applying this protocol, we have built the fungal secretome knowledge-base (FunSecKB). The utility of FunSecKB is described in detail. FunSecKB serves the community as a central portal for search and deposition of fungal secretome information.

Keywords

Secreted proteins Secretome Signal peptide Fungi Prediction Knowledge-base Database 

References

  1. 1.
    Tjalsma H, Bolhuis A, Jongbloed JD, Bron S, van Dijl JM (2000) Signal peptide-dependent protein transport in Bacillus subtilis: a genome-based survey of the secretome. Microbiol Mol Biol Rev 64:515–547PubMedCrossRefGoogle Scholar
  2. 2.
    Greenbaum D, Luscombe NM, Jansen R, Gerstein M (2001) Interrelating different types of genomic data, from proteome to secretome: ‘oming in on function. Genome Res 11:1463–1468PubMedCrossRefGoogle Scholar
  3. 3.
    Hathout Y (2007) Approaches to the study of the cell secretome. Expert Rev Proteomics 4:239–248PubMedCrossRefGoogle Scholar
  4. 4.
    Simpson JC, Mateos A, Pepperkok R (2007) Maturation of the mammalian secretome. Genome Biol 8:211PubMedCrossRefGoogle Scholar
  5. 5.
    Lum, G. and Min, X. J. (2011) FunSecKB: the Fungal Secretome KnowledgeBase. Database 2011: doi:10.1093/database/bar001.
  6. 6.
    O’Toole N, Min XJ, Storms R, Butler G, Tsang A (2006) Sequence-based analysis of fungal secretomes. Appl Mycol Biotechnol 6:277–296CrossRefGoogle Scholar
  7. 7.
    Min XJ (2010) Evaluation of computational methods for secreted protein prediction in different eukaryotes. J Proteomics Bioinformatics 3:143–147Google Scholar
  8. 8.
    Choi J, Park J, Kim D et al (2010) Fungal secretome database: integrated platform for annotation of fungal secretomes. BMC Genomics 11:105PubMedCrossRefGoogle Scholar
  9. 9.
    Bendtsen JD, von Nielsen H, Heijne G, Brunak S (2004) Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 340:783–795PubMedCrossRefGoogle Scholar
  10. 10.
    Emanuelsson O, Nielsen H, Brunak S, von Heijne G (2000) Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol 30:1005–1016CrossRefGoogle Scholar
  11. 11.
    Krogh A, Larsson B, von Heijne G, Sonnhammer ELL (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305:567–580PubMedCrossRefGoogle Scholar
  12. 12.
    Käll L, Krogh A, Sonnhammer ELL (2004) A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338:1027–1036PubMedCrossRefGoogle Scholar
  13. 13.
    Horton P, Park KJ, Obayashi T, Fujita N, Harada H, Adams-Collier CJ et al (2007) WoLF PSORT: protein localization predictor. Nucleic Acids Res 35:W585–W587 (Web Server issue)PubMedCrossRefGoogle Scholar
  14. 14.
    Sigrist CJA, Cerutti L, de Casro E, Langendijk-Genevaux PS, Bulliard V, Bairoch A et al (2010) PROSITE, a protein domain database for functional characterization and annotation. Nucleic Acids Res 38:161–166CrossRefGoogle Scholar
  15. 15.
    Poisson G, Chauve C, Chen X, Bergeron A (2007) FragAnchor a large scale all Eukaryota predictor of glycosylphosphatidylinositol-anchor in protein sequences by qualitative scoring. Genomics Proteomics Bioinformatics 5:121–130PubMedCrossRefGoogle Scholar
  16. 16.
    Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 405:442–451PubMedCrossRefGoogle Scholar
  17. 17.
    Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H (2000) Assessing the accuracy of prediction ­algorithms for classification: an overview. Bioinformatics 16:412–424PubMedCrossRefGoogle Scholar
  18. 18.
    Menne KM, Hermjakob H, Apweiler R (2000) A comparison of signal sequence prediction methods using a test set of signal peptides. Bioinformatics 16:741–742PubMedCrossRefGoogle Scholar
  19. 19.
    Tsang A, Butler G, Powlowski J et al (2009) Analytical and computational approaches to define the Aspergillus niger secretome. Fungal Genet Biol 46:S153–S160PubMedCrossRefGoogle Scholar
  20. 20.
    Oda K, Kakizono D, Yamada O, Iefuji H, Akita O, Iwashita K (2006) Proteomic analysis of extracellular proteins from Aspergillus oryzae grown under submerged and solid-state culture conditions. Appl Environ Microbiol 72:3448–3457PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  1. 1.Department of Computer Science and Information SystemsYoungstown State UniversityYoungstownUSA
  2. 2.Department of Biological Sciences, Center for Applied Chemical BiologyYoungstown State University, One University PlazaYoungstownUSA

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