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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.
 
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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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Enzymes02:34

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Introduction to Enzymes01:22

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Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Artificial Intelligence-Driven Enzyme Engineering from Structural Prediction to De Novo Design.

Hongling Shi1,2, Xueyang Bai1, Fangyuan Tian1

  • 1Henan Provincial Engineering Research Center of Insect Bio-reactor, College of Life Science, Nanyang Normal University, 1638 Wolong Road, Nanyang, Henan 473061, People's Republic of China.

Journal of Agricultural and Food Chemistry
|March 18, 2026
PubMed
Summary

Artificial intelligence (AI) is revolutionizing enzyme design, moving beyond analysis to prescriptive creation. AI tools now enable the custom design of enzymes with specific catalytic properties, accelerating biocatalysis innovation.

Keywords:
AI in biocatalysisde novo enzyme designenzyme engineeringindustrial biocatalysisprotein language models

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A Protocol for Computer-Based Protein Structure and Function Prediction
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Area of Science:

  • Biocatalysis
  • Computational enzymology
  • Protein engineering

Background:

  • Artificial intelligence (AI) is transforming biocatalysis from structural analysis to prescriptive enzyme design.
  • Breakthroughs like AlphaFold2 and CLEAN now link protein sequences to catalytic properties such as kinetics and substrate specificity.

Purpose of the Study:

  • To review the AI-driven revolution in biocatalysis and enzyme design.
  • To compare rational design strategies, including evolutionary-guided redesign and generative de novo approaches.
  • To illustrate the role of AI algorithms in understanding protein chemistry and overcoming design challenges.

Main Methods:

  • Dissection of AI algorithms like Graph Neural Networks and Transformers.
  • Evaluation of breakthroughs such as AlphaFold2 and CLEAN.
  • Analysis of generative models including diffusion models and protein language models (PLMs).

Main Results:

  • AI effectively bridges protein sequences with desired catalytic functions.
  • Generative models explore novel sequence spaces for enzyme discovery.
  • AI addresses critical challenges like the stability-activity trade-off in enzyme engineering.

Conclusions:

  • AI provides a roadmap for next-generation computational enzymology.
  • Engineered biocatalysts will be systematically integrated into metabolic networks.
  • The future includes autonomous biofoundries and virtual cell modeling for advanced biocatalysis.