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Toward Accurate pH-Dependent Binding Constant Predictions Using Molecular Docking and Constant-pH MD Calculations.

Mohannad J Yousef1, Nuno F B Oliveira1, João N M Vitorino1

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Investigating pH effects on donepezil binding to acetylcholinesterase (AChE) revealed computational methods predict improved binding at lower pH. Experimental data, however, showed decreased affinity, highlighting limitations in current computational approaches for protein-drug interactions.

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Area of Science:

  • Biochemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Protein structure and function are sensitive to pH-dependent protonation states.
  • Enzyme activity and ligand binding can be significantly altered by changes in pH.
  • Understanding pH effects is crucial for drug design and predicting drug efficacy.

Purpose of the Study:

  • To evaluate the impact of pH on the binding affinity of donepezil to acetylcholinesterase (AChE).
  • To compare computational predictions with experimental data regarding pH-dependent binding.
  • To identify limitations in computational methods for modeling pH effects in protein-drug interactions.

Main Methods:

  • Molecular docking weighted by constant-pH molecular dynamics (MD) simulations.
  • Molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) calculations.
  • Isothermal titration calorimetry (ITC) for experimental validation.

Main Results:

  • Computational methods predicted enhanced donepezil binding to AChE at lower pH (increased drug protonation).
  • Experimental data (ITC) showed a loss of binding affinity at pH 6.0, contradicting computational trends.
  • Discrepancies suggest uncaptured factors like conformational changes or entropic effects.

Conclusions:

  • Current computational approaches may not fully capture the complex pH-dependent modulation of protein-drug binding.
  • Further refinement of computational methods is needed to accurately predict pH effects on ligand-protein interactions.
  • Experimental validation remains essential to uncover all factors influencing binding affinity in biological systems.