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Related Concept Videos

Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

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The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
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Introduction to Mechanisms of Enzyme Catalysis01:13

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For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes...
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Introduction to Mechanisms of Enzyme Catalysis01:13

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Turnover Number and Catalytic Efficiency01:19

Turnover Number and Catalytic Efficiency

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The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
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Enzyme Kinetics01:19

Enzyme Kinetics

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Enzymes speed up reactions by lowering the activation energy of the reactants. The speed at which the enzyme turns reactants into products is called the rate of reaction. Several factors impact the rate of reaction, including the number of available reactants. Enzyme kinetics is the study of how an enzyme changes the rate of a reaction.
Scientists typically study enzyme kinetics with a fixed amount of enzyme in the controlled environment of a test tube. When more reactant, or substrate, is...
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Enzymes02:34

Enzymes

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Inside living organisms, enzymes act as catalysts for many biochemical reactions involved in cellular metabolism. The role of enzymes is to reduce the activation energies of biochemical reactions by forming complexes with its substrates. The lowering of activation energies favor an increase in the rates of biochemical reactions.
Enzyme deficiencies can often translate into life-threatening diseases. For example, a genetic abnormality resulting in the deficiency of the enzyme G6PD...
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Updated: Apr 19, 2026

Modeling an Enzyme Active Site using Molecular Visualization Freeware
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Rational and Multidimensional Optimization of Nanozyme Catalytic Performance.

Zheng Xu1, Kelong Fan1

  • 1State Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.

Small (Weinheim an Der Bergstrasse, Germany)
|April 17, 2026
PubMed
Summary

Nanozymes, nanomaterials with enzyme-like activity, offer enhanced stability and cost-effectiveness. This review details strategies for optimizing their catalytic performance through material and environmental regulation for advanced applications.

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Area of Science:

  • Materials Science
  • Biochemistry
  • Nanotechnology

Background:

  • Nanozymes are nanomaterials with inherent biocatalytic properties, surpassing natural enzymes in stability and cost.
  • They are crucial in environmental monitoring, biosensing, and biomedical diagnostics.
  • Optimizing nanozyme activity is challenging due to complex synergistic effects.

Purpose of the Study:

  • To provide a comprehensive perspective on optimizing nanozyme catalytic performance.
  • To systematically review structure-activity relationships and enhancement strategies.
  • To integrate mechanistic insights for rational nanozyme design.

Main Methods:

  • Summarizing fundamental catalytic mechanisms and nanozyme classes.
  • Discussing intrinsic material regulation (oxidation states, defects) and extrinsic environmental regulation (pH, temperature).
  • Reviewing theoretical calculations and machine learning for nanozyme design.

Main Results:

  • Detailed structure-activity relationships at the nanoscale.
  • Identified key strategies for intrinsic and extrinsic performance enhancement.
  • Highlighted the role of computational and AI approaches in guiding design.

Conclusions:

  • A unified framework for rational nanozyme optimization is proposed.
  • Integrating mechanistic insights with regulation strategies is key.
  • This review guides future development of high-performance nanozymes.