Development of a high through-put method for the functional annotation of enzymes involved in the degradation of biomass.

Hemicellulose is a complex heteropolysaccharide with branched structure and consisting of different monosacchride building blocks. For its hydrolysis an array of enzymes (hemicellulasen) with different specificity is needed. In the discovery, initial screening and characterization of enzymes active on lignocellulose, a limited amount of substrates is used (chromogenic substrates, commercially available oligosaccharides or xylan model substrates). These give little information on the substrate specificity of the enzymes. Recently, a different class of enzymes is discovered that boost lignocellulose degradation. These enzymes were originally classified in glycoside hydrolase family 61 (GH61), but use a O2- and metal-dependent oxidative mechanism using unknown molecules in the lignocellulose matrix. One of the bottlenecks in this research is the detection and quantification of the oligosaccharide products formed.

 

In this project, a fast and sensitive assay for the high-throughput screening of biomass active enzymes is presented that is based on a library of biomass-derived oligosaccharides. The assay will be optimized and validated with hemicellulases produced by the model organisms Aspergillus niger and Trichoderma reesei (Hypocrea jecorina).

We will build further on the assembly of an oligosaccharide library derived from lignocellulosic biomass. The sugars are analysed in parallel with DSA-FACE en HPAEC-PAD and yield a 2D-plot where the spots are clearly separated (Multiplexed Analytical Glycomics Technologie). This database will contribute to the identification of unknown oligosacchride products. It is also demonstrated that the developed 2D technologie is of added-value in the detection/qauntification of oxidized cellodextins in the degradation of lignocellulose by GH family 61 enzymes. A case study with arabinofuranosidases will show that the DSA-FACE technology is perfetly suited for enzyme kinetics on real substrates. Also we will show that the methodology –due to its high resolution- is suited to determine the substrate specificity of arabinofuranosidases.

All expertise will at the end result in a technology allowing a high through-put functional annotation of hemicellulases and other related enzymes.

 

Contact: Maria Fonseca

Funding: Ghent University
Supervisors: Dr. ing. Ingeborg Stals, Prof. Dr. A Van Landschoot

Co-supervisors: Prof. Dr. T. Desmet

Period: 2012 – 2016