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Accurately predicting solute permeation across lipid bilayers is crucial for drug design. Molecular dynamics simulations combined with the inhomogeneous solubility-diffusion model provide reliable estimates for membrane permeability coefficients (Pm).

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Area of Science:

  • Computational chemistry
  • Biophysics
  • Pharmacology

Background:

  • Predicting solute permeation across lipid bilayers is vital for drug design, toxicology, and understanding cellular signaling.
  • Molecular dynamics simulations coupled with the inhomogeneous solubility-diffusion model are key tools for estimating these permeation rates.

Purpose of the Study:

  • To assess the efficiency and accuracy of various methods for calculating the potential of mean force (PMF) and position-dependent diffusivity.
  • To compare the performance of different simulation techniques in predicting the membrane permeability (Pm) of small molecules across a DMPC bilayer.

Main Methods:

  • Umbrella sampling, replica exchange umbrella sampling, adaptive biasing force, and multiple-walker adaptive biasing force were employed for PMF calculations.
  • Bayesian inference and generalized Langevin methods were compared for calculating solute diffusivity.
  • Simulations were performed for urea, benzoic acid, and codeine across a model DMPC lipid bilayer.

Main Results:

  • No single PMF calculation method showed a definitive advantage in predicting membrane permeability (Pm) when sufficient equilibration was achieved.
  • Both Bayesian inference and generalized Langevin methods for diffusivity were sensitive to parameter choices and membrane defect relaxation.
  • Computed Pm values showed agreement within 1.5 log units with experimental data for all solutes and methods.

Conclusions:

  • Accurate membrane permeability prediction is achievable using molecular dynamics simulations and the inhomogeneous solubility-diffusion model.
  • Discrepancies with experimental data may stem from force field limitations and slow collective lipid dynamics.
  • Reliable Pm estimation is possible even with limited simulation data points, offering practical recommendations for future studies.