Computational Redesign of Thermo-Resistant MHETase for Complete Polyethylene Terephthalate Degradation by Dual-Enzyme System

热稳定性 材料科学 合理设计 对苯二甲酸 降级(电信) 分子动力学 催化效率 聚酯纤维 产量(工程) 催化作用 水解 羧酸酯酶 瓶颈 聚对苯二甲酸乙二醇酯 化学 定向进化 蛋白质工程 可扩展性 生化工程 过程(计算) 突变 组合化学
作者
Yunxin Zheng,Jiaxing Zhang,Tao Gu,Mengfan Wang,Shengping You,Rongxin Su,Wei Qi
出处
期刊:ACS Synthetic Biology [American Chemical Society]
卷期号:15 (1): 284-296 被引量:1
标识
DOI:10.1021/acssynbio.5c00745
摘要

Polyethylene terephthalate (PET) causes significant environmental challenges due to its difficulty in degradation. While enzymatic recycling by dual-enzyme systems with PETase/MHETase can degrade PET into terephthalic acid (TPA), the limited thermostability and catalytic efficiency of MHETases mismatch with the thermophilic PETases, which becomes the bottleneck of industrial scalability of dual-enzyme systems. Here, inspired by a previous report using a protein scaffold, a thermophilic carboxyesterase (Est30) fromGeobacillus sterarothermophillus(Commun Biol 2023, 6, 1135), we built the dual-enzyme system with engineered carboxylesterase EstD9 and PETase to enhance the efficiency for PET degradation. We improved EstD9's activity and thermostability by computational redesign, aiming to match the optimal reaction conditions of PETases. By prioritizing low-risk mutagenesis for combinations, we effectively mitigated epistatic effects and successfully constructed a high-performance mutant (11M-MHETase), exhibiting a 95-fold enhancement in catalytic efficiency (kcat/Km) toward MHET hydrolysis and a 16.4 °C improvement in the melting temperature. The enhanced dual-enzyme systems composed of PETases and 11M-MHETase contribute significantly to the complete degradation and industrial-scale recycling of PET. The LCC-YGA-11M-MHETase system showed a 158.3% increase of TPA yield compared with the LCC-YGA-only system. Furthermore, the molecular mechanism of improved catalytic performance was analyzed by molecular dynamics simulations and first-principles calculations. Totally, the strategy proposed in this study accelerates the improvement of enzyme performance through low-risk combinatorial design guided by a dynamic interaction matrix, thereby establishing an efficient method for the thermostability engineering of industrial enzymes.
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