Structural Basis for the β-Adrenergic Receptor Subtype Selectivity of the Representative Agonists and Antagonists

Abstract

The β(3)-adrenegic receptor (β(3)-AR) selectivity over β(1)- and β(2)-ARs has been the most important aspect for successful therapeutic agents for obesity and type-II diabetes, as the concomitant activation of β(1)- and β(2)-ARs would lead to undesirable side effects, such as increased heart rate. In order to explore the structural basis for the β-AR subtype selectivity of agonists and anatagonists, a three-dimensional structure of until date unresolved β(3)-AR has been modeled, compared with the resolved X-ray structures of β(1)- and β(2)-ARs, and used to study its stereoselective binding with until-date known diverse classes of representative agonists and antagonist. The obtained binding structures and calculated prime molecular mechanics-generalized Born surface area (MM-GBSA) binding free energies consistently reveal that while the subtype selectivity is strongly governed by the residues present in the extracellular ends of TM3, TM5, TM6, TM7 helices and of the ECL2 domain, the binding affinity is governed by the conserved residues present in the deep pocket limiting the degree of conformational and rotational freedoms to the bound ligand. The study demonstrates that the key structural requirements for the β(3)-selectivity are: (i) a negatively ionizable group (NIG) for direct interaction with β(3)-specific residue R315(6.58), (ii) a linker (9-10 Å length) between the protonated amine and NIG, and (iii) a substituted aryl ring directly attached to the β-hydroxyl carbon. The new computational insights acquired in this study are expected to be valuable in structure-based rational design of high-affinity agonists and antagonists with pronounced β(3)-selectivity for successful therapeutic agents for type-II diabetes and obesity.

Pharmacophore‐guided Virtual Screening to Identify New β3‐adrenergic Receptor Agonists

“…This is thought to be due to subtle differences in the binding site of the rodent receptors versus human receptors [21] . As previously stated by Roy and Saxena, A197 in the EL2 domain of the hβ 3 ‐AR that corresponds to the aspartic acid residue at both hβ 1 ‐AR and hβ 2 ‐AR, must be involved in the selectivity within hβ‐ARs [82] . In line with this, the hydroxyl group of S194 (EL2) of mβ 3 ‐ARs interacted with compound 21 (Figure 7) and mirabegron through hydrogen binding.…”

“…found that S208 5.42 and Ser212 5.46 form hydrogen bonds with the hydroxyl group of catechol of the endogenous agonists [83] . However, most β 3 ‐AR agonists lack a catechol group, therefore this hydrogen bonds were mostly absent [82] . Nagiri et al .…”

“…Previous homology modeling studies with the hβ 3 -AR were based on the 3D structure of the hβ 2 -AR. [82,83] These models were based, however, on hβ 2 -AR bound to the inverse agonist carazolol (PDB ID: 2RH1) [84] which is an inactive conformation. [35] Hence, the structure was using the hβ 2 -AR coupled to G proteins (PDB ID: 3SN6, res: 3.20 Å).…”

Insights into a defined secondary binding region on β-adrenoceptors and putative roles in ligand binding and drug design

“…4). 100,101 Indeed, the advances in this area could be the key for developing compounds with high selectivity for either β 2 ARs or β 3 ARs.…”

Integrating Pharmacophore into Membrane Molecular Dynamics Simulations to Improve Homology Modeling of G Protein‐coupled Receptors with Ligand Selectivity: A2A Adenosine Receptor as an Example

“…As an important ligand‐based drug design strategy, the generation of pharmacophores based on a series of antagonists/agonists can determine the position and direction of hydrogen bond, as well as hydrophobic cavity that aid in the identification and characterization of binding sites. Furthermore, the sophisticated strategy of membrane molecular dynamics (MD), which mimics a GPCR–ligand complex embedded into explicit lipid–water environments, has been adapted to the investigation of ligand–receptor binding mechanism . In this study, we propose a novel strategy by integrating pharmacophore and membrane MD simulations to improve homology modeling of GPCRs with ligand selectivity.…”