To address the problem of specificity in G-protein coupled receptor (GPCR) drug discovery, there has been tremendous recent interest in allosteric drugs that bind at sites topographically distinct from the orthosteric site. Unfortunately, structure-based drug design of allosteric GPCR ligands has been frustrated by the paucity of structural data for allosteric binding sites, making a strong case for predictive computational methods. In this work, we map the surfaces of the beta1 (beta1AR) and beta2 (beta2AR) adrenergic receptor structures to detect a series of five potentially druggable allosteric sites. We employ the FTMAP algorithm to identify 'hot spots' with affinity for a variety of organic probe molecules corresponding to drug fragments. Our work is distinguished by an ensemble-based approach, whereby we map diverse receptor conformations taken from molecular dynamics (MD) simulations totaling approximately 0.5 micros. Our results reveal distinct pockets formed at both solvent-exposed and lipid-exposed cavities, which we interpret in light of experimental data and which may constitute novel targets for GPCR drug discovery. This mapping data can now serve to drive a combination of fragment-based and virtual screening approaches for the discovery of small molecules that bind at these sites and which may offer highly selective therapies.

Molecular dynamics (MD) simulation is a well-established method for understanding protein dynamics. Conformations from unrestrained MD simulations have yet to be assessed for blind virtual screening (VS) by docking. This study presents a critical analysis of the predictive power of MD snapshots to this regard, evaluating two well-characterized systems of varying flexibility in ligand-bound and unbound configurations. Results from such VS predictions are discussed with respect to experimentally determined structures. In all cases, MD simulations provide snapshots that improve VS predictive power over known crystal structures, possibly due to sampling more relevant receptor conformations. Additionally, MD can move conformations previously not amenable to docking into the predictive range.

The biased agonism of the G protein-coupled receptors (GPCRs), where in addition to a traditional G protein-signaling pathway a GPCR promotes intracellular signals though β-arrestin, is a novel paradigm in pharmacology. Biochemical and biophysical studies have suggested that a GPCR forms a distinct ensemble of conformations signaling through the G protein and β-arrestin. Here we report on the dynamics of the β2 adrenergic receptor bound to the β-arrestin and G protein-biased agonists and the empty receptor to further characterize the receptor conformational changes caused by biased agonists. We use conventional and accelerated molecular dynamics (aMD) simulations to explore the conformational transitions of the GPCR from the active state to the inactive state. We found that aMD simulations enable monitoring of the transition within the nanosecond time scale while capturing the known microscopic characteristics of the inactive states, such as the ionic lock, the inward position of F6.44, and water clusters. Distinct conformational states are shown to be stabilized by each biased agonist. In particular, in simulations of the receptor with the β-arrestin-biased agonist N-cyclopentylbutanepherine, we observe a different pattern of motions in helix 7 when compared to simulations with the G protein-biased agonist salbutamol that involves perturbations of the network of interactions within the NPxxY motif. Understanding the network of interactions induced by biased ligands and the subsequent receptor conformational shifts will lead to development of more efficient drugs.

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) are potent anti-HIV chemotherapeutics. Although there are FDA-approved NNRTIs, challenges such as the development of resistance have limited their utility. Here, we describe the identification of novel NNRTIs through a combination of computational and experimental approaches. Based on the known plasticity of the NNRTI binding pocket (NNIBP), we adopted an ensemble-based virtual screening strategy: coupling receptor conformations from 10 X-ray crystal structures with 120 snapshots from a total of 480 ns of molecular dynamics (MD) trajectories. A screening library of 2864 National Cancer Institute (NCI) compounds was built and docked against the ensembles in a hierarchical fashion. Sixteen diverse compounds were tested for their ability to block HIV infection in human tissue cultures using a luciferase-based reporter assay. Three promising compounds were further characterized, using a HIV-1 RT-based polymerase assay, to determine the specific mechanism of inhibition. We found that 2 of the three compounds inhibited the polymerase activity of RT (with potency similar to the positive control, the FDA-approved drug nevirapine). Through a computational approach, we were able to discover two compounds which inhibit HIV replication and block the activity of RT, thus offering the potential for optimization into mature inhibitors.