Orphan receptor ligand discovery by pickpocketing pharmacological neighbors.

Understanding the pharmacological similarity of G protein-coupled receptors (GPCRs) is paramount for 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 in 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.

Identification of allosteric binding sites for PI3Kα oncogenic mutant specific inhibitor design.

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<300Da) 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.

The second extracellular loop of the adenosine A1 receptor mediates activity of allosteric enhancers.

Allosteric enhancers of the adenosine A1 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 A1 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 A1 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.

Homology modeling of human muscarinic acetylcholine receptors.

We have developed homology models of the acetylcholine muscarinic receptors M1R-M5R, based on the ß2-adrenergic receptor crystal as the template. This is the first report of homology modeling of all five subtypes of acetylcholine muscarinic receptors with binding sites optimized for ligand binding. The models were evaluated for their ability to discriminate between muscarinic antagonists and decoy compounds using virtual screening using enrichment factors, area under the ROC curve (AUC), and an early enrichment measure, LogAUC. The models produce rational binding modes of docked ligands as well as good enrichment capacity when tested against property-matched decoy libraries, which demonstrates their unbiased predictive ability. To test the relative effects of homology model template selection and the binding site optimization procedure, we generated and evaluated a naïve M2R model, using the M3R crystal structure as a template. Our results confirm previous findings that binding site optimization using ligand(s) active at a particular receptor, i.e. including functional knowledge into the model building process, has a more pronounced effect on model quality than target-template sequence similarity. The optimized M1R-M5R homology models are made available as part of the Supporting Information to allow researchers to use these structures, compare them to their own results, and thus advance the development of better modeling approaches.

In silico analysis of the conservation of human toxicity and endocrine disruption targets in aquatic species.

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 subpockets 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 subpockets may vary dramatically. Finally, we identified the aquatic model(s) with the highest binding site similarity, compared to the corresponding human toxicity target.

Discovery of a Novel Class of d-Amino Acid Oxidase Inhibitors Using the Schrödinger Computational Platform.

d-Serine is a coagonist of the N-methyl d-aspartate (NMDA) receptor, a key excitatory neurotransmitter receptor. In the brain, d-serine is synthesized from its l-isomer by serine racemase and is metabolized by the D-amino acid oxidase (DAO, DAAO). Many studies have linked decreased d-serine concentration and/or increased DAO expression and enzyme activity to NMDA dysfunction and schizophrenia. Thus, it is feasible to employ DAO inhibitors for the treatment of schizophrenia and other indications. Powered by the Schrödinger computational modeling platform, we initiated a research program to identify novel DAO inhibitors with the best-in-class properties. The program execution leveraged an hDAO FEP+ model to prospectively predict compound potency. A new class of DAO inhibitors with desirable properties has been discovered from this endeavor. Our modeling technology on this program has not only enhanced the efficiency of structure-activity relationship development but also helped to identify a previously unexplored subpocket for further optimization.

Homology modeling and docking evaluation of aminergic G protein-coupled receptors.

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.

Molecular dynamics techniques for modeling G protein-coupled receptors.

G protein-coupled receptors (GPCRs) constitute a major class of drug targets and modulating their signaling can produce a wide range of pharmacological outcomes. With the growing number of high-resolution GPCR crystal structures, we have the unprecedented opportunity to leverage structure-based drug design techniques. Here, we discuss a number of advanced molecular dynamics (MD) techniques that have been applied to GPCRs, including long time scale simulations, enhanced sampling techniques, water network analyses, and free energy approaches to determine relative binding free energies. On the basis of the many success stories, including those highlighted here, we expect that MD techniques will be increasingly applied to aid in structure-based drug design and lead optimization for GPCRs.

Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation.

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, ß1 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.

In silico identification and pharmacological evaluation of novel endocrine disrupting chemicals that act via the ligand-binding domain of the estrogen receptor α.

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 ß-adrenergic receptor (ßAR) agonists, dobutamine, and ractopamine (a feed additive that promotes leanness in cattle and poultry), which are the first ßAR agonists identified as activators of ERα-mediated gene transcription. This approach can be applied to other receptors implicated in endocrine disruption.

Homobivalent ligands of the atypical antipsychotic clozapine: design, synthesis, and pharmacological evaluation.

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.

Synthesis and pharmacological evaluation of dual acting antioxidant A(2A) adenosine receptor agonists.

A series of adenosine-5'-N-alkylcarboxamides and N(6)-(2,2-diphenylethyl)adenosine-5'-N-alkylcarboxamides bearing antioxidant moieties in the 2-position were synthesized from the versatile intermediate, O(6)-(benzotriazol-1-yl)-2-fluoro-2',3'-O-isopropylideneinosine-5'-N-alkylcarboxamide (1). These compounds were evaluated as A(2A) adenosine receptor (A(2A)R) agonists in a cAMP accumulation assay, and a number of potent and selective agonists were identified. Three of these compounds were evaluated further in an ischemic injury cell survival assay and a reactive oxygen species (ROS) production assay whereby 15b and 15c were shown to reduce ROS activity and cell death due to ischemia.