Abstract
Xylanase is an industrially important enzyme having wide range of applications especially in paper industry. It is crucial to gain an understanding about the structure and functional aspects of various xylanases produced from diverse sources. In this study, a bioinformatics and molecular modeling approach was adopted to explore properties and structure of xylanases. Physico-chemical properties were predicted and prediction of motifs, disulfide bridges and secondary structure was performed for functional characterization. Apart from these analyses, three dimensional structures were constructed and stereo-chemical quality was evaluated by different structure validation tools. Comparative catalytic site analysis and assessment was performed to extract information about the important residues. Asn72 was found to be the common residue in the active sites of the proteins P35809 and Q12603.
Highlights
Burgeoning rise in demand for industrial enzymes is anticipated to touch the mark of $5.1 billion soon
Xylanases are mainly exploited in the Kraft process for the removal of the lignincarbohydrate complexes [7, 8, 9]
Xylanases are classified under two classes: Family (F) and Family (G), based on hydrophobic cluster analysis and sequence homology [16, 17, 18]
Summary
Burgeoning rise in demand for industrial enzymes is anticipated to touch the mark of $5.1 billion soon. Xylanases are glycosidases (O-glycoside hydrolases, EC 3.2.1.x) which degrade the linear polysaccharide beta-1, 4-xylan into xylose and produced by a myriad group of microrganisms belonging to varied genera and species of bacteria, actinomycetes and fungi [2, 4, 5, 6] This group of enzymes has been attracting a lot of attention in the recent past due to its probable applicability in a spectrum of industrial processes [1]. Secondary structure prediction: SOPMA [25] was employed for calculating the secondary structural features of the selected target protein sequences considered for this study (Table 4 in supplementary material). SOSUI server [27] was used to predict the transmembrane tendency of the proteins considered for this study (Table 6 in supplementary material). Important residues involved in active sites were identified and compared for the modeled proteins (Table 10 in supplementary material)
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