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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.
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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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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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The Integrated Rate Law: The Dependence of Concentration on Time02:39

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While the differential rate law relates the rate and concentrations of reactants, a second form of rate law called the integrated rate law relates concentrations of reactants and time. Integrated rate laws can be used to determine the amount of reactant or product present after a period of time or to estimate the time required for a reaction to proceed to a certain extent. For example, an integrated rate law helps determine the length of time a radioactive material must be stored for its...
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Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
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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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Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data.

Daniel C Zielinski1, Marta R A Matos2, James E de Bree1

  • 1Department of Bioengineering, University of California, San Diego, CA, 92093, USA.

Metabolic Engineering Communications
|May 7, 2024
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Summary
This summary is machine-generated.

This study introduces MASSef, a computational workflow for robustly estimating enzyme kinetic parameters. It reconciles inconsistent data and builds scalable metabolic models, leveraging existing experimental and machine learning data.

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Area of Science:

  • Metabolic Engineering
  • Systems Biology
  • Computational Biology

Background:

  • Kinetic models are crucial for understanding metabolic systems and designing production strains.
  • Assembling kinetic models enzyme-by-enzyme ('bottom-up') faces challenges like data gaps, complex mechanisms, and in vitro-in vivo discrepancies.

Purpose of the Study:

  • To develop a robust computational workflow for estimating kinetic parameters in detailed mass action enzyme models.
  • To create a software package (MASSef) for handling diverse kinetic parameters and reaction mechanisms.
  • To reconcile inconsistent kinetic data and enable the construction of scalable, in vivo-consistent pathway models.

Main Methods:

  • Developed a computational workflow for robust kinetic parameter estimation, accounting for parameter uncertainty.
  • Implemented the workflow in the MASSef software package, supporting macroscopic and microscopic kinetic parameters.
  • Utilized three enzyme case studies to demonstrate data reconciliation and model assembly.

Main Results:

  • MASSef successfully identified and reconciled inconsistent kinetic data from in vitro experiments and between in vitro and in vivo conditions.
  • Parameterized enzyme modules were effectively used to assemble pathway-scale kinetic models that align with in vivo behavior.
  • The workflow demonstrated robustness in estimating kinetic parameters for detailed mass action enzyme models.

Conclusions:

  • The MASSef workflow provides a robust method for parameterizing enzyme kinetic models at scale.
  • This approach effectively utilizes historical literature data and machine learning estimates to overcome limitations in kinetic modeling.
  • Enables the creation of more accurate and predictive metabolic models for research and strain design.