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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.
 
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Non-equilibrium in the Cell01:16

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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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Synthetic Biology02:55

Synthetic Biology

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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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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.
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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.
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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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Related Experiment Video

Updated: Nov 9, 2025

Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System
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Revolutionizing enzyme engineering through artificial intelligence and machine learning.

Nitu Singh1, Sunny Malik1, Anvita Gupta2

  • 1Laboratory of Biocatalysis and Enzyme Engineering, Regional Centre for Biotechnology, Faridabad, Haryana 121001, India.

Emerging Topics in Life Sciences
|April 9, 2021
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) and Machine Learning (ML) offer powerful solutions for enzyme engineering, overcoming limitations of traditional methods. These AI tools enable smart enzyme design for improved activity, selectivity, and solubility, accelerating molecular biology advancements.

Keywords:
artificial intelligencedirected evolutionenzyme engineeringmachine learningrational protein design

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Molecular Biology

Background:

  • Traditional enzyme engineering faces challenges due to vast sequence space and limitations in achieving multi-target objectives like improved selectivity, solubility, and activity.
  • Existing methods often require extensive a priori knowledge or laborious screening of large protein libraries, hindering complex optimization.

Purpose of the Study:

  • To explore the role and potential of Artificial Intelligence (AI) and Machine Learning (ML) in revolutionizing enzyme engineering.
  • To demonstrate how AI can augment traditional approaches like directed evolution and rational design for enhanced enzyme optimization.
  • To discuss the scope, limitations, challenges, and future directions of AI in enzyme engineering.

Main Methods:

  • Review and analysis of current AI and ML techniques applicable to enzyme engineering.
  • Examination of how AI tools can extend traditional enzyme design methodologies.
  • Highlighting successful case studies of AI-assisted enzyme engineering projects.

Main Results:

  • AI and ML present a paradigm shift, enabling 'smart' enzyme engineering without complete molecular understanding.
  • AI tools can significantly enhance the efficiency and scope of enzyme optimization beyond traditional methods.
  • Successful AI-driven projects demonstrate novel approaches to enzyme design and improvement.

Conclusions:

  • AI and ML are poised to transform enzyme engineering by addressing complex multi-objective optimization challenges.
  • Integrating AI with directed evolution and rational design offers a powerful synergistic approach for future enzyme development.
  • Continued research into AI applications is crucial for unlocking the full potential of enzyme engineering.