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Related Concept Videos

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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Determination of Michaelis Constant and Maximum Elimination Rate01:20

Determination of Michaelis Constant and Maximum Elimination Rate

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The Michaelis constant (KM) and the theoretical maximum process rate (Vmax) are vital parameters in the Michaelis-Menten equation, central to many biochemical reactions. They provide essential insights into enzyme kinetics and drug metabolism.
These parameters can be estimated by analyzing plasma concentration data post-drug administration. A notable example of this application is phenytoin, a drug with capacity-limited kinetics. It's recommended that phenytoin should be administered at two...
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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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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.
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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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Calculating Enzyme Inhibition with Random Forests.

Amauri Duarte da Silva1, Walter Filgueira de Azevedo2

  • 1Graduate Program in Information Technologies and Health Management, Federal University of Health Sciences of Porto Alegre, Porto Alegre, RS, Brazil.

Methods in Molecular Biology (Clifton, N.J.)
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Summary

This study introduces a Random Forest model for predicting binding affinity using molecular docking. The machine learning approach aids in identifying potential anticancer drugs targeting cyclin-dependent kinase 2.

Keywords:
Artificial intelligenceComplex systemsEnsemble methodsMachine learningMolegro Virtual DockerRandom forestSAnDReS 2.0

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Area of Science:

  • Computational chemistry and cheminformatics
  • Artificial intelligence in drug discovery
  • Machine learning for molecular modeling

Background:

  • Cyclin-dependent kinase 2 (CDK2) is a crucial target for anticancer drug development due to its role in cell cycle regulation.
  • Accurate prediction of binding affinity is essential for identifying potent drug candidates.
  • Molecular docking simulations provide structural insights into drug-target interactions.

Purpose of the Study:

  • To develop a regression model for predicting binding affinity using docked structures.
  • To apply the Random Forest machine learning algorithm for enhanced predictive accuracy.
  • To create a computational workflow for drug discovery targeting CDK2.

Main Methods:

  • Utilized the Random Forest algorithm from the Scikit-Learn library for regression modeling.
  • Employed molecular docking results generated by Molegro Virtual Docker (MVD).
  • Developed a Google Colab workflow integrating docking data and machine learning models (MVD4ML and SKReg4Model).

Main Results:

  • Successfully built a Random Forest regression model to predict the inhibition of cyclin-dependent kinase 2.
  • The workflow demonstrated the feasibility of using docking results for generating predictive regression models.
  • Code and datasets are publicly available on GitHub for reproducibility and further research.

Conclusions:

  • Random Forest regression is a powerful tool for predicting binding affinity in drug discovery.
  • The developed workflow streamlines the process of identifying potential CDK2 inhibitors.
  • Open-source availability of tools and data promotes collaborative research in computational drug design.