Proteomics-based compositional analysis of complex cellulase-hemicellulase mixtures

Shishir P.S. Chundawat, Mary S. Lipton, Samuel O. Purvine, Nirmal Uppugundla, Dahai Gao, Venkatesh Balan, Bruce E. Dale

Research output: Contribution to journalArticlepeer-review

62 Scopus citations

Abstract

Efficient deconstruction of cellulosic biomass to fermentable sugars for fuel and chemical production is accomplished by a complex mixture of cellulases, hemicellulases, and accessory enzymes (e.g., >50 extracellular proteins). Cellulolytic enzyme mixtures, produced industrially mostly using fungi like Trichoderma reesei, are poorly characterized in terms of their protein composition and its correlation to hydrolytic activity on cellulosic biomass. The secretomes of commercial glycosyl hydrolase-producing microbes was explored using a proteomics approach with high-throughput quantification using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Here, we show that proteomics-based spectral counting approach is a reasonably accurate and rapid analytical technique that can be used to determine protein composition of complex glycosyl hydrolase mixtures that also correlates with the specific activity of individual enzymes present within the mixture. For example, a strong linear correlation was seen between Avicelase activity and total cellobiohydrolase content. Reliable, quantitative and cheaper analytical methods that provide insight into the cellulosic biomass degrading fungal and bacterial secretomes would lead to further improvements toward commercialization of plant biomass-derived fuels and chemicals.

Original languageEnglish (US)
Pages (from-to)4365-4372
Number of pages8
JournalJournal of Proteome Research
Volume10
Issue number10
DOIs
StatePublished - Oct 7 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Chemistry(all)

Keywords

  • LC-MS/MS
  • cellulase
  • cellulosic ethanol
  • enzymatic hydrolysis
  • glycosyl hydrolases
  • hemicellulase
  • lignocellulose
  • mass spectrometry
  • spectral counting

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