Computational approach for identification, characterization, three-dimensional structure modelling and machine learning-based thermostability prediction of xylanases from the genome of Aspergillus fumigatus

Abstract

Identification of thermostable and alkaline xylanases from different fungal and bacterial species have gained an interest for the researchers because of its biotechnological relevance in many industries, such as pulp, paper, and bioethanol. In this study, we have identified and characterized xylanases from the genome of the thermophilic fungus of Aspergillus fumigatus by in silico analysis. Genome data mining revealed that the A fumigatus genome has six xylanase genes that belong to GH10, GH11, GH43 glycoside hydrolase families. In general, most of the bacterial and fungal GH11 xylanases are alkaline, and GH10 xylanases are acidic; however, we found that one identified xylanase from A fumigatus that belongs to the GH10 family is alkaline while the rest are acidic. Moreover, physicochemical properties also stated that most of the xylanases identified have lower molecular weight except one that belongs to the GH43 family. Structure prediction by homology modelling gave optimized structures of the xylanases. It suggests that GH10 family structure models adapt (β∕α) 8 barrel type, GH11 homology models adapt β-jelly type, and the GH43 family has a fivefold β-propeller type structure. Molecular docking of identified xylanases with xylan revealed that GH11 xylanases have strong interaction (-9.6 kcal/mol) with xylan than the GH10 (-8.5 and -9.3 kcal/mol) and GH43 (-8.8 kcal/mol). We used the machine learning approach based TAXyl server to predict the thermostability of the xylanases. It revealed that two GH10 xylanases and one GH11 xylanase are thermo-active up to 75ᵒC. We have explored the physiochemical properties responsible for maintaining thermostability for bacterial and fungal GH10 and GH11 xylanases by comparing crystal structures. All the analyzed parameters specified that GH10 xylanases from both the fungi and bacteria are more thermostable due to higher hydrogen bonds, salt bridges, and helical content.

Keywords: Aspergillus fumigatus; Bioinformatics; Genome And Machine-learning; Thermostable; Xylanase.