基于生物催化剂的多变金属有机框架的顺序优化
Weibin Liang1, Sisi Zheng2, Ross Andrew Shalliker1
1School of Science, Western Sydney University, Penrith, New South Wales, Australia.
Small (Weinheim an der Bergstrasse, Germany)
|February 17, 2026
概括
本研究引入了一种新的拉丁超立方体采样合贝叶斯优化 (LHS-BO) 工作流程,用于优化酶@金属有机框架生物复合材料 (E-MOF) 和生物. 优化的E-MOF和反应条件显著提高了酶稳定性和生物催化效率.
科学领域:
- 生物催化和反应工程
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 有效的生物级联需要对生物催化剂和反应条件进行综合优化.
- 酶@金属有机框架生物复合物 (E-MOF) 为酶稳定和增强催化活性提供了一个有前途的平台.
- 复杂系统的多变量优化,如E-MOF和生物级别仍然具有挑战性.
研究的目的:
- 开发和验证一个连续优化工作流程,将拉丁超立方采样和贝叶斯优化 (LHS-BO) 结合起来,用于设计多变量E-MOF和优化下游生物.
- 研究优化的E-MOFs对不同条件下的酶构成,活性和稳定性的影响.
- 通过集成的E-MOF设计和反应条件优化,实现高生产率的葡萄糖氧化酶-马过氧化酶 (GOx-HRP) 生物.
主要方法:
- 一个拉丁式超立方体采样-合贝叶斯优化 (LHS-BO) 工作流被用于顺序优化.
- 酶分析,ATR-FTIR和UV-Vis光谱学被用来描述优化的E-MOFs (ZG67,ZH16).
- 机器学习建模和微动力学建模被用于预测和验证生物级联性能.
主要成果:
- 优化的E-MOF (ZG67,ZH16) 显示出高封装效率 (90-92%),保持活性 (87-103%),以及在热和溶剂应力下增强的稳定性.
- 光谱分析证实,E-MOF在生物活性构成中稳定葡萄糖氧化酶 (GOx) 和胡卜过氧化酶 (HRP).
- 优化的GOx-HRP级联条件 (R49) 实现了超过95%的2,3-diaminophenazine (DAP) 理论最大生产率.
结论:
- 一般化的LHS-BO策略是合理的E-MOF设计和多酶级联优化的一个强大而强大的工具.
- 这种综合优化框架通过提高酶稳定性和催化效率,显著推进生物催化和反应工程.
- 实验,机器学习和动力建模之间的强烈一致性验证了拟议的优化方法.
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