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Related Concept Videos

Introduction to Enzymes01:22

Introduction to Enzymes

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The use of enzymes by humans dates to 7000 BCE. Humans first used enzymes to ferment sugars and produce alcohol without knowing that this was an enzyme-catalyzed reaction. Wilhelm Kuhne coined the term 'enzyme' in 1877 from the Greek words ‘en’ meaning ‘in’ or ‘within’ and ‘zyme’ meaning ‘yeast.’
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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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Introduction to Enzyme Kinetics01:19

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Enzyme kinetics studies the rates of biochemical reactions. Scientists monitor the reaction rates for a particular enzymatic reaction at various substrate concentrations. Additional trials with inhibitors or other molecules that affect the reaction rate may also be performed.
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Enzymes02:34

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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.
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Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System
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Click, Compute, Create: A Review of Web-based Tools for Enzyme Engineering.

Adrian Tripp1, Markus Braun1, Florian Wieser1

  • 1Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria.

Chembiochem : a European Journal of Chemical Biology
|April 18, 2024
PubMed
Summary

This review highlights in silico tools to accelerate enzyme engineering. Computational methods, including machine learning, aid in predicting structures, discovering enzymes, and enhancing their stability and production.

Keywords:
biocatalysiscomputational protein designcomputational toolsenzyme engineering

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Enzyme Engineering

Background:

  • Enzyme engineering is crucial for biotechnology but is often slow and resource-intensive.
  • In silico approaches offer a promising solution to streamline enzyme engineering processes.

Purpose of the Study:

  • To provide an overview of computational tools supporting enzyme engineering.
  • To cover methods from structure prediction and enzyme discovery to property modulation and redesign.

Main Methods:

  • Structure prediction and activity classification tools.
  • Methods for enhancing enzyme thermostability and production yields.
  • Computational approaches for modulating enzyme activity and selectivity.
  • Machine learning-based enzyme redesign strategies.

Main Results:

  • A comprehensive review of accessible in silico methodologies for enzyme engineering.
  • Focus on web-accessible tools and Python scripts for broad usability.
  • Integration of traditional computational methods with modern machine learning techniques.

Conclusions:

  • In silico tools significantly accelerate and improve enzyme engineering.
  • Accessible computational methods empower a wider research community.
  • Machine learning offers advanced capabilities for enzyme redesign.