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Benchmarking of Force Fields for Molecule-Membrane Interactions.

Markéta Paloncýová1, Gabin Fabre1,2, Russell H DeVane3

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Evaluating drug-membrane interactions is crucial for drug delivery. Slipids force field showed the best performance in molecular dynamics simulations, offering accurate predictions for drug partitioning coefficients.

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Area of Science:

  • Computational chemistry and biophysics
  • Pharmacology and drug delivery

Background:

  • Drug-membrane interactions are vital for understanding drug delivery, accumulation, and metabolism.
  • Molecular dynamics (MD) simulations offer insights into these interactions, but their accuracy depends heavily on the chosen force field.
  • Accurate prediction of drug partitioning coefficients (log K) is essential for evaluating drug behavior.

Purpose of the Study:

  • To evaluate the performance of five different molecular dynamics force fields (Berger, Slipids, CHARMM36, GAFFlipids, GROMOS 43A1-S3) and the COSMOmic tool for predicting drug-membrane interactions.
  • To determine the most suitable computational methods for calculating free energy profiles and partition coefficients of molecules across a model dimyristoylphosphatidylcholine (DMPC) membrane.
  • To provide recommendations for selecting appropriate computational tools based on specific research needs, such as high-throughput screening or studies involving hydrophilic molecules.

Main Methods:

  • Calculation of free energy profiles for 11 molecules traversing a DMPC membrane bilayer using five distinct all-atom force fields.
  • Utilized the semi-implicit solvent model COSMOmic for comparative analysis.
  • Compared calculated partition coefficients (log K) against experimental data to assess accuracy.

Main Results:

  • High correlation was observed between theoretical and experimental partition coefficients (log K).
  • All-atom force fields (Slipids, CHARMM36, GAFFlipids) and COSMOmic showed partition coefficients within 0.75 log units of experimental values.
  • The Slipids force field demonstrated the highest accuracy among the evaluated methods for predicting drug partitioning.

Conclusions:

  • For high-throughput thermodynamic evaluations (e.g., log K), COSMOmic is recommended due to its low computational cost.
  • CHARMM36 is suggested for studies focusing on hydrophilic molecules.
  • Slipids is identified as the preferred force field for complex drug-membrane interaction studies, balancing accuracy and computational considerations.