Partition coefficients describe how a solute is distributed between two immiscible solvents. They are used in drug design as a measure of a solute’s hydrophobicity and a proxy for its membrane permeability. We calculate partition coefficients from transfer free energies using molecular dynamics simulations in explicit solvent. Setup is done by our new Solvation Toolkit which automates the process of creating input files for any combination of solutes and solvents for many popular molecular dynamics software packages. We calculate partition coefficients between octanol/water and cyclohexane/water with the Generalized AMBER Force Field (GAFF) and the Dielectric Corrected GAFF (GAFF-DC). With similar methods in the past we found a root-mean-squared error (RMSE) of 6.3 kJ/mol in hydration free energies which would correspond to an error of around 1.6 log units in partition coefficients if solvation free energies in both solvents were estimated with comparable accuracy. Here we find an overall RMSE of about 1.2 log units with both force fields. Results from GAFF and GAFF-DC seem to exhibit systematic biases in opposite directions with GAFF and GAFF-DC for calculated cyclohexane/water partition coefficients.

An efficient methodology has been developed to quantify water energetics by analysis of explicit solvent molecular simulations of organic and biomolecular systems. The approach, grid cell theory (GCT), relies on a discretization of the cell theory methodology on a three-dimensional grid to spatially resolve the density, enthalpy, and entropy of water molecules in the vicinity of solute(s) of interest. Entropies of hydration are found to converge more efficiently than enthalpies of hydration. GCT predictions of free energies of hydration on a data set of small molecules are strongly correlated with thermodynamic integration predictions. Agreement with the experiment is comparable for both approaches. A key advantage of GCT is its ability to provide from a single simulation insightful graphical analyses of spatially resolved components of the enthalpies and entropies of hydration.

To compare ordered water positions from experiment with those from molecular dynamics (MD) simulations, a number of MD models of water structure in crystalline endoglucanase were calculated. The starting MD model was derived from a joint X-ray and neutron diffraction crystal structure, enabling the use of experimentally assigned protonation states. Simulations were performed in the crystalline state, using a periodic 2×2×2 supercell with explicit solvent. Water Xray and neutron scattering density maps were computed from MD trajectories using standard macromolecular crystallography methods. In one set of simulations, harmonic restraints were applied to bias the protein structure toward the crystal structure. For these simulations, the recall of crystallographic waters using strong peaks in the MD water electron density was very good, and there also was substantial visual agreement between the boomerang-like wings of the neutron scattering density and the crystalline water hydrogen positions. An unrestrained simulation also was performed. For this simulation, the recall of crystallographic waters was much lower. For both restrained and unrestrained simulations, the strongest water density peaks were associated with crystallographic waters. The results demonstrate that it is now possible to recover crystallographic water structure using restrained MD simulations, but that it is not yet reasonable to expect unrestrained MD simulations to do the same. Further development and generalization of MD water models for force field development, macromolecular crystallography, and medicinal chemistry

Protein-carbohydrate recognition is crucial in many vital biological processes including host-pathogen recognition, cell-signaling, and catalysis. Accordingly, computational prediction of protein-carbohydrate binding free energies is of enormous interest for drug design. However, the accuracy of current force fields (FFs) for predicting binding free energies of protein-carbohydrate complexes is not well understood owing to technical challenges such as the highly polar nature of the complexes, anomerization, and conformational flexibility of carbohydrates. The present study evaluated the performance of alchemical predictions of binding free energies with the GAFF1.7/AM1-BCC and GLYCAM06j force fields for modeling protein-carbohydrate complexes. Mean unsigned errors of 1.1 ± 0.06 (GLYCAM06j) and 2.6 ± 0.08 (GAFF1.7/AM1-BCC) kcal·mol(-1) are achieved for a large data set of monosaccharide ligands for Ralstonia solanacearum lectin (RSL). The level of accuracy provided by GLYCAM06j is sufficient to discriminate potent, moderate, and weak binders, a goal that has been difficult to achieve through other scoring approaches. Accordingly, the protocols presented here could find useful applications in carbohydrate-based drug and vaccine developments.