在体内自动化的酶工程加速了生物催化剂优化
Enrico Orsi1, Lennart Schada von Borzyskowski2, Stephan Noack3
1The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.
Nature communications
|April 24, 2024
概括
开发稳定的生物催化剂对于生物基过程至关重要. 这项研究提出了机器学习引导的自动化工作流程,以加速发现优质酶,减少手工劳动和增加吞吐量.
科学领域:
- 生物催化和酶工程 生物催化和酶工程
- 合成生物学 合成生物学
- 计算生物学 计算生物学
背景情况:
- 具有成本效益的生物制造依赖于高效的生物催化剂.
- 传统的酶工程方法是缓慢和劳动密集的.
- 机器学习 (ML) 提供了扩大酶设计可能性的潜力.
研究的目的:
- 为加速酶工程提供一个集成的,自动化的工作流.
- 为了利用机器学习来引导酶进化.
- 为工业应用增强优质生物催化剂的开发.
主要方法:
- 开发 ML 引导的自动化工作流程.
- 集成图书馆生成和超级突变系统.
- 实验室进化和体内生长合选择的实施.
主要成果:
- 拟议的自动化工作流显著增加了酶工程的吞吐量.
- 机器学习指导着更广泛的酶设计空间的探索.
- 可以实现稳定和选择性的生物催化剂的加速开发.
结论:
- 集成的,ML引导的自动化工作流程对于快速的生物催化剂开发是有效的.
- 这种方法克服了传统的低通量酶工程的局限性.
- 该战略加快了向具有成本竞争力的生物基工艺的发展进程.
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