Data-Driven Evolutionary Design of Multienzyme-like Nanozymes

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 CuMnCo7O12, 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.