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Related Concept Videos

Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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The Equilibrium Binding Constant and Binding Strength02:18

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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Related Experiment Video

Updated: Jun 11, 2025

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
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An ANI-2 enabled open-source protocol to estimate ligand strain after docking.

Francois Berenger1, Koji Tsuda1

  • 1Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.

Journal of Computational Chemistry
|October 5, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational protocol to improve protein-ligand docking accuracy by estimating ligand strain using quantum mechanics and Monte Carlo methods. This approach can significantly enhance hit rates in virtual screening campaigns.

Keywords:
ANI‐2xLIT‐PCBAMCQMSBVSdockingligandstrain

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Protein-ligand docking scores are often imprecise due to difficulties in calculating ligand internal energy with molecular mechanics force fields.
  • This imprecision introduces noise, negatively impacting the accurate ranking of potential drug candidates.

Purpose of the Study:

  • To develop and evaluate an open-source protocol for estimating ligand strain energy post-docking.
  • To improve the accuracy of virtual screening by filtering docked ligands based on their estimated strain.

Main Methods:

  • The protocol employs two quantum mechanics (QM) single-point energy calculations sandwiching a Monte Carlo (MC) based ligand minimization in dihedral space.
  • The MC simulation utilizes the ANI-2x force field, an approximation of QM, for efficient strain estimation.
  • A structure-based virtual screening campaign was conducted on nine protein targets using the CCDC genetic optimization for ligand docking (GOLD) software.

Main Results:

  • The proposed strain estimation protocol, when applied after docking, demonstrated the ability to significantly improve hit rates for certain protein targets.
  • Analysis of a virtual screening campaign showed that the method's effectiveness is dependent on the specific protein target and the availability of known active/inactive molecules.
  • The protocol does not universally guarantee improved hit rates, highlighting the need for target-specific evaluation.

Conclusions:

  • The developed open-source protocol offers a promising approach to refine protein-ligand docking by incorporating ligand strain estimation.
  • Strain filtering can enhance virtual screening efficiency, but its success is contingent on the target system and data availability.
  • Further evaluation is recommended to determine the protocol's utility for specific drug discovery projects.