Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method

Abstract

Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson‐Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1‐BCC‐GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1‐BCC (Austin Model 1‐bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types, on, oi, hn1, hn2, hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein‐ligand binding free energies using the MM‐PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM‐PBSA method.

The SAMPL9 host–guest blind challenge: an overview of binding free energy predictive accuracy

“…For example, a method used a newly developed ABCG2 (AM1-BCC-GAFF2) method. 59 Another method (Alchemical Transfer Method (ATM) [60][61][62] ) utilized an in house development at Roivant Discovery, a force field parameter engine termed here as FFEngine. Although fixed charge force fields were common, one approach used a standard double decoupling method (DDM) with the polarizable atomic multiplole AMOEBA force field.…”

Development and test of highly accurate endpoint free energy methods. 3: partition coefficient prediction using a Poisson–Boltzmann method combined with a solvent accessible surface area model for SAMPL challenges

“…The same PB radius parameters derived using HFEs in our previous work 6,7 are directly applied in toluene and cyclohexane; therefore, the only parameters of toluene and cyclohexane that differ from those of water are g and b in eqn (4) in addition to the dielectric constant. The parameterization of g and b can be obtained directly by linear regression analysis (single data point per solute), but given the limited amount of data in organic solvents, we used the multi-conformation approach to conduct the linear regression process (multiple data points per solute).…”

“…We implemented this new PBSA strategy and obtained a root mean square error (RMSE) of 1.05 kcal mol −1 on the HFEs of 544 molecules. 6 Extending this method to the solvent n -octanol yielded a prediction error of RMSE = 0.91 log units on log P octanol/water calculations of 707 drug molecules in the ZINC database. 7 We called this transfer free energy-based log P method as FELogP.…”

“…5 Modeling the solvent effect as a whole may lead to overfitting and the unbalanced contributions of the two types of solvent effect. Therefore, recently we conducted a series of studies on the development of a highly accurate PBSA model for SFE prediction, 6,7 which is combined with the general AMBER force field 2 (GAFF2) and our recently developed ABCG2 charge model. 8 In this new PBSA model, we developed a set of atom radii for PB calculations targeting the electrostatic (polar) contribution from the thermodynamic integration (TI) Paper PCCP calculations of hydration free energy (HFE); then the nonelectrostatic (non-polar part) term was fitted targeting the experimental values of HFE or SFE.…”

“…Note that the PB atomic radii optimized from HFE were directly utilized for SFE calculations in organic solvents; by this way only the non-polar DG SASA model needs to be redeveloped for individual organic solvents. In this study, we essentially used the previously developed PB boundary definitions 6,8 and derived the solvent dependent parameters g and b for toluene and cyclohexane solvents. The parameterization of g and b targeted to fit experimental SFEs and using multiple conformations to avoid overfitting.…”