机器学习辅助的新型信号的合理设计和进化在Yarrowia lipolytica中
Zizhao Wu1,2, Wenhao Chen1, Yuxiang Hong1
1Department of Chemical Engineering, Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion (MATEC), Guangdong Technion - Israel Institute of Technology, Shantou, 515063, China.
Synthetic and systems biotechnology
|August 14, 2025
概括
研究人员在Yarrowia lipolytica中开发了新的信号 (SP),以增强蛋白质分泌. 新的SP提高了Nanoluc luciferase分泌的2.91倍,扩大了可持续蛋白质生产的工具.
科学领域:
- 生物技术是生物技术.
- 合成生物学 合成生物学
- 蛋白质工程是指蛋白质的工程.
背景情况:
- 油性酵母Yarrowia lipolytica是可持续微生物蛋白质生产的有希望的平台.
- N-终端信号 (SPs) 对于将蛋白质引导到分泌途径至关重要,对于高效的蛋白质生产至关重要.
- 扩大Y. lipolytica的SP工具包是必要的,以满足对异质蛋白过度表达的日益增长的需求.
研究的目的:
- 进化和识别新型,高性能信号 (SPs) 以提高Yarrowia lipolytica中的异质蛋白质分泌.
- 评估新发现的SPs在不同分泌的蛋白质中的通用性和多功能性.
- 评估用于预测信号性能的机器学习模型.
主要方法:
- 采用定向进化方法,使用吉布森组合与退化的核酸快速进化XPR2-pre SP.
- 利用纳诺卢克 (Nluc) 光酶作为记者来选447种SP突变体,以提高分泌效率.
- 验证了顶级SPs与异种酶 (β-galactosidase,α-amylase,PET hydrolase) 的性能,并评估了机器学习模型.
主要成果:
- 确定了新的SPs,在Nluc luciferase分泌中显著优于原生XPR2-pre SP,酶活性增加高达2.91倍.
- 证明了在多个异构酶中表现最好的SPs的多功能性,表明了蛋白质特异性效率.
- 机器学习模型显示出基于查数据预测SP突变性能的潜力.
结论:
- 成功扩大了Yarrowia lipolytica的功能信号的曲目.
- 新型SPs提高了分泌蛋白过度表达的效率,使Y. lipolytica作为细胞工厂受益.
- 定向进化和机器学习的整合为信号的发现和优化提供了一个强大的策略.
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