Junmei Wang

We describe here a general Amber force field (GAFF) for organic molecules. GAFF is designed to be compatible with existing Amber force fields for proteins and nucleic acids, and has parameters for most organic and pharmaceutical molecules that are composed of H, C, N, O, S, P, and halogens. It uses a simple functional form and a limited number of atom types, but incorporates both empirical and heuristic models to estimate force constants and partial atomic charges. The performance of GAFF in test cases is encouraging. In test I, 74 crystallographic structures were compared to GAFF minimized structures, with a root-mean-square displacement of 0.26 A, which is comparable to that of the Tripos 5.2 force field (0.25 A) and better than those of MMFF 94 and CHARMm (0.47 and 0.44 A, respectively). In test II, gas phase minimizations were performed on 22 nucleic acid base pairs, and the minimized structures and intermolecular energies were compared to MP2/6-31G* results. The RMS of displacements and relative energies were 0.25 A and 1.2 kcal/mol, respectively. These data are comparable to results from Parm99/RESP (0.16 A and 1.18 kcal/mol, respectively), which were parameterized to these base pairs. Test III looked at the relative energies of 71 conformational pairs that were used in development of the Parm99 force field. The RMS error in relative energies (compared to experiment) is about 0.5 kcal/mol. GAFF can be applied to wide range of molecules in an automatic fashion, making it suitable for rational drug design and database searching.

Molecular mechanics models have been applied extensively to study the dynamics of proteins and nucleic acids. Here we report the development of a third-generation point-charge all-atom force field for proteins. Following the earlier approach of Cornell et al., the charge set was obtained by fitting to the electrostatic potentials of dipeptides calculated using B3LYP/cc-pVTZ//HF/6-31G** quantum mechanical methods. The main-chain torsion parameters were obtained by fitting to the energy profiles of Ace-Ala-Nme and Ace-Gly-Nme di-peptides calculated using MP2/cc-pVTZ//HF/6-31G** quantum mechanical methods. All other parameters were taken from the existing AMBER data base. The major departure from previous force fields is that all quantum mechanical calculations were done in the condensed phase with continuum solvent models and an effective dielectric constant of epsilon = 4. We anticipate that this force field parameter set will address certain critical short comings of previous force fields in condensed-phase simulations of proteins. Initial tests on peptides demonstrated a high-degree of similarity between the calculated and the statistically measured Ramanchandran maps for both Ace-Gly-Nme and Ace-Ala-Nme di-peptides. Some highlights of our results include (1) well-preserved balance between the extended and helical region distributions, and (2) favorable type-II poly-proline helical region in agreement with recent experiments. Backward compatibility between the new and Cornell et al. charge sets, as judged by overall agreement between dipole moments, allows a smooth transition to the new force field in the area of ligand-binding calculations. Test simulations on a large set of proteins are also discussed.

In this study, we present conformational energies for a molecular mechanical model (Parm99) developed for organic and biological molecules using the restrained electrostatic potential (RESP) approach to derive the partial charges. This approach uses the simple “generic” force field model (Parm94), and attempts to add a minimal number of extra Fourier components to the torsional energies, but doing so only when there is a physical justification. The results are quite encouraging, not only for the 34-molecule set that has been studied by both the highest level ab initiomodel (GVB/LMP2) and experiment, but also for the 55-molecule set for which high-quality experimental data are available. Considering the 55 molecules studied by all the force field models for which there are experimental data, the average absolute errors (AAEs) are 0.28 (this model), 0.52 (MM3), 0.57 (CHARMm [MSI]), and 0.43 kcal/mol (MMFF). For the 34-molecule set, the AAEs of this model versus experiment and ab initio are 0.28 and 0.27 kcal/mol, respectively. This is a lower error than found with MM3 and CHARMm, and is comparable to that found with MMFF (0.31 and 0.22 kcal/mol). We also present two examples of how well the torsional parameters are transferred from the training set to the test set. The absolute errors of molecules in the test set are only slightly larger than in the training set (differences of <0.1 kcal/mol). Therefore, it can be concluded that a simple “generic” force field with a limited number of specific torsional parameters can describe intra- and intermolecular interactions, although all comparison molecules were selected from our 82-compound training set. We also show how this effective two-body model can be extended for use with a nonadditive force field (NAFF), both with and without lone pairs. Without changing the torsional parameters, the use of more accurate charges and polarization leads to an increase in average absolute error compared with experiment, but adjustment of the parameters restores the level of agreement found with the additive model. After reoptimizing the Ψ, Φ torsional parameters in peptides using alanine dipeptide (6 conformational pairs) and alanine tetrapeptide (11 conformational pairs), the new model gives better energies than the Cornell et al. ( J Am Chem Soc 1995, 117, 5179–5197) force field. The average absolute error of this model for high-level ab initio calculation is 0.82 kcal/mol for alanine dipeptide and tetrapeptide as compared with 1.80 kcal/mol for the Cornell et al. model. For nucleosides, the new model also gives improved energies compared with the Cornell et al. model. To optimize force field parameters, we developed a program called parmscan, which can iteratively scan the torsional parameters in a systematic manner and finally obtain the best torsional potentials. Besides the organic molecules in our test set, parmscan was also successful in optimizing the Ψ, Φ torsional parameters in peptides to significantly improve agreement between molecular mechanical and high-level ab initio energies. © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 1049–1074, 2000

The Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) methods calculate binding free energies for macromolecules by combining molecular mechanics calculations and continuum solvation models. To systematically evaluate the performance of these methods, we report here an extensive study of 59 ligands interacting with six different proteins. First, we explored the effects of the length of the molecular dynamics (MD) simulation, ranging from 400 to 4800 ps, and the solute dielectric constant (1, 2, or 4) on the binding free energies predicted by MM/PBSA. The following three important conclusions could be observed: (1) MD simulation length has an obvious impact on the predictions, and longer MD simulation is not always necessary to achieve better predictions. (2) The predictions are quite sensitive to the solute dielectric constant, and this parameter should be carefully determined according to the characteristics of the protein/ligand binding interface. (3) Conformational entropy often show large fluctuations in MD trajectories, and a large number of snapshots are necessary to achieve stable predictions. Next, we evaluated the accuracy of the binding free energies calculated by three Generalized Born (GB) models. We found that the GB model developed by Onufriev and Case was the most successful model in ranking the binding affinities of the studied inhibitors. Finally, we evaluated the performance of MM/GBSA and MM/PBSA in predicting binding free energies. Our results showed that MM/PBSA performed better in calculating absolute, but not necessarily relative, binding free energies than MM/GBSA. Considering its computational efficiency, MM/GBSA can serve as a powerful tool in drug design, where correct ranking of inhibitors is often emphasized.