一个依赖NADH的7β-Hydroxysteroid脱酶的机器学习指导工程,用于经济合成 Ursodeoxycholic 酸
Mu-Qiang Wang1, Zhi-Neng You1, Bing-Yi Yang1
1Laboratory of Biocatalysis and Synthetic Biotechnology, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China.
Journal of agricultural and food chemistry
|November 28, 2023
概括
机器学习增强了一种依赖于NADH的酶,用于生产ursodeoxycholic acid (UDCA). 改进的酶变体显著提高了UDCA生物合成的效率和成本,使其更具经济可行性.
科学领域:
- 生物催化和酶工程 生物催化和酶工程
- 代谢工程是代谢工程.
- 机器学习在生物技术中的应用
背景情况:
- 使用依赖NADH的7β-基类固醇脱酶 (7β-HSDH) 酶合成ursodeoxycholic acid (UDCA) 由于辅因子成本,比依赖NADH的系统具有经济优势.
- 由于其较差的催化性能,NADH依赖的7β-HSDH的实际应用受到阻碍.
研究的目的:
- 使用机器学习引导的蛋白质工程来增强来自Ruminococcus扭矩的NADH依赖的7β-HSDH的催化性能.
- 提高UDCA生物合成的效率和经济可行性.
主要方法:
- 机器学习算法包括随机森林,高斯天真贝叶斯分类器和高斯过程回归被用于蛋白质工程.
- 用定向进化和实验验证来确定最佳的酶变体.
- 准备性生物转化使用"一子中的两个阶段"序列过程进行.
主要成果:
- 一种优异的变体,Rt*7β-HSDHM3 (R40I/R41K/F94Y/S196A/Y253F),被开发出来,在40°C时,其特定活性增加了4.1倍,半衰期改善了8.3倍.
- 该工程酶实现了192g的时空产量 (STY) L-1 d-1 用于UDCA生物合成从使用NAD+作为辅因子的氧化胆酸 (CDCA) 作为辅因子.
- 与原始酶 (*Rt*7β-HSDHM0) 相比,使用*Rt*7β-HSDHM3的UDCA生产原材料成本下降了22%.
结论:
- 机器学习引导的蛋白质工程有效地显著提高了依赖NADH的7β-HSDH的性能.
- 变种*Rt*7β-HSDHM3显示出 UDCA 的高效和成本效益的工业生产的巨大潜力.
- 这项研究突出了优化生物催化剂用于制药合成的有希望的方法.
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