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The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

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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Updated: Jun 6, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Estimating binding affinities by docking/scoring methods using variable protonation states.

Min-Sun Park1, Cen Gao, Harry A Stern

  • 1Department of Biochemistry and Biophysics, University of Rochester, Rochester, New York 14642, USA.

Proteins
|November 9, 2010
PubMed
Summary
This summary is machine-generated.

Considering multiple protonation states of ligands and receptors improves protein-ligand recognition predictions. Ensembles of all states, not just the most probable, best correlate with experimental binding affinities.

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Area of Science:

  • Computational Chemistry
  • Structural Biology
  • Drug Discovery

Background:

  • Protein-ligand recognition is crucial for biological processes and drug development.
  • The protonation state of titratable groups can significantly influence molecular interactions.
  • Accurate prediction of binding affinities remains a challenge in computational drug design.

Purpose of the Study:

  • To evaluate the impact of considering multiple protonation states on protein-ligand recognition accuracy.
  • To compare different computational protocols for assessing protein-ligand binding affinities.
  • To determine the optimal strategy for incorporating protonation states in molecular docking.

Main Methods:

  • Generated alternative protonation and tautomer states for ligands and receptors based on predicted pK(a) values and proximity to the binding site.
  • Performed independent docking calculations for each generated state.
  • Evaluated three protocols: ensemble of all states, most probable state, and best scoring state, using a dataset of 176 protein-ligand complexes with experimental data.

Main Results:

  • Using ligand poses from experimental crystal structures yielded the best agreement with experimental binding affinities.
  • For 9 out of 15 receptors, employing an ensemble of all generated protonation states for both ligand and receptor showed the strongest correlation between calculated and measured affinities.
  • The ensemble approach generally outperformed protocols relying on single, most probable, or best-scoring states.

Conclusions:

  • Accounting for multiple protonation states is essential for improving the accuracy of protein-ligand recognition predictions.
  • An ensemble-based approach considering all relevant protonation states offers a more robust method for predicting binding affinities.
  • This strategy enhances the reliability of computational methods in drug discovery and molecular modeling.