Liang Weibin, Zheng Sisi, Shu Ying, Huang Jun
School of Chemical and Biomolecular Engineering, The University of Sydney, Darlington, NSW 2008, Australia.
JACS Au. 2024 Aug 12;4(8):3170-3182. doi: 10.1021/jacsau.4c00485. eCollection 2024 Aug 26.
In this study, we present the first example of using a machine learning (ML)-assisted design strategy to optimize the synthesis formulation of enzyme/ZIFs (zeolitic imidazolate framework) for enhanced performance. Glucose oxidase (GOx) and horseradish peroxidase (HRP) were chosen as model enzymes, while Zn(eIM) (eIM = 2-ethylimidazolate) was selected as the model ZIF to test our ML-assisted workflow paradigm. Through an iterative ML-driven training-design-synthesis-measurement workflow, we efficiently discovered GOx/ZIF (G151) and HRP/ZIF (H150) with their overall performance index (OPI) values (OPI represents the product of encapsulation efficiency ( in %), retained enzymatic activity ( in %), and thermal stability ( in %)) at least 1.3 times higher than those in systematic seed data studies. Furthermore, advanced statistical methods derived from the trained random forest model qualitatively and quantitatively reveal the relationship among synthesis, structure, and performance in the enzyme/ZIF system, offering valuable guidance for future studies on enzyme/ZIFs. Overall, our proposed ML-assisted design strategy holds promise for accelerating the development of enzyme/ZIFs and other enzyme immobilization systems for biocatalysis applications and beyond, including drug delivery and sensing, among others.
在本研究中，我们展示了首个使用机器学习（ML）辅助设计策略来优化酶/沸石咪唑酯骨架（ZIFs）的合成配方以提高性能的实例。选择葡萄糖氧化酶（GOx）和辣根过氧化物酶（HRP）作为模型酶，同时选择Zn（eIM）（eIM = 2-乙基咪唑）作为模型ZIF来测试我们的ML辅助工作流程范式。通过迭代的ML驱动的训练-设计-合成-测量工作流程，我们高效地发现了GOx/ZIF（G151）和HRP/ZIF（H150），其整体性能指数（OPI）值（OPI代表包封效率（以%计）、保留的酶活性（以%计）和热稳定性（以%计）的乘积）至少比系统种子数据研究中的值高1.3倍。此外，从训练后的随机森林模型衍生出的先进统计方法定性和定量地揭示了酶/ZIF系统中合成、结构和性能之间的关系，为未来关于酶/ZIFs的研究提供了有价值的指导。总体而言，我们提出的ML辅助设计策略有望加速酶/ZIFs和其他用于生物催化应用及其他领域（包括药物递送和传感等）的酶固定化系统的开发。
