Cellulases pp 103-113 | Cite as

Discovery of Novel Cellulases Using Proteomic Strategies

  • Marta Zoglowek
  • Heather Brewer
  • Angela Norbeck
Part of the Methods in Molecular Biology book series (MIMB, volume 1796)


In order to develop cost-effective processes for conversion of lignocellulosic biomass, discovery of novel enzymes for enhanced lignocellulose hydrolysis is one of the main scientific and industrial goals. This could be achieved by applying proteomic strategies for identification of proteins secreted by filamentous fungi that are among the most powerful producers of biomass-degrading enzymes. Here a strategy for a comparative study of proteins differentially secreted on media inducing production of biomass-degrading enzymes (e.g., lignocellulosic biomass) and media repressing secretion of those enzymes (e.g., glucose) are presented. The protocols presented include preparation of samples for mass spectrometry and identification of cellulolytic and other carbohydrate-degrading enzymes using bioinformatics.

Key words

Filamentous fungi Cellulases Sample preparation for mass spectrometry Proteomics Bioinformatics 


  1. 1.
    Punt PJ, van Biezen N, Conesa A et al (2002) Filamentous fungi as cell factories for heterologous protein production. Trends Biotechnol 20:200–206CrossRefGoogle Scholar
  2. 2.
    Bouws H, Wattenberg A, Zorn H (2008) Fungal secretomes—nature’s toolbox for white biotechnology. Appl Microbiol Biotechnol 80:381–388. Scholar
  3. 3.
    Samson A, Hoekstra ES, Frisvad JC (2004) Introduction to food-and airborne fungi. Centraalbureau voor Schimmelcultures (CBS), UtrechtGoogle Scholar
  4. 4.
    Pedersen M, Hollensted M, Lange L, Andersen B (2009) Screening for cellulose and hemicellulose degrading enzymes from the fungal genus Ulocladium. Int Biodeter Biodegrad 63:484–489CrossRefGoogle Scholar
  5. 5.
    Maiolica A, Borsotti D, Rappsilber J (2005) Self-made frits for nanoscale columns in proteomics. Proteomics 5:3847–3850CrossRefGoogle Scholar
  6. 6.
    Kelly RT, Page JS, Luo Q et al (2006) Chemically etched open tubular and monolithic emitters for nanoelectrospray ionization mass spectrometry. Anal Chem 78:7796–7801CrossRefGoogle Scholar
  7. 7.
    Horton P, Park K, Obayashi T et al (2007) WoLF PSORT: protein localization predictor. Nucleic Acids Res 35:W585–W587CrossRefGoogle Scholar
  8. 8.
    Petersen TN, Brunak S, von Heijne G et al (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8:785–786CrossRefGoogle Scholar
  9. 9.
    Bendtsen JD, Jensen LJ, Blom N et al (2004) Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng Des Sel 17:349–356CrossRefGoogle Scholar
  10. 10.
    Punta M, Coggill PC, Eberhardt RY et al (2012) The Pfam protein families database. Nucleic Acids Res 40(D1):D290–D301CrossRefGoogle Scholar
  11. 11.
    Quevillon E, Silventoinen V, Pillai S et al (2005) InterProScan: protein domains identifier. Nucleic Acids Res 33(suppl 2):W116–W120CrossRefGoogle Scholar
  12. 12.
    Benson DA, Cavanaugh M, Clark K et al (2013) GenBank. Nucleic Acids Res 41:D36–D42CrossRefGoogle Scholar
  13. 13.
    Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol 215:403–410CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Marta Zoglowek
    • 1
  • Heather Brewer
    • 2
  • Angela Norbeck
    • 2
  1. 1.Section of Sustainable Biotechnology, Department of Chemistry and BioscienceAalborg UniversityCopenhagenDenmark
  2. 2.Environmental Molecular Sciences Laboratory, Mass Spectrometry FacilityRichlandUSA

Personalised recommendations