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通过人工智能驱动的EGTD工程,通过多酶级联实现高效的ergothioneine合成

Yamiao Li¹, Jingxin Rao², Zhentao Jiang¹

¹School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China.

International journal of biological macromolecules

|September 1, 2025

概括

研究人员开发了一种高效的体外合成厄戈氨酸 (ERG) 的方法. 通过人工智能和酶工程, 他们显著提高了ERG的产量,

科学领域:

生物催化和酶工程
合成生物学
抗氧化剂研究

背景情况:

一种天然抗氧化剂,具有显著的生物医学潜力,但其微生物发酵产量较低.
对于其治疗和工业应用而言,高效的ERG生产至关重要.

研究的目的:

开发一种高效的ergothioneine (ERG) 生物合成的体外多酶级联.
在ERG生产中克服限制胺甲基化的步骤.
使用人工智能辅助方法建立ERG的准备规模合成.

主要方法:

整合机器学习 (CataPro,DLkcat) 的协同策略用于动力预测,分子动力学模拟和构造分析.
由计算分析指导的Mycolicibacrterium smegmatis EgtD的位点定向突变发生.
一个多酶级联与S-adenosyl-L-methionine (SAM) 再生模块的重建.

主要成果:

工程三重突变M3 (L53A/ T59S/ E282S) 的催化效率分别提高了3. 4倍和2. 8倍.
机械洞察力显示M3突变体的基质接近性,催化角度和联体道动力学有所改善.
实现了预制规模的ERG合成,其度为6.05g·L-1和生产率为10.01g·L-1d-1,这是迄今为止报告的最高值.

结论:

人工智能辅助的结构导向方法成功克服了ERG合成的酶催化过程中的进化约束.
设计的生物催化级联能够高效且可扩展的生产高价值的ergothioneine.
这项研究证明了加速开发高效生物催化工艺的强大平台.

关键词:

埃尔戈西奥内因甲基转移酶分子动力学

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