Molecular Basis of Action of a Small-Molecule Positive Allosteric Modulator Agonist at the Type 1 Cholecystokinin Holoreceptor.

Allosteric modulation of receptors provides mechanistic safety while effectively achieving biologic endpoints otherwise difficult or impossible to obtain by other means. The theoretical case has been made for the development of a positive allosteric modulator (PAM) of the type 1 cholecystokinin receptor (CCK1R) having minimal intrinsic agonist activity to enhance meal-induced satiety for the treatment of obesity, while reducing the risk of side effects and/or toxicity. Unfortunately, such a drug does not currently exist. In this work, we have identified a PAM agonist of the CCK1R, SR146131, and determined its putative binding mode and receptor activation mechanism by combining molecular modeling, chimeric CCK1R/CCK2R constructs, and site-directed mutagenesis. We probed the structure-activity relationship of analogs of SR146131 for impact on agonism versus cooperativity of the analogs. This identified structural features that might be responsible for binding affinity and potency while retaining PAM activity. SR146131 and several of its analogs were docked into the receptor structure, which had the natural endogenous peptide agonist, cholecystokinin, already in the bound state (by docking), providing a refined structural model of the intact CCK1R holoreceptor. Both SR146131 and its analogs exhibited unique probe-dependent cooperativity with orthosteric peptide agonists and were simultaneously accommodated in this model, consistent with the derived structure-activity relationships. This provides improved understanding of the molecular basis for CCK1R-directed drug development.

Identification of previously unrecognized antiestrogenic chemicals using a novel virtual screening approach.

The physiological roles of estrogen in sexual differentiation and development, female and male reproductive processes, and bone health are complex and diverse. Numerous natural and synthetic chemical compounds, commonly known as endocrine disrupting chemicals (EDCs), have been shown to alter the physiological effects of estrogen in humans and wildlife. As such, these EDCs may cause unanticipated and even undesirable effects. Large-scale in vitro and in vivo screening of chemicals to assess their estrogenic activity would demand a prodigious investment of time, labor, and money and would require animal testing on an unprecedented scale. Approaches in silico are increasingly recognized as playing a vital role in screening and prioritizing chemicals to extend limited resources available for experimental testing. Here, we evaluated a multistep procedure that is suitable for in silico (virtual) screening of large chemical databases to identify compounds exhibiting estrogenic activity. This procedure incorporates Shape Signatures, a novel computational tool that rapidly compares molecules on the basis of similarity in shape, polarity, and other bio-relevant properties. Using 4-hydroxy tamoxifen (4-OH TAM) and diethylstilbestrol (DES) as input queries, we employed this scheme to search a sample database of approximately 200,000 commercially available organic chemicals for matches (hits). Of the eight compounds identified computationally as potentially (anti)estrogenic, biological evaluation confirmed two as heretofore unknown estrogen antagonists. Subsequent radioligand binding assays confirmed that two of these three compounds exhibit antiestrogenic activities comparable to 4-OH TAM. Molecular modeling studies of these ligands docked inside the binding pocket of estrogen receptor alpha (ERalpha) elucidated key ligand-receptor interactions that corroborate these experimental findings. The present study demonstrates the utility of our computational scheme for this and related applications in drug discovery, predictive toxicology, and virtual screening.

Enrichment of ligands for the serotonin receptor using the Shape Signatures approach.

Shape Signatures, a new 3-dimensional molecular comparison method, has been adapted to rank ligands of the serotonin receptors. A set of 825 agonists and 400 antagonists together with approximately 10,000 randomly chosen compounds from the NCI database were used in this study. Both 1D and 2D Shape Signature databases were created, and enrichment studies were carried out. Results from these studies reveal that the 1D Shape Signature approach is highly efficient in separating agonists from a mixture of molecules which includes compounds randomly selected from the NCI database taken as inactives. It is also equally effective at separating agonists and antagonists from a pool of active ligands for the serotonin receptor. Parallel enrichment studies using 2D shape signatures showed high selectivity with more restricted coverage due to the high specificity of 2D signatures. The influence of conformational variation of the shape signature on enrichment was explored by docking a subset of ligands into the crystal structure of serotonin N-acetyltransferase. Enrichment studies on the resulting "docked" conformations produced only slightly improved results compared with the CORINA-generated conformations.