Lignocellulosic biomass is considered an attractive and immediate source for liquid biofuel. Lignocellulose hydrolysis requires the synergistic actions of several enzymes termed glycoside hydrolases (GHs). In the framework of this research, metagenomics and biochemical approaches are combined to isolate and characterize novel enzymatic systems geared for lignocellulose degradation.
Our view on the GHs world is restricted to the current known sequences available in databases. Thus, when analyzing metagenomes with conventional methods novel GHs genes with low sequence similarities are usually slip under the radar.
Our sequence based screening design to identify novel GHs genes in metadata. We designed our search algorithm under three principles:
A. GHs designated algorithm rather than a full data analysis.
B. Genomic Neighborhood approach assimilation for sieving excess data.
C. Conserved Neighborhood approach for putative novel GH genes evaluation.
Our search algorithm had managed to reduce the analyzed data and allowed us to focus on several clusters which potentially harbor novel GHs. Following the scan of 75 thermal-springs metagenomes, 97 DNA scaffolds with 778 hemicellulolytic genes were identified. Two clusters with putative cellulose degradation pathways were chosen for further evaluation and 3 putative novel GHs were taken for biochemical analysis. One gene encodes for an enzyme with activity on pNP-α-L-arabinopyranose. To date there are no known GH family with such activity. Furthermore, the enzyme has no characterized homologous. Thus, it appears that we have identified a novel GH family. The gene product is now in the process of complete biochemical characterization.
