Chemogenomics of allosteric binding sites in GPCRs

Emerging strategies targeting CB2 cannabinoid receptor: Biased agonism and allosterism

During these last years, the CB₂ cannabinoid receptor has emerged as a potential anti-inflammatory target in diseases such as multiple sclerosis, amyotrophic lateral sclerosis, Huntington's disease, ischemic stroke, autoimmune diseases, osteoporosis, and cancer. However, the development of clinically useful CB₂ agonists reveals to be very challenging. Allosterism and biased-signaling mechanisms at CB₂ receptor may offer new avenues for the development of improved CB₂ receptor-targeted therapies. Although there has been some exploration of CB₁ receptor activation by new CB₁ allosteric or biased-signaling ligands, the CB₂ receptor is still at initial stages in this domain. In an effort to understand the molecular basis behind these pharmacological approaches, we have analyzed and summarized the structural data reported so far at CB₂ receptor.

Chemogenomic Profiling of the Fungal Pathogen Candida albicans

There is currently a small number of classes of antifungal drugs, and these drugs are known to target a very limited set of cellular functions. We derived a set of approximately 900 nonessential, transactivator-defective disruption strains from the tetracycline-regulated GRACE collection of strains of the fungal pathogen Candida albicans This strain set was screened against classic antifungal drugs to identify gene inactivations that conferred either enhanced sensitivity or increased resistance to the compounds. We examined two azoles, fluconazole and posaconazole; two echinocandins, caspofungin and anidulafungin; and a polyene, amphotericin B. Overall, the chemogenomic profiles within drug classes were highly similar, but there was little overlap between classes, suggesting that the different drug classes interacted with discrete networks of genes in C. albicans We also tested two pyridine amides, designated GPI-LY7 and GPI-C107; these drugs gave very similar profiles that were distinct from those of the echinocandins, azoles, or polyenes, supporting the idea that they target a distinct cellular function. Intriguingly, in cases where these gene sets can be compared to genetic disruptions conferring drug sensitivity in other fungi, we find very little correspondence in genes. Thus, even though the drug targets are the same in the different species, the specific genetic profiles that can lead to drug sensitivity are distinct. This implies that chemogenomic screens of one organism may be poorly predictive of the profiles found in other organisms and that drug sensitivity and resistance profiles can differ significantly among organisms even when the apparent target of the drug is the same.

Identification of Histamine H3 Receptor Ligands Using a New Crystal Structure Fragment-based Method

Virtual screening offers an efficient alternative to high-throughput screening in the identification of pharmacological tools and lead compounds. Virtual screening is typically based on the matching of target structures or ligand pharmacophores to commercial or in-house compound catalogues. This study provides the first proof-of-concept for our recently reported method where pharmacophores are instead constructed based on the inference of residue-ligand fragments from crystal structures. We demonstrate its unique utility for G protein-coupled receptors, which represent the largest families of human membrane proteins and drug targets. We identified five neutral antagonists and one inverse agonist for the histamine H₃ receptor with potencies of 0.7-8.5 μM in a recombinant receptor cell-based inositol phosphate accumulation assay and validated their activity using a radioligand competition binding assay. H₃ receptor antagonism is of large therapeutic value and our ligands could serve as starting points for further lead optimisation. The six ligands exhibit four chemical scaffolds, whereof three have high novelty in comparison to the known H₃ receptor ligands in the ChEMBL database. The complete pharmacophore fragment library is freely available through the GPCR database, GPCRdb, allowing the successful application herein to be repeated for most of the 285 class A GPCR targets. The method could also easily be adapted to other protein families.

Molecular interaction fingerprint approaches for GPCR drug discovery

Protein-ligand interaction fingerprints (IFPs) are binary 1D representations of the 3D structure of protein-ligand complexes encoding the presence or absence of specific interactions between the binding pocket amino acids and the ligand. Various implementations of IFPs have been developed and successfully applied for post-processing molecular docking results for G Protein-Coupled Receptor (GPCR) ligand binding mode prediction and virtual ligand screening. Novel interaction fingerprint methods enable structural chemogenomics and polypharmacology predictions by complementing the increasing amount of GPCR structural data. Machine learning methods are increasingly used to derive relationships between bioactivity data and fingerprint descriptors of chemical and structural information of binding sites, ligands, and protein-ligand interactions. Factors that influence the application of IFPs include structure preparation, binding site definition, fingerprint similarity assessment, and data processing and these factors pose challenges as well possibilities to optimize interaction fingerprint methods for GPCR drug discovery.

Generic GPCR residue numbers - aligning topology maps while minding the gaps

Generic residue numbers facilitate comparisons of, for example, mutational effects, ligand interactions, and structural motifs. The numbering scheme by Ballesteros and Weinstein for residues within the class A GPCRs (G protein-coupled receptors) has more than 1100 citations, and the recent crystal structures for classes B, C, and F now call for a community consensus in residue numbering within and across these classes. Furthermore, the structural era has uncovered helix bulges and constrictions that offset the generic residue numbers. The use of generic residue numbers depends on convenient access by pharmacologists, chemists, and structural biologists. We review the generic residue numbering schemes for each GPCR class, as well as a complementary structure-based scheme, and provide illustrative examples and GPCR database (GPCRDB) web tools to number any receptor sequence or structure.