Prediction of octanol-water partition coefficients for the SAMPL6- log ⁡ P molecules using molecular dynamics simulations with OPLS-AA, AMBER and CHARMM force fields

Abstract

All-atom molecular dynamics simulations with stratified alchemical free energy calculations were used to predict the octanol-water partition coefficient $\log P_{ow}$ of eleven small molecules as part of the SAMPL6-$\log P$ blind prediction challenge using four different force field parametrizations: standard OPLS-AA with transferable charges, OPLS-AA with non-transferable CM1A charges, AMBER/GAFF, and CHARMM/CGenFF. Octanol parameters for OPLS-AA, GAFF and CHARMM were validated by comparing the density as a function of temperature, the chemical potential, and the hydration free energy to experimental values. The partition coefficients were calculated from the solvation free energy for the compounds in water and pure ("dry") octanol or "wet" octanol with 27 mol% water dissolved. Absolute solvation free energies were computed by thermodynamic integration (TI) and the multistate Bennett acceptance ratio with uncorrelated samples from data generated by an established protocol using 5-ns windowed alchemical free energy perturbation (FEP) calculations with the Gromacs molecular dynamics package. Equilibration of sets of FEP simulations was quantified by a new measure of convergence based on the analysis of forward and time-reversed trajectories. The accuracy of the $\log P_{ow}$ predictions was assessed by descriptive statistical measures such as the root mean square error (RMSE) of the data set compared to the experimental values. Discarding the first 1 ns of each 5-ns window as an equilibration phase had a large effect on the GAFF data, where it improved the RMSE by up to 0.8 log units, while the effect for other data sets was smaller or marginally worsened the agreement. Overall, CGenFF gave the best prediction with RMSE 1.2 log units, although for only eight molecules because the current CGenFF workflow for Gromacs does not generate files for certain halogen-containing compounds. Over all eleven compounds, GAFF gave an RMSE of 1.5. The effect of using a mixed water/octanol solvent slightly decreased the accuracy for CGenFF and GAFF and slightly increased it for OPLS-AA. The GAFF and OPLS-AA results displayed a systematic error where molecules were too hydrophobic whereas CGenFF appeared to be more balanced, at least on this small data set.

Keywords: AMBER force field; CHARMM force field; Free energy perturbation; Ligand parametrization; Molecular dynamics; OPLS-AA force field; Octanol-water partition coefficient; Solvation free energy.

Fig. 1:

Chemical structures of the SAMPL6-logP data set with the selected microstates.

Fig. 2:

Dependence of the density of dry and wet octanol on the temperature. Green squares are experimental data; blue triangles were computed from 100-ns MD simulations with dry octanol; orange diamonds were computed from 100-ns MD simulations with wet octanol

Fig. 3:

$\langle \frac{\partial \mathcal{H}}{\partial \lambda }\rangle$ time-reversed and time-forward convergence plots for SM14. The calculated convergence time fraction R_c was 0.05, indicating a well equilibrated window. b The calculated R_c was 0.89, indicating that this window was not well equilibrated.

Fig. 4:

Cumulative distribution functions $\mathcal{C}\left(R_C\right)$ of the convergence time fraction R_c for Coulomb and VDW λ windows from all data (0–5 ns) (dashed lines) and data with the first 1 ns discarded as equilibration (1–5 ns) (solid lines) for SM14 (GAFF).

Fig. 5:

Correlation between experimental and computed octanol-water coefficients log P_ow for simulations performed with dry or wet octanol with OPLS-AA (mol2ff) parameters. The gray band indicates ±1 log-units from ideal correlation, shown by the dashed line. The root mean square error (RMSE), the absolute unsigned error (AUE), and the (signed) mean error (ME) are indicated. Error bars represent the error in the experiments or the error on the mean, derived from the simulations.

Fig. 6:

Correlation between experimental and computed octanol-water coefficients log P_ow for simulations performed with dry or wet octanol with OPLS-AA (LigParGen) parameters.

Fig. 7:

Correlation between experimental and computed octanol-water coefficients log P_ow for simulations performed with dry or wet octanol with GAFF parameters.

Fig. 8:

Correlation between experimental and computed octanol-water coefficients log P_ow for simulations performed with dry or wet octanol with CGenFF parameters.

Fig. 9:

Chemical structures of the three microstates of compound SM08 evaluated in this study.