Identification of Novel Allosteric Modulators of Metabotropic Glutamate Receptor Subtype 5 Acting at Site Distinct from 2-Methyl-6-(phenylethynyl)-pyridine Binding

Current Trends in GPCR Allostery

GPCRs remain the most important drug target comprising ~ 34% of the Food and Drug Administration (FDA)-approved drugs. In modern pharmacology of GPCRs, modulating receptor signaling based on requirement of a specific disorder is of immense interest. Classical drugs targeting orthosteric sites in GPCRs completely block the binding of endogenous ligand and consequently inhibit all important signals from a GPCR. Some of many signals elicited by the endogenous ligands may play vital role and inhibiting these may also cause severe side effects in the long run. However, allosteric drugs can modulate GPCR signaling without blocking the endogenous ligand binding. Therefore, allosteric drugs can maintain beneficial signaling of the receptor and prevent unwanted side effects. In this chapter, we will discuss GPCR crystal structures solved with allosteric ligands, advantages of allosteric drugs, and allosteric drugs which are in clinical use or trials.

BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling

G protein-coupled receptors (GPCRs) are the largest family of membrane proteins with more than 800 members. GPCRs are involved in numerous physiological functions within the human body and are the target of more than 30% of the United States Food and Drug Administration (FDA) approved drugs. At present, over 400 experimental GPCR structures are available in the Protein Data Bank (PDB) representing 76 unique receptors. The absence of an experimental structure for the majority of GPCRs demand homology models for structure-based drug discovery workflows. The generation of good homology models requires appropriate templates. The commonly used methods for template selection are based on sequence identity. However, there exists low sequence identity among the GPCRs. Sequences with similar patterns of hydrophobic residues are often structural homologs, even with low sequence identity. Extending this, we propose a biophysical approach for template selection based principally on hydrophobicity correspondence between the target and the template. Our approach takes into consideration other relevant parameters, including resolution, similarity within the orthosteric binding pocket of GPCRs, and structure completeness, for template selection. The proposed method was implemented in the form of a free tool called Bio-GATS, to provide the user with easy selection of the appropriate template for a query GPCR sequence. Bio-GATS was successfully validated with recent published benchmarking datasets. An application to an olfactory receptor to select an appropriate template has also been provided as a case study.

Exception That Proves the Rule: Investigation of Privileged Stereochemistry in Designing Dopamine D3R Bitopic Agonists

In this study, starting from our selective D₃R agonist FOB02-04A (5), we investigated the chemical space around the linker portion of the molecule via insertion of a hydroxyl substituent and ring-expansion of the trans-cyclopropyl moiety into a trans-cyclohexyl scaffold. Moreover, to further elucidate the importance of the primary pharmacophore stereochemistry in the design of bitopic ligands, we investigated the chiral requirements of (+)-PD128907 ((+)-(4a R ,10b R )-2)) by synthesizing and resolving bitopic analogues in all the cis and trans combinations of its 9-methoxy-3,4,4a,10b-tetrahydro-2H,5H-chromeno[4,3-b][1,4] oxazine scaffold. Despite the lack of success in obtaining new analogues with improved biological profiles, in comparison to our current leads, a "negative" result due to a poor or simply not improved biological profile is fundamental toward better understanding chemical space and optimal stereochemistry for target recognition. Herein, we identified essential structural information to understand the differences between orthosteric and bitopic ligand-receptor binding interactions, discriminate D₃R active and inactive states, and assist multitarget receptor recognition. Exploring stereochemical complexity and developing extended D₃R SAR from this new library complements previously described SAR and inspires future structural and computational biology investigation. Moreover, the expansion of chemical space characterization for D₃R agonism may be utilized in machine learning and artificial intelligence (AI)-based drug design, in the future.