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Evaluating the estrogen degradation potential of laccase and peroxidase from Bacillus ligniniphilus L1 through
Muhammad Zohaib Nawaz1, Syed Zeeshan Haider1, Liang Zhe1
1International Joint Laboratory on Synthetic Biology and Biomass Biorefinery, Biofuels Institute, School of Emergency Management, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China; Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment, Suzhou University of Science and Technology, Suzhou 215009, China.
Abstract:
This study investigated the degradation potential of laccase and catalase-peroxidase from the extremophilic marine bacterium Bacillus ligniniphilus L1 against endogenous and synthetic estrogen compounds using an integrated computational and experimental approach. Molecular docking identified five estrogen compounds exhibiting reliable bindings with enzymes, which were then subjected to enzyme activity assays. The degradation potential of the two enzymes against five selected estrogen compounds were investigated and compared with their commercial counterparts. Laccase from L1 showed higher degradation potential against estrone (47.02 % without and 62.21 % with ABTS) compared to commercial laccase (39 % without and 54.20 % with ABTS). For estradiol valerate, commercial laccase showed a slightly higher degradation (52.47 %) than L1 laccase (49.94 %), but with ABTS, L1 laccase performed better (74.15 % vs. 68.03 %). Notably, L1 catalase-peroxidase demonstrated significantly higher degradation for all tested compounds compared to its commercial counterpart with efficiencies of 96.16 %, 89.09 %, 74.94 %, 64.91 %, and 62.80 % against estropipate, quinestrol, estradiol valerate, estriol and estrone, respectively, revealing its potential for commercial applications. Molecular dynamics simulations revealed the interaction and stability of enzyme-estrogen complexes, with MMGBSA binding energy calculations supporting experimental results. These findings highlight the usefulness of the computational approach in elucidating the molecular mechanisms underlying enzyme-mediated bioremediation of environmental contaminants.
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