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Sequential Optimization of Multivariate Metal-Organic Framework Based Biocatalysis.

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Summary
This summary is machine-generated.

This study introduces a novel Latin hypercube sampling-coupled Bayesian optimization (LHS-BO) workflow for optimizing enzyme@metal-organic framework biocomposites (E-MOFs) and biocascades. The optimized E-MOFs and reaction conditions significantly enhance enzyme stability and biocatalytic efficiency.

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bayesian optimizationenzyme biocatalysismetal–organic framework‐based biocomposite

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Area of Science:

  • Biocatalysis and Reaction Engineering
  • Materials Science
  • Computational Chemistry

Background:

  • Efficient biocascades necessitate integrated optimization of biocatalysts and reaction conditions.
  • Enzyme@metal-organic framework biocomposites (E-MOFs) offer a promising platform for enzyme stabilization and enhanced catalytic activity.
  • Multivariate optimization of complex systems like E-MOFs and biocascades remains challenging.

Purpose of the Study:

  • To develop and validate a sequential optimization workflow combining Latin hypercube sampling-Bayesian optimization (LHS-BO) for designing multivariate E-MOFs and optimizing downstream biocascades.
  • To investigate the impact of optimized E-MOFs on enzyme conformation, activity, and stability under various conditions.
  • To achieve high production rates in glucose oxidase-horseradish peroxidase (GOx-HRP) biocascades through integrated E-MOF design and reaction condition optimization.

Main Methods:

  • A Latin hypercube sampling-coupled Bayesian optimization (LHS-BO) workflow was employed for sequential optimization.
  • Enzymatic assays, ATR-FTIR, and UV-Vis spectroscopy were used to characterize the optimized E-MOFs (ZG67, ZH16).
  • Machine learning modeling and microkinetic modeling were utilized to predict and validate biocascade performance.

Main Results:

  • Optimized E-MOFs (ZG67, ZH16) demonstrated high encapsulation efficiency (90-92%), retained activity (87-103%), and enhanced stability under thermal and solvent stress.
  • Spectroscopic analyses confirmed that E-MOFs stabilize glucose oxidase (GOx) and horseradish peroxidase (HRP) in bioactive conformations.
  • The optimized GOx-HRP cascade condition (R49) achieved over 95% of the theoretical maximum production rate for 2,3-diaminophenazine (DAP).

Conclusions:

  • The generalized LHS-BO strategy is a robust and powerful tool for rational E-MOF design and multi-enzyme cascade optimization.
  • This integrated optimization framework significantly advances biocatalysis and reaction engineering by improving enzyme stability and catalytic efficiency.
  • The strong agreement between experimental, machine learning, and kinetic modeling validates the proposed optimization approach.