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Related Concept Videos

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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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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Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

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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.
The experimenter can then plot the initial reaction rate or velocity (Vo) of a given trial against the substrate concentration ([S]) to obtain a graph of the reaction properties. For many enzymatic reactions involving a...
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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.
 
Most enzymes...
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Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

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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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Enzyme Inhibition01:30

Enzyme Inhibition

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Inhibitors are molecules that reduce enzyme activity by binding to the enzyme. In a normally functioning cell, enzymes are regulated by a variety of inhibitors. Drugs and other toxins can also inhibit enzymes. Some inhibitors bind to the enzyme’s active site, while others inhibit enzymatic activity by binding to other sites on the protein structure.
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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

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Large language model agents as experimental orchestrators in data-driven enzyme engineering.

Yuan Gao1, Lihao Fu2, Tong Si1

  • 1State Key Laboratory for Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China.

Current Opinion in Biotechnology
|November 1, 2025
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Summary
This summary is machine-generated.

Large language model (LLM) agents streamline enzyme engineering by intelligently orchestrating experimental workflows. These AI systems dynamically generate protocols and adapt to feedback, accelerating the design-build-test-learn cycle.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Artificial Intelligence

Background:

  • Computational enzyme design is advancing rapidly due to artificial intelligence (AI).
  • Experimental validation of AI-driven enzyme designs faces significant bottlenecks.
  • Current automation methods lack the flexibility to manage complex experimental workflows.

Purpose of the Study:

  • To examine how large language model (LLM) agents can bridge the gap between computational enzyme design and experimental validation.
  • To highlight the role of LLM agents in orchestrating complex experimental workflows.
  • To showcase the potential of LLM agents in accelerating enzyme engineering.

Main Methods:

  • Utilizing multi-agent frameworks powered by LLMs.
  • Dynamically generating experimental protocols and translating them into machine commands.
  • Coordinating resources and adapting to real-time experimental feedback for closed-loop iterations.

Main Results:

  • LLM agents enable intelligent orchestration of experimental workflows, moving beyond rigid scripting.
  • Demonstrated closed-loop design-build-test-learn cycles in enzyme engineering using LLM agents.
  • Showcased the ability of LLM systems to handle complex, context-dependent experimental decisions.

Conclusions:

  • LLM agents offer a powerful solution to the experimental validation bottleneck in computational enzyme design.
  • These AI systems provide both standardization and flexibility, accelerating enzyme engineering.
  • LLM-powered experimental orchestration enhances accessibility to advanced AI capabilities in scientific research.