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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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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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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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Introduction to Mechanisms of Enzyme Catalysis01:13

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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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Turnover Number and Catalytic Efficiency01:19

Turnover Number and Catalytic Efficiency

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The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
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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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Exploring the competitive dynamic enzyme allocation scheme through enzyme cost minimization.

Shanshan Qi1,2, Gangsheng Wang3,4, Wanyu Li1,2

  • 1State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China.

ISME Communications
|November 21, 2023
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Summary
This summary is machine-generated.

Microbial enzyme allocation models now include multiple enzyme groups for nitrogen cycling. This new model optimizes enzyme production based on substrate availability, minimizing costs for metabolic processes.

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Area of Science:

  • Microbial ecology
  • Biogeochemistry
  • Computational modeling

Background:

  • Enzyme allocation is a key microbial trait influencing soil biogeochemical cycles.
  • Existing microbial ecological models often lack comprehensive enzyme allocation schemes for multiple enzyme groups.
  • Understanding enzyme dynamics is crucial for predicting ecosystem responses to climate change.

Purpose of the Study:

  • To develop a novel COmpetitive Dynamic Enzyme ALlocation (CODEAL) scheme for six enzyme groups involved in inorganic nitrogen transformations.
  • To integrate this scheme into the Microbial-ENzyme Decomposition (MEND) model.
  • To investigate how enzyme allocation strategies impact microbial metabolic efficiency and nitrogen cycling.

Main Methods:

  • Development of a dynamic enzyme allocation scheme with time-variant coefficients for multiple enzyme groups.
  • Implementation of mutual competition among enzyme groups within the MEND model.
  • Utilizing substrate saturation levels to guide enzyme allocation and minimize metabolic costs.

Main Results:

  • The CODEAL scheme successfully models the allocation of six enzyme groups for nitrogen cycling.
  • Enzyme cost minimization is achieved by allocating enzymes based on substrate saturation levels.
  • Relative substrate availability influences the trade-off between enzyme production and metabolic flux.

Conclusions:

  • The CODEAL scheme provides a more nuanced understanding of enzyme-mediated biogeochemical processes.
  • This model advances microbial ecological modeling by incorporating competitive enzyme allocation.
  • Findings offer insights into nitrogen cycle dynamics and microbial adaptation strategies.