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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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Enzymes and Activation Energy01:13

Enzymes and Activation Energy

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The activation energy (or free energy of activation), abbreviated as Ea, is the small amount of energy input necessary for all chemical reactions to occur. During chemical reactions, certain chemical bonds break, and new ones form. For example, when a glucose molecule breaks down, bonds between the molecule's carbon atoms break. Since these are energy-storing bonds, they release energy when broken. However, the molecule must be somewhat contorted to get into a state that allows the bonds to...
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Arrhenius Plots02:34

Arrhenius Plots

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The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules.
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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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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.
Enzyme deficiencies can often translate into life-threatening diseases. For example, a genetic abnormality resulting in the deficiency of the enzyme G6PD...
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Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
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On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing

Hiroaki Okada1, Satoshi Maeda2,3,4,5

  • 1Graduate School of Chemical Sciences and Engineering, Hokkaido University, Sapporo, Hokkaido 060-8628, Japan.

ACS Omega
|February 19, 2024
PubMed
Summary
This summary is machine-generated.

Computational Gibbs energy barriers can significantly reduce the number of experiments needed for substrate optimization in organic synthesis. This approach, even with noise, enhances efficiency in chemical research.

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Area of Science:

  • Organic Synthesis
  • Computational Chemistry
  • Chemoinformatics

Background:

  • Substrate optimization is crucial but resource-intensive in organic synthesis.
  • Bayesian optimization (BO) offers systematic improvements.
  • Further reducing experimental needs is a key challenge.

Purpose of the Study:

  • To investigate the utility of computational Gibbs energy barriers for reducing experiments in BO-assisted substrate optimization.
  • To assess the impact of noise in computational data on optimization efficiency.

Main Methods:

  • Utilized a literature dataset of computational Gibbs energy barriers.
  • Performed extensive numerical simulations, treating barriers as both virtual experimental and noisy computational results.
  • Evaluated the reduction in required experiments under different noise conditions.

Main Results:

  • Computational Gibbs energy barriers, even with up to 20 kJ/mol of noise, effectively reduce the number of experiments.
  • The inclusion of computational data enhances the efficiency of substrate optimization.
  • Noise in computational results does not negate the benefits of this approach.

Conclusions:

  • Computational Gibbs energy barriers are a viable tool to accelerate substrate optimization.
  • This method offers a significant advantage over traditional experimental approaches.
  • The findings support the integration of computational chemistry in synthetic planning.