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

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.