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PET水解酶传统与智能分子设计研究进展

Research Advances on Traditional and Intelligent Molecular Design of PET Hydrolases
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摘要 塑料由于其耐久性和耐降解性造成的环境污染日趋严重,而塑料废弃物的处理回收方法存在着缺陷。聚对苯二甲酸乙二醇酯(polyethylene terephthalate,PET)是应用最广泛的塑料类型之一,但在自然条件下很难被降解。近年来,虽然多种具有PET降解活性的酶被发现,但这些酶的催化活性和热稳定性难以支撑实际工业所需,因此提高PET水解酶的降解能力已成为研究热点而备受关注。脂肪酶、角质酶、IsPETase和IsMHETase是目前研究最为广泛的PET水解酶,就这几种酶的结构、活性特征进行了总结,重点阐述了传统蛋白质工程和人工智能分子设计在增强PET水解酶应用性能方面的研究进展。期望塑料降解酶可以进一步发展优化,为循环塑料经济做出有价值的贡献。 Environmental pollution caused by plastic is becoming more and more serious due to its durability and degradation resistance,and there are defects in the treatment and recycling methods of plastic waste.Polyethylene terephthalate(PET)is one of the most widely used types of plastic,but it is difficult to degrade under natural conditions.In recent years,although a variety of enzymes with PET degradation activities have been discovered,the catalytic activity and thermal stability of these enzymes are difficult to support practical industrial needs.Therefore,improving the degradation ability of PET hydrolase has become a research hotspot and has received attention.Lipase,cutinase,IsPETase and IsMHETase are the most widely studied PET hydrolases.The structure and activity characteristics of these enzymes were summarized,with emphasis on the research progress of traditional protein engineering and artificial intelligence molecular design in enhancing the application performance of PET hydrolase.It was expected that plastic-degrading enzymes could be further developed and optimized to make valuable contributions to the circular plastics economy.
作者 苗瑞菊 丁尊丹 田健 张红兵 关菲菲 MIAO Ruiju;DING Zundan;TIAN Jian;ZHANG Hongbing;GUAN Feifei(College of Bioscience and Engineering,Hebei University of Economics and Business,Shijiazhuang 050061,China;Biotechnology Research Institute,Chinese Academy of Agricultural Sciences,Beijing 100081,China)
出处 《生物技术进展》 2023年第1期46-54,共9页 Current Biotechnology
基金 国家重点研发计划项目(2021YFC2100301)。
关键词 PET水解酶 蛋白质工程 智能分子设计 机器学习 PET hydrolase protein engineering intelligent molecular design machine learning
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