Azoridactase Enzyme Engineering to Induce Structural Changes in the Active Site and Improve Its Affinity for Azo Dyes
Authors
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Alireza Zakeri
- Dept. of Biological Sciences, School of Materials Engineering & Interdisciplinary Science, Shahid Rajaee Teacher Training University, Tehran, Iran.
https://orcid.org/0000-0002-5718-6999
- Maryam Yaghobi - Dept. of Biological Sciences, School of Materials Engineering & Interdisciplinary Science, Shahid Rajaee Teacher Training University, Tehran, Iran.
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Saeed Khalili
- Dept. of Biological Sciences, School of Materials Engineering & Interdisciplinary Science, Shahid Rajaee Teacher Training University, Tehran, Iran.
https://orcid.org/0000-0003-0493-9595
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Zahra Sadat Hashemi
- ATMP Dept., Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran.
https://orcid.org/0000-0001-6353-987X
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Navid Pourzardost
- Dep. of Biochemistry, Guilan University of Medical Sciences, Rasht, Iran.
https://orcid.org/0000-0001-7855-0967
DOI:
https://doi.org/10.22100/jkh.v18i4.2790Abstract
Introduction: Large quantities of dyes, including refractory azo dyes, are discharged directly into wastewater from the textile and petroleum industries and can be removed by bioremediation. In this study, bioinformatics tools were used to modify the structure of the enzyme azuridactase to improve its performance in degrading these dyes, including methyl red.
Methods: The amino acid sequence of the azoridactase enzyme was obtained from the UniProt database. The three-dimensional structure of the enzyme was predicted using modeling tools, and the best model was determined using Qmean web software. Due to the close proximity of the active site of this enzyme to that of Bacillus Smithii, the substrate (methyl red) was docked to a three-dimensional model of the active site using the PyRx program. Potential mutations at the active site were identified through sequence alignment. The exerted mutations were examined regarding the changes in binding energy and the interaction network.
Results: The structure generated by Robetta was chosen as the best model for the Q9X4K2 sequence. The mutagenesis results, in terms of binding energy and interaction plot, indicated that the optimal mutation involves changing proline 132 to serine. This mutation reduces the binding energy between methyl red and azoridactase from -6.9 kcal/mol to -7.4 kcal/mol. Furthermore, an examination of the interaction network in the mutant protein revealed the formation of a new hydrogen bond.
Conclusion: The reduced binding energy between the enzyme and methyl red suggests that the enzyme is more favorably positioned towards the substrate, thereby enhancing the enzyme's efficacy in degrading azo dyes.
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