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Related Concept Videos

Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

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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Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...
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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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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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Introduction To Enzymes01:22

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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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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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Updated: Jul 12, 2026

A Practical Guide to Phage- and Robotics-Assisted Near-Continuous Evolution
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Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.

Kexin Hao1, Jianguang Liu2, Hui Tang1

  • 1State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Institute of Pharmaceutical Research, 306 Huiren Road, Tianjin, 300301, China.

Bioresources and Bioprocessing
|July 10, 2026
PubMed
Summary

Artificial intelligence (AI) and automation are transforming enzyme engineering, moving beyond trial-and-error to data-driven design. This shift accelerates enzyme optimization for industrial applications, though challenges in data and interpretability persist.

Keywords:
Artificial intelligenceAutomation technologiesBiofoundryDesign-build-test-learn (DBTL) cycleEnzyme engineeringProtein language models

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Area of Science:

  • Biotechnology
  • Biomanufacturing
  • Enzyme Engineering

Background:

  • Natural enzymes often lack the efficiency, stability, and specificity required for industrial biomanufacturing.
  • Traditional enzyme engineering relies on empirical trial-and-error, which is slow and inefficient.

Purpose of the Study:

  • To review the convergence of artificial intelligence (AI) and automation in reshaping enzyme engineering.
  • To analyze the transition towards data-driven, closed-loop enzyme design systems.
  • To provide a roadmap for advancing AI-guided and autonomous enzyme engineering.

Main Methods:

  • Tracing the evolution of AI in enzyme engineering, from machine learning to deep learning and protein language models.
  • Examining the progression of automation, from standalone tasks to integrated biofoundry workflows.
  • Analyzing AI and automation convergence using a stage-based autonomy framework, including Design-Build-Test-Learn (DBTL) systems.

Main Results:

  • AI-guided prediction, automated experimentation, and active learning accelerate enzyme optimization.
  • The convergence enables semi-automated, conditional, and high-autonomy DBTL systems.
  • Key barriers include biased datasets, limited generalization, poor interpretability, and automation interoperability.

Conclusions:

  • AI and automation are crucial for overcoming limitations in current enzyme engineering.
  • Addressing challenges in data, interpretability, and modularity is essential for future progress.
  • Developing autonomous closed-loop systems and FAIR data infrastructure will advance enzyme engineering.