Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment

The community-wide GPCR Dock assessment is conducted to evaluate the status of molecular modeling and ligand docking for human G protein-coupled receptors. The present round of the assessment was based on the recent structures of dopamine D3 and CXCR4 chemokine receptors bound to small molecule antagonists and CXCR4 with a synthetic cyclopeptide. Thirty-five groups submitted their receptor-ligand complex structure predictions prior to the release of the crystallographic coordinates. With closely related homology modeling templates, as for dopamine D3 receptor, and with incorporation of biochemical and QSAR data, modern computational techniques predicted complex details with accuracy approaching experimental. In contrast, CXCR4 complexes that had less-characterized interactions and only distant homology to the known GPCR structures still remained very challenging. The assessment results provide guidance for modeling and crystallographic communities in method development and target selection for further expansion of the structural coverage of the GPCR universe. © 2011 Elsevier Ltd. All rights reserved

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 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

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

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

<i>In Silico</i> 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 <i>in silico</i> the ligand-binding sites of 28 human “side-effect” targets to their corresponding orthologs in <i>Danio rerio</i>, <i>Pimephales promelas</i>, <i>Takifugu rubripes</i>, <i>Xenopus laevis</i>, and <i>Xenopus tropicalis</i>, 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

Homobivalent Ligands of the Atypical Antipsychotic Clozapine: Design, Synthesis, and Pharmacological Evaluation

To date all typical and atypical antipsychotics target the dopamine D₂ receptor. Clozapine represents the best-characterized atypical antipsychotic, although it displays only moderate (submicromolar) affinity for the dopamine D₂ 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 D₂ 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

Homology Modeling of Human Muscarinic Acetylcholine Receptors

We have developed homology models of the acetylcholine muscarinic receptors M₁R–M₅R, based on the β₂-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 naı̈ve 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