The rapid growth of structural information for G-protein-coupled receptors (GPCRs) has led to a greater understanding of their structure, function, selectivity, and ligand binding. Although novel ligands have been identified using methods such as virtual screening, computationally driven lead optimization has been possible only in isolated cases because of challenges associated with predicting binding free energies for related compounds. Here, we provide a systematic characterization of the performance of free-energy perturbation (FEP) calculations to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters. Using the FEP+ package, first we validated the protocol, which includes a full lipid bilayer and explicit solvent, by predicting the binding affinity for a total of 45 different ligands across four different GPCRs (adenosine A2AAR, β1 adrenergic, CXCR4 chemokine, and δ opioid receptors). Comparison with experimental binding affinity measurements revealed a highly predictive ranking correlation (average spearman ρ = 0.55) and low root-mean-square error (0.80 kcal/mol). Next, we applied FEP+ in a prospective project, where we predicted the affinity of novel, potent adenosine A2A receptor (A2AR) antagonists. Four novel compounds were synthesized and tested in a radioligand displacement assay, yielding affinity values in the nanomolar range. The affinity of two out of the four novel ligands (plus three previously reported compounds) was correctly predicted (within 1 kcal/mol), including one compound with approximately a tenfold increase in affinity compared to the starting compound. Detailed analyses of the simulations underlying the predictions provided insights into the structural basis for the two cases where the affinity was overpredicted. Taken together, these results establish a protocol for systematically applying FEP+ to GPCRs and provide guidelines for identifying potent molecules in drug discovery lead optimization projects.

We report the development of homology models of dopamine (D(2), D(3), and D(4)), serotonin (5-HT(1B), 5-HT(2A), 5-HT(2B), and 5-HT(2C)), histamine (H(1)), and muscarinic (M(1)) receptors, based on the high-resolution structure of the beta(2)-adrenergic receptor. The homology models were built and refined using Prime. We have addressed the required modeling of extracellular loop 2, which is often implicated in ligand binding. The orthosteric sites of the models were optimized using induced fit docking, to allow for side-chain flexibility, and the resulting receptor models have been evaluated using protein validation tools. Of the nine homology models developed, six models showed moderate to good enrichment in virtual screening experiments (5-HT(2A), 5-HT(1B), D(2), 5-HT(2C), D(3), and M(1)). The 5-HT(2A) receptor displayed the highest enrichment in virtual screening experiments with enrichment factors of 6.1, 6.9, and 5.9 at 2, 5, and 10%, respectively, of the screened database. However, three of the models require further refinement (5-HT(2B), D(4), and H(1)), due to difficulties in modeling some of the binding site residues as well as the extracellular loop 2. Our effort also aims to supplement the limited number of tested G protein-coupled receptor homology models based on the beta(2) crystal structure that are freely available to the research community.

To date all typical and atypical antipsychotics target the dopamine D(2) receptor. Clozapine represents the best-characterized atypical antipsychotic, although it displays only moderate (submicromolar) affinity for the dopamine D(2) receptor. Herein, we present the design, synthesis, and pharmacological evaluation of three series of homobivalent ligands of clozapine, differing in the length and nature of the spacer and the point of attachment to the pharmacophore. Attachment of the spacer at the N4' position of clozapine yielded a series of homobivalent ligands that displayed spacer-length-dependent gains in affinity and activity for the dopamine D(2) receptor. The 16 and 18 atom spacer bivalent ligands were the highlight compounds, displaying marked low nanomolar receptor binding affinity (1.41 and 1.35 nM, respectively) and functional activity (23 and 44 nM), which correspond to significant gains in affinity (75- and 79-fold) and activity (9- and 5-fold) relative to the original pharmacophore, clozapine. As such these ligands represent useful tools with which to investigate dopamine receptor dimerization and the atypical nature of clozapine.

Understanding the pharmacological similarity of G protein-coupled receptors (GPCRs) is paramount to predicting ligand off-target effects, drug repurposing, and ligand discovery for orphan receptors. Phylogenetic relationships do not always correctly capture pharmacological similarity. Previous family-wide attempts to define pharmacological relationships were based on three-dimensional structures and/or known receptor:ligand pairings, both unavailable for orphan GPCRs. Here, we present GPCR-CoINPocket, a novel contact-informed neighboring pocket metric of GPCR binding site similarity that is informed by patterns of ligand:residue interactions observed in crystallographically-characterized GPCRs. GPCR-CoINPocket is applicable to receptors with unknown structure or ligands and accurately captures known pharmacological relationships between GPCRs, even those undetected by phylogeny. When applied to orphan receptor GPR37L1, GPCR-CoINPocket identified its pharmacological neighbors, and transfer of their pharmacology aided discovery of the first surrogate ligands for this orphan with a 30% success rate. Although primarily designed for GPCRs, the method is easily transferable to other protein families.

Pharmaceuticals and industrial chemicals, both in the environment and in research settings, commonly interact with aquatic vertebrates. Due to their short life-cycles and the traits that can be generalized to other organisms, fish and amphibians are attractive models for the evaluation of toxicity caused by endocrine disrupting chemicals (EDCs) and adverse drug reactions. EDCs, such as pharmaceuticals or plasticizers, alter the normal function of the endocrine system and pose a significant hazard to human health and the environment. The selection of suitable animal models for toxicity testing is often reliant on high sequence identity between the human proteins and their animal orthologs. Herein, we compare in silico the ligand-binding sites of 28 human `side-effect' targets to their corresponding orthologs in Danio rerio, Pimephales promelas, Takifugu rubripes, Xenopus laevis, and Xenopus tropicalis, as well as sub-pockets involved in protein interactions with specific chemicals. We found that the ligand-binding pockets had much higher conservation than the full proteins, while the peroxisome proliferator-activated receptor γ and corticotropin-releasing factor receptor 1, were notable exceptions. Furthermore, we demonstrated that the conservation of sub-pockets may vary dramatically. Finally, we identified the aquatic model(s) with the highest binding site similarity, compared to the corresponding human toxicity target.

Endocrine disrupting chemicals (EDCs) pose a significant threat to human health, society, and the environment. Many EDCs elicit their toxic effects through nuclear hormone receptors, like the estrogen receptor α (ERα). In silico models can be used to prioritize chemicals for toxicological evaluation to reduce the amount of costly pharmacological testing and enable early alerts for newly designed compounds. However, many of the current computational models are overly dependent on the chemistry of known modulators and perform poorly for novel chemical scaffolds. Herein we describe the development of computational, three-dimensional multi-conformational pocket-field docking, and chemical-field docking models for the identification of novel EDCs that act via the ligand-binding domain of ERα. These models were highly accurate in the retrospective task of distinguishing known high-affinity ERα modulators from inactive or decoy molecules, with minimal training. To illustrate the utility of the models in prospective in silico compound screening, we screened a database of over 6000 environmental chemicals and evaluated the 24 top-ranked hits in an ERα transcriptional activation assay and a differential scanning fluorimetry-based ERα binding assay. Promisingly, six chemicals displayed ERα agonist activity (32nM-3.98μM) and two chemicals had moderately stabilizing effects on ERα. Two newly identified active compounds were chemically related β-adrenergic receptor (βAR) agonists, dobutamine, and ractopamine (a feed additive that promotes leanness in cattle and poultry), which are the first βAR agonists identified as activators of ERα-mediated gene transcription. This approach can be applied to other receptors implicated in endocrine disruption.

PIK3CA , the gene that encodes the catalytic subunit of phosphatidylinositol 3-kinase α (PI3Kα), is frequently mutated in breast and other types of cancer. A specific inhibitor that targets the mutant forms of PI3Kα could maximize treatment efficiency while minimizing side-effects. Herein we describe the identification of novel binding pockets that may provide an opportunity for the design of mutant selective inhibitors. Using a fragment-based approach, we screened a library of 352 fragments (MW <300 Da) for binding to PI3Kα by X-ray crystallography. Five novel binding pockets were identified, each providing potential opportunities for inhibitor design. Of particular interest was a binding pocket near Glu542, which is located in one of the two most frequently mutated domains.

Allosteric enhancers of the adenosine A 1 receptor amplify signaling by orthosteric agonists. Allosteric enhancers are appealing drug candidates because their activity requires that the orthosteric site be occupied by an agonist, thereby conferring specificity to stressed or injured tissues that produce adenosine. To explore the mechanism of allosteric enhancer activity, we examined their action on several A 1 receptor constructs, including (1) species variants, (2) species chimeras, (3) alanine scanning mutants, and (4) site-specific mutants. These findings were combined with homology modeling of the A 1 receptor and in silico screening of an allosteric enhancer library. The binding modes of known docked allosteric enhancers correlated with the known structure-activity relationship, suggesting that these allosteric enhancers bind to a pocket formed by the second extracellular loop, flanked by residues S150 and M162. We propose a model in which this vestibule controls the entry and efflux of agonists from the orthosteric site and agonist binding elicits a conformational change that enables allosteric enhancer binding. This model provides a mechanism for the observations that allosteric enhancers slow the dissociation of orthosteric agonists but not antagonists.