Accuracy comparison of several common implicit solvent models and their implementations in the context of protein-ligand binding

Abstract

In this study several commonly used implicit solvent models are compared with respect to their accuracy of estimating solvation energies of small molecules and proteins, as well as desolvation penalty in protein-ligand binding. The test set consists of 19 small proteins, 104 small molecules, and 15 protein-ligand complexes. We compared predicted hydration energies of small molecules with their experimental values; the results of the solvation and desolvation energy calculations for small molecules, proteins and protein-ligand complexes in water were also compared with Thermodynamic Integration calculations based on TIP3P water model and Amber12 force field. The following implicit solvent (water) models considered here are: PCM (Polarized Continuum Model implemented in DISOLV and MCBHSOLV programs), GB (Generalized Born method implemented in DISOLV program, S-GB, and GBNSR6 stand-alone version), COSMO (COnductor-like Screening Model implemented in the DISOLV program and the MOPAC package) and the Poisson-Boltzmann model (implemented in the APBS program). Different parameterizations of the molecules were examined: we compared MMFF94 force field, Amber12 force field and the quantum-chemical semi-empirical PM7 method implemented in the MOPAC package. For small molecules, all of the implicit solvent models tested here yield high correlation coefficients (0.87–0.93) between the calculated solvation energies and the experimental values of hydration energies. For small molecules high correlation (0.82–0.97) with the explicit solvent energies is seen as well. On the other hand, estimated protein solvation energies and protein-ligand binding desolvation energies show substantial discrepancy (up to 10 kcal/mol) with the explicit solvent reference. The correlation of polar protein solvation energies and protein-ligand desolvation energies with the corresponding explicit solvent results is 0.65–0.99 and 0.76–0.96 respectively, though this difference in correlations is caused more by different parameterization and less by methods and indicates the need for further improvement of implicit solvent models parameterization. Within the same parameterization, various implicit methods give practically the same correlation with results obtained in explicit solvent model for ligands and proteins: e.g. correlation values of polar ligand solvation energies and the corresponding energies in the frame of explicit solvent were 0.953–0.966 for the APBS program, the GBNSR6 program and all models used in the DISOLV program. The DISOLV program proved to be on a par with the other used programs in the case of proteins and ligands solvation energy calculation. However, the solution of the Poisson-Boltzmann equation (APBS program) and Generalized Born method (implemented in the GBNSR6 program) proved to be the most accurate in calculating the desolvation energies of complexes.

Water models for biomolecular simulations

“…For nonspherical biomolecular shapes, ASC can be found numerically. Several computationally efficient adaptations/approximations of the general ASC formalism, notably many variants of Polarizable Continuum Model (PCM), e.g., COSMO, are widely used in computational chemistry to model solvation effects, see Ref for a comprehensive review. Some of these approximations are conceptually equivalent to the GB model …”

Evaluation of the novel algorithm of flexible ligand docking with moveable target-protein atoms

“…It is demonstrated here that even limited mobility of protein atoms results in considerable improvement of the docking positioning accuracy. Whilst the present results were obtained for the MMFF94 force field [44] in vacuum, the performance of SOL-P allows including in the docking procedure one of either rigorous (PCM or COSMO) or heuristic (Generalized Born) solvent models [45]. The ability to perform docking with the PCM solvent model has been already demonstrated by the FLM program which demands more computing resources [5].…”

Properties and Molecular docking of Antiviral to COVID-19 Chloroquine combining DFT calculations with SQMFF approach

“…Then, the R(+) form was modelled with the GaussView program [54] and, after that, both species were optimized in gas phase and aqueous solution by using the functional hybrid B3LYP/6-311++G** level of theory with the Revision A.02 of Gaussian 09 program [55]. Universal solvation and IEFPCM methods consider the solvent effects and, they were used to optimize the two forms in aqueous solution [35][36][37][56][57][58]. The variations of volumes that both forms experiment in solution were computed at the same level of theory with the Moldraw program [59].…”