Asian Journal of Scientific Research

Volume 13 (1), 92-110, 2020


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In silico Structural Modelling and Comparative Analysis of β-mannanases from Psychrophilic and Mesophilic Arthrobacter sp.

Wan Nur Shuhaida Wan Mahadi, Clemente Michael Wong Vui Ling, Kenneth Francis Rodrigues and Cahyo Budiman

Background and Objective: Endo-1,4-β-mannanase (β-mannanase, EC 3.2.1.78) is an industrially important enzyme which catalyses the hydrolysis of mannan-based polysaccharides. This enzyme is produced by psychrophilic and mesophilic groups of Arthrobacter sp., yet none of them were structurally characterized. This study aims to decipher the structural features of Arthrobacter β-mannanases that might responsible for their cold adaptation. Material and Methods: Thirty amino acid sequences encoding β-mannanases from Arthrobacter sp. were retrieved from GenBank and subjected to series of analysis of amino acid profiling and structural homology modelling using Phyre2 and SWISS-MODEL. Results: Structural alignment showed the catalytic residues (2 glutamic acids) were conserved among β-mannanase suggesting that they might shares catalytic mechanism. Psychrophilic β-mannanases showed remarkable differences from the mesophilic ones in the content of hydrophilic, particularly negatively charged, residues and proline, which were thought to be important for its cold adaptation. Three-dimensional model of all Arthrobacter β-mannanases forms a classic α/β barrel motif consisting of 8 helices and 9 β-sheets structures, except for psychrophilic ones, which having 8 helices and 8 β-sheets. Conclusion: Adaptation of Arthrobacter β-mannanases towards cold temperature involves structural adjustment particularly on structural flexibility and amino acid distribution.

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How to cite this article:

Wan Nur Shuhaida Wan Mahadi, Clemente Michael Wong Vui Ling, Kenneth Francis Rodrigues and Cahyo Budiman, 2020. In silico Structural Modelling and Comparative Analysis of β-mannanases from Psychrophilic and Mesophilic Arthrobacter sp.. Asian Journal of Scientific Research, 13: 92-110.


DOI: 10.3923/ajsr.2020.92.110
URL: https://ansinet.com/abstract.php?doi=ajsr.2020.92.110

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