The charges and Lennard-Jones parameters of the TIP3P water potential have been modified to improve its performance under the common condition for molecular dynamics simulations of using Ewald summation in lieu of relatively short nonbonded truncation schemes. These parameters were optimized under the condition that the hydrogen atoms do not have Lennard-Jones parameters, thus making the model independent of the combining rules used for the calculation of nonbonded, heteroatomic interaction energies, and limiting the number of Lennard-Jones calculations required. Under these conditions, this model provides accurate density (rho = 0.997 g/ml) and heat of vaporization (DeltaH(vap) = 10.53 kcal/mol) at 25 degrees C and 1 atm, but also provides improved structure in the second peak of the O-O radial distribution function and improved values for the dielectric constant (epsilon(0) = 89) and the diffusion coefficient (D = 4.0 x 10(-5) cm(2)/s) relative to the original parametrization. Like the original parameterization, however, this model does not show a temperature density maximum. Several similar models are considered with the additional constraint of trying to match the performance of the optimized potentials for liquid simulation atom force field to that obtained when using the simulation conditions under which it was originally designed, but no model was entirely satisfactory in reproducing the relative difference in free energies of hydration between the model compounds, phenol and benzene. Finally, a model that incorporates a long-range correction for truncated Lennard-Jones interactions is presented, which provides a very accurate dielectric constant (epsilon(0) = 76), however, the improvement in this estimate is on the same order as the uncertainty in the calculation.

An assessment of nine scoring functions commonly applied in docking using a set of 189 protein-ligand complexes is presented. The scoring functions include the CHARMm potential, the scoring function DrugScore, the scoring function used in AutoDock, the three scoring functions implemented in DOCK, as well as three scoring functions implemented in the CScore module in SYBYL (PMF, Gold, ChemScore). We evaluated the abilities of these scoring functions to recognize near-native configurations among a set of decoys and to rank binding affinities. Binding site decoys were generated by molecular dynamics with restraints. To investigate whether the scoring functions can also be applied for binding site detection, decoys on the protein surface were generated. The influence of the assignment of protonation states was probed by either assigning "standard" protonation states to binding site residues or adjusting protonation states according to experimental evidence. The role of solvation models in conjunction with CHARMm was explored in detail. These include a distance-dependent dielectric function, a generalized Born model, and the Poisson equation. We evaluated the effect of using a rigid receptor on the outcome of docking by generating all-pairs decoys ("cross-decoys") for six trypsin and seven HIV-1 protease complexes. The scoring functions perform well to discriminate near-native from misdocked conformations, with CHARMm, DOCK-energy, DrugScore, ChemScore, and AutoDock yielding recognition rates of around 80%. Significant degradation in performance is observed in going from decoy to cross-decoy recognition for CHARMm in the case of HIV-1 protease, whereas DrugScore and ChemScore, as well as CHARMm in the case of trypsin, show only small deterioration. In contrast, the prediction of binding affinities remains problematic for all of the scoring functions. ChemScore gives the highest correlation value with R(2) = 0.51 for the set of 189 complexes and R(2) = 0.43 for the set of 116 complexes that does not contain any of the complexes used to calibrate this scoring function. Neither a more accurate treatment of solvation nor a more sophisticated charge model for zinc improves the quality of the results. Improved modeling of the protonation states, however, leads to a better prediction of binding affinities in the case of the generalized Born and the Poisson continuum models used in conjunction with the CHARMm force field.

We introduce a toolset of program libraries collectively titled MATCH (Multipurpose Atom-Typer for CHARMM) for the automated assignment of atom types and force field parameters for molecular mechanics simulation of organic molecules. The toolset includes utilities for the conversion from multiple chemical structure file formats into a molecular graph. A general chemical pattern-matching engine using this graph has been implemented whereby assignment of molecular mechanics atom types, charges and force field parameters is achieved by comparison against a customizable list of chemical fragments. While initially designed to complement the CHARMM simulation package and force fields by generating the necessary input topology and atom-type data files, MATCH can be expanded to any force field and program, and has core functionality that makes it extendable to other applications such as fragment-based property prediction. In the present work, we demonstrate the accurate construction of atomic parameters of molecules within each force field included in CHARMM36 through exhaustive cross validation studies illustrating that bond increment rules derived from one force field can be transferred to another. In addition, using leave-one-out substitution it is shown that it is also possible to substitute missing intra and intermolecular parameters with ones included in a force field to complete the parameterization of novel molecules. Finally, to demonstrate the robustness of MATCH and the coverage of chemical space offered by the recent CHARMM CGENFF force field (Vanommeslaeghe, et al., JCC., 2010, 31, 671–690), one million molecules from the PubChem database of small molecules are typed, parameterized and minimized.