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Related Concept Videos

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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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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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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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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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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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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Related Experiment Video

Updated: May 26, 2025

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
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Robust Prediction of Enzyme Variant Kinetics with RealKcat.

Karuna Anna Sajeevan1,2, Abraham Osinuga3, Arunraj B1

  • 1Department of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA.

Biorxiv : the Preprint Server for Biology
|February 24, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed RealKcat, a novel computational model for predicting enzyme kinetics. This tool accurately forecasts mutation effects on enzyme activity, advancing enzyme design and biocatalysis applications.

Keywords:
Bio-aware Machine learningBiocatalysisBiochemistryBiophysics and Computational BiologyDatabase curationEnzyme engineeringEnzyme kineticsSystems Biology

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Steady-state, Pre-steady-state, and Single-turnover Kinetic Measurement for DNA Glycosylase Activity
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Area of Science:

  • Biocatalysis and Enzyme Engineering
  • Computational Biology and Bioinformatics
  • Protein Science

Background:

  • Accurate prediction of enzyme kinetic parameters (catalytic turnover, substrate affinity) is vital for understanding enzyme function and designing novel biocatalysts.
  • Existing computational models often struggle to accurately predict the impact of mutations on catalytically essential residues, hindering their application in enzyme design.
  • The development of sophisticated predictive models is necessary to overcome these limitations and enable precise enzyme engineering.

Purpose of the Study:

  • To develop and validate a novel computational framework, RealKcat, for the accurate prediction of enzyme kinetic parameters, specifically catalytic turnover (kcat) and substrate affinity (Km).
  • To address the limitations of current models in capturing mutation effects on catalytically essential residues.
  • To establish a new benchmark in predicting enzyme activity changes due to genetic modifications.

Main Methods:

  • Extensive grid search across ten model architectures and 25,671 hyperparameter combinations.
  • Development of a gradient-based additive framework named RealKcat.
  • Training the model on a manually curated dataset (KinHub-27k) comprising 27,176 experimental entries from 2,158 scientific articles.
  • Clustering kinetic parameters (kcat, Km) by rational orders of magnitude for robust analysis.

Main Results:

  • RealKcat achieved >85% test accuracy in predicting kinetic parameters.
  • The model demonstrated superior sensitivity to mutation-induced variability compared to existing methods.
  • RealKcat was the first model to accurately predict complete loss of enzyme activity upon deletion of catalytic residues.
  • Achieved state-of-the-art 96% validation accuracy on an industrial alkaline phosphatase (PafA) mutant dataset, confirming generalizability.

Conclusions:

  • RealKcat represents a significant advancement in predicting enzyme kinetics and the impact of mutations.
  • The model's ability to accurately capture per-residue catalytic relevance enhances its utility in enzyme design and directed evolution.
  • RealKcat's performance on industrial datasets validates its potential for practical applications in biocatalysis and enzyme engineering.