A head-to-head comparison of MM/PBSA and MM/GBSA in predicting binding affinities for the CB₁ cannabinoid ligands

The substantial increase in the number of active and inactive-state CB₁ receptor experimental structures has provided opportunities for CB₁ drug discovery using various structure-based drug design methods, including the popular end-point methods for predicting binding free energies-Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA). In this study, we have therefore evaluated the performance of MM/PBSA and MM/GBSA in calculating binding free energies for CB₁ receptor. Additionally, with both MM/PBSA and MM/GBSA being known for their highly individualized performance, we have evaluated the effects of various simulation parameters including the use of energy minimized structures, choice of solute dielectric constant, inclusion of entropy, and the effects of the five GB models. Generally, MM/GBSA provided higher correlations than MM/PBSA (r_MM/GBSA = 0.433 - 0.652 vs. r_MM/PBSA = 0.100 - 0.486) regardless of the simulation parameters, while also offering faster calculations. Improved correlations were observed with the use of molecular dynamics ensembles compared with energy minimized structures and larger solute dielectric constants. Incorporation of entropic terms led to unfavorable results for both MM/PBSA and MM/GBSA for a majority of the dataset, while the evaluation of the various GB models exerted a varying effect on both the datasets. The findings obtained in this study demonstrate the utility of MM/PBSA and MM/GBSA in predicting binding free energies for the CB₁ receptor, hence providing a useful benchmark for their applicability in the endocannabinoid system as well as other G protein-coupled receptors.

The study utilized the docked dataset (Induced Fit Docking with Glide XP scoring function) from Loo et al., consisting of 46 ligands-23 agonists and 23 antagonists. The equilibrated structures from Loo et al. were subjected to 30 ns production simulations using GROMACS 2018 at 300 K and 1 atm with the velocity rescaling thermostat and the Parinello-Rahman barostat. AMBER ff99SB*-ILDN was used for the proteins, General Amber Force Field (GAFF) was used for the ligands, and Slipids parameters were used for lipids. MM/PBSA and MM/GBSA binding free energies were then calculated using gmx_MMPBSA. The solute dielectric constant was varied between 1, 2, and 4 to study the effect of different solute dielectric constants on the performance of MM/PB(GB)SA. The effect of entropy on MM/PB(GB)SA binding free energies was evaluated using the interaction entropy module implemented in gmx_MMPBSA. Five GB models, GB^HCT, GB^OBC1, GB^OBC2, GB^Neck, and GB^Neck2, were evaluated to study the effect of the choice of GB models in the performance of MM/GBSA. Pearson correlation coefficients were used to measure the correlation between experimental and predicted binding free energies.