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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 compar