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Related Concept Videos

Inhibition of Cdk Activity02:34

Inhibition of Cdk Activity

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The orderly progression of the cell cycle depends on the activation of Cdk protein by binding to its cyclin partner. However, the cell cycle must be restricted when undergoing abnormal changes. Most cancers correlate to the deregulated cell cycle, and since Cdks are a central component of the cell cycle, Cdk inhibitors are extensively studied to develop anticancer agents. For instance, cyclin D associates with several Cdks, such as Cdk 4/6, to form an active complex. The cyclin D-Cdk4/6 complex...
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Checkpoints throughout the cell cycle serve as safeguards and gatekeepers, allowing the cell cycle to progress in favorable conditions and slow or halt it in problematic ones. This regulation is known as the cell cycle control system.
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Positive regulators allow a cell to advance through cell cycle checkpoints. Negative regulators have an equally important role as they terminate a cell’s progression through the cell cycle—or pause it—until the cell meets specific criteria.
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Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
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Elastic Net Regression to Predict CDK2 Inhibition.

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.)
|October 11, 2025
PubMed
Summary
This summary is machine-generated.

Elastic Net regression models complex protein systems, like protein-drug interactions. This study details its use for predicting enzyme inhibition, outperforming traditional scoring functions.

Keywords:
Artificial intelligenceCDK2Complex systemsElastic NetMachine learningSAnDReS 2.0Scoring function space

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Area of Science:

  • Computational biology
  • Cheminformatics
  • Drug discovery

Background:

  • Elastic Net regression is a powerful tool for building predictive models in complex biological systems.
  • Protein-drug interactions and enzyme inhibition are critical areas in drug discovery and development.
  • Existing computational methods for predicting enzyme inhibition can be improved.

Purpose of the Study:

  • To explain the Elastic Net regression method and its application in modeling protein systems.
  • To introduce the SAnDReS 2.0 program for building regression models to predict enzyme inhibition.
  • To develop and evaluate an Elastic Net regression model for predicting protein target inhibition using docking simulation data.

Main Methods:

  • Utilized the Scikit-Learn implementation of Elastic Net regression.
  • Employed the SAnDReS 2.0 program for regression model development.
  • Applied Elastic Net regression to atomic coordinates from docking simulations to predict enzyme inhibition.
  • Explored the concept of scoring functions and their implementation with Elastic Net.

Main Results:

  • Developed an Elastic Net regression model to calculate protein target inhibition.
  • Demonstrated superior predictive performance of the Elastic Net regression model compared to a classical scoring function.
  • Successfully predicted the inhibition of cyclin-dependent kinase 2.

Conclusions:

  • Elastic Net regression offers a robust approach for modeling protein-drug interactions and predicting enzyme inhibition.
  • The developed Elastic Net model shows enhanced predictive accuracy over conventional scoring functions.
  • Open-source tools like SAnDReS 2.0 and Scikit-Learn facilitate the application of advanced regression techniques in computational drug discovery.