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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.
Chymotrypsin is a pancreatic enzyme that breaks down proteins during digestion....
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Enzyme Kinetics01:19

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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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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.
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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Predicting Reaction Outcomes02:24

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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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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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Predicting Products: Substitution vs. Elimination02:52

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When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
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Related Experiment Video

Updated: Jul 23, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning.

Alexander Kroll1, Yvan Rousset1, Xiao-Pan Hu1

  • 1Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany.

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|July 12, 2023
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Summary

We developed TurNuP, a novel computational model that accurately predicts enzyme efficiency (kcat) across different organisms. This tool enhances our understanding of cellular physiology and resource allocation by providing reliable enzyme turnover number estimates.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Systems Biology

Background:

  • Enzyme efficiency, measured by turnover number (kcat), is crucial for cellular physiology and resource allocation.
  • Experimental kcat data is scarce for most enzymes, necessitating accurate computational prediction methods.
  • Existing machine learning models lack organism independence and struggle with enzymes dissimilar to training data.

Purpose of the Study:

  • To develop a general and organism-independent computational model for predicting enzyme turnover numbers (kcat).
  • To improve the accuracy and generalizability of kcat predictions for wild-type enzymes.
  • To provide a tool for better understanding cellular resource allocation and metabolic modeling.

Main Methods:

  • Utilized differential reaction fingerprints to represent chemical reactions.
  • Employed a modified Transformer Network model for protein sequence representation.
  • Trained and validated the TurNuP model on a diverse dataset of natural enzymatic reactions.

Main Results:

  • TurNuP demonstrates superior performance compared to existing models.
  • The model exhibits strong generalization capabilities, even for enzymes with low sequence similarity to the training set.
  • Incorporating TurNuP-predicted kcat values into metabolic models improved proteome allocation predictions.

Conclusions:

  • TurNuP offers a robust and broadly applicable solution for predicting enzyme turnover numbers.
  • The model advances the study of molecular biochemistry and cellular physiology.
  • A web server has been developed to facilitate the use of TurNuP in research.