The parametrization and validation of the OPLS3 force field for small molecules and proteins are reported. Enhancements with respect to the previous version (OPLS2.1) include the addition of off-atom charge sites to represent halogen bonding and aryl nitrogen lone pairs as well as a complete refit of peptide dihedral parameters to better model the native structure of proteins. To adequately cover medicinal chemical space, OPLS3 employs over an order of magnitude more reference data and associated parameter types relative to other commonly used small molecule force fields (e.g., MMFF and OPLS_2005). As a consequence, OPLS3 achieves a high level of accuracy across performance benchmarks that assess small molecule conformational propensities and solvation. The newly fitted peptide dihedrals lead to significant improvements in the representation of secondary structure elements in simulated peptides and native structure stability over a number of proteins. Together, the improvements made to both the small molecule and protein force field lead to a high level of accuracy in predicting protein-ligand binding measured over a wide range of targets and ligands (less than 1 kcal/mol RMS error) representing a 30% improvement over earlier variants of the OPLS force field.

Designing tight binding ligands is a primary objective of small molecule drug discovery.Over the past few decades, free energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low cost parallel computing.However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (~5X in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations.Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized based on other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.3

http://ub.cbm.uam.es/mammoth/mult.

SUMMARY The function of G-protein coupled receptors is tightly modulated by the lipid environment. Long timescale molecular dynamics simulations (totaling ~3 microsec) of the A2A receptor in cholesterol-free bilayers, with and without the antagonist ZM241385 bound, demonstrate an instability of helix II in the apo receptor in cholesterol-poor membrane regions. We directly observe that the effect of cholesterol binding is to stabilize helix II against a buckling type deformation, perhaps rationalizing the observation that the A2A receptor couples to G-protein only in the presence of cholesterol (Zezula and Freissmuth, 2008). The results suggest a mechanism by which the A2A receptor may function as a coincidence detector, activating only in the presence of both cholesterol and agonist. We also observed a previously hypothesized conformation of the tryptophan “rotameric switch” on helix VI in which a phenylalanine on helix V positions the tryptophan out of the ligand binding pocket.