College of Materials Science and Engineering, Qingdao University of Science and Technology, 53 Zhengzhou Road, Qingdao 266042, Shandong, China.
J Am Chem Soc. 2024 Mar 20;146(11):7565-7574. doi: 10.1021/jacs.3c13588. Epub 2024 Mar 6.
Multienzyme-like nanozymes are nanomaterials with multiple enzyme-like activities and are the focus of nanozyme research owing to their ability to facilitate cascaded reactions, leverage synergistic effects, and exhibit environmentally responsive selectivity. However, multienzyme-like nanozymes exhibit varying enzyme-like activities under different conditions, making them difficult to precisely regulate according to the design requirements. Moreover, individual enzyme-like activity in a multienzyme-like activity may accelerate, compete, or antagonize each other, rendering the overall activity a complex interplay of these factors rather than a simple sum of single enzyme-like activity. A theoretically guided strategy is highly desired to accelerate the design of multienzyme-like nanozymes. Herein, nanozyme information was collected from 4159 publications to build a nanozyme database covering element type, element ratio, chemical valence, shape, pH, etc. Based on the clustering correlation coefficients of the nanozyme information, the material features in distinct nanozyme classifications were reorganized to generate compositional factors for multienzyme-like nanozymes. Moreover, advanced methods were developed, including the quantum mechanics/molecular mechanics method for analyzing the surface adsorption and binding energies of substrates, transition states, and products in the reaction pathways, along with machine learning algorithms to identify the optimal reaction pathway, to aid the evolutionary design of multienzyme-like nanozymes. This approach culminated in creating CuMnCoO, a highly active multienzyme-like nanozyme. This process is named the genetic-like evolutionary design of nanozymes because it resembles biological genetic evolution in nature and offers a feasible protocol and theoretical foundation for constructing multienzyme-like nanozymes.
多酶样纳米酶是具有多种酶样活性的纳米材料，由于其能够促进级联反应、利用协同效应并表现出对环境响应的选择性，因此成为纳米酶研究的焦点。然而，多酶样纳米酶在不同条件下表现出不同的酶样活性，使得根据设计要求精确调节变得困难。此外，多酶样活性中的单个酶样活性可能会相互促进、竞争或拮抗，使得整体活性成为这些因素的复杂相互作用，而不是单个酶样活性的简单总和。因此，非常需要一种理论指导的策略来加速多酶样纳米酶的设计。
在这里，从 4159 篇出版物中收集了纳米酶信息，构建了一个涵盖元素类型、元素比例、化学价、形状、pH 值等纳米酶数据库。基于纳米酶信息的聚类相关系数，对不同纳米酶分类中的材料特征进行了重新组织，以生成多酶样纳米酶的组成因素。此外，还开发了先进的方法，包括量子力学/分子力学方法来分析底物、过渡态和产物在反应途径中的表面吸附和结合能，以及机器学习算法来识别最佳反应途径，以辅助多酶样纳米酶的进化设计。这种方法最终成功地创建了具有高活性的多酶样纳米酶 CuMnCoO。这种方法被命名为纳米酶的遗传样进化设计，因为它类似于自然界中的生物遗传进化，为构建多酶样纳米酶提供了可行的方案和理论基础。
