离子微环境稳定二硫键工程赖氨酸脱羧酶以高效生产尸胺

IF 9.1 Q1 ENGINEERING, CHEMICAL Green Chemical Engineering Pub Date : 2023-06-01 DOI:10.1016/j.gce.2021.11.010
Zhuang Li , Yaju Xue , Xiuling Ji , Yuhong Huang
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引用次数: 2

摘要

Cadaverine是合成尼龙5X的关键单体。当反应pH从6.3增加到8.5时,高效和碱性稳定的赖氨酸脱羧酶对于尸胺的生产是非常需要的。然而,研究最多的赖氨酸脱羧酶CadA(大肠杆菌)在pH 8.0时几乎失去了所有活性,这是工业化尸胺生产的首要挑战。在这项研究中,我们首先发现Na+-微环境显著改善了二硫化物工程赖氨酸脱羧酶ΔLdcEt3(P233C/L628C)的碱性稳定性(半衰期362 h) ,与传统缓冲液相比(半衰期0.66 h) pH 8.0。同时,工业级l-赖氨酸与ΔLdcEt3的全细胞转化率在2 h。实验研究和分子动力学证实,Na+-微环境可以改善ΔLdcEt3的活性聚集状态并影响其二级结构。因此,Na+-微环境稳定了ΔLdcEt3,为高水平的尸胺生产提供了巨大的工业应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Ionic-microenvironment stabilizes the disulfide engineered lysine decarboxylase for efficient cadaverine production

Cadaverine is the key monomer for the synthesis of nylon 5X. Efficient and alkaline stable lysine decarboxylases are highly desirable for cadaverine production as the reaction pH increasing from 6.3 to 8.5. However, the most studied lysine decarboxylase CadA (E. coli) lost almost all activity at pH 8.0, which is the foremost challenge for the industrial-cadaverine production. In this study, we first found that the Na+-microenvironment significantly improved the alkaline stability of the disulfide engineered lysine decarboxylase ΔLdcEt3 (P233C/L628C) (half-life 362 h), compared to the conventional buffer (half-life 0.66 h) at pH 8.0. Meanwhile, the whole-cell conversion efficiency of the industrial-grade l-lysine with ΔLdcEt3 could reach up to 99% in 2 h in the fermenter. Experimental investigation and molecular dynamics confirmed that Na+-microenvironment could improve active-aggregation state and affect secondary structure of ΔLdcEt3. Therefore, Na+-microenvironment stabilizes ΔLdcEt3 providing a great potential industrial application for high-level cadaverine production.

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来源期刊
Green Chemical Engineering
Green Chemical Engineering Process Chemistry and Technology, Catalysis, Filtration and Separation
CiteScore
11.60
自引率
0.00%
发文量
58
审稿时长
51 days
期刊最新文献
OFC: Outside Front Cover Outside Back Cover Outside Back Cover OFC: Outside Front Cover Integration of physical information and reaction mechanism data for surrogate prediction model and multi-objective optimization of glycolic acid production
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