Pharmacophore and receptor models for neurokinin receptors

Abstract

Three neurokinin (NK) antagonist pharmacophore models (Models 1-3) accounting for hydrogen bonding groups in the 'head' and 'tail' of NK receptor ligands have been developed by use of a new procedure for treatment of hydrogen bonds during superimposition. Instead of modelling the hydrogen bond acceptor vector in the strict direction of the lone pair, an angle is allowed between the hydrogen bond acceptor direction and the ideal lone pair direction. This approach adds flexibility to hydrogen bond directions and produces more realistic RMS values. By using this approach, two novel pharmacophore models were derived (Models 2 and 3) and a hydrogen bond acceptor was added to a previously published NK2 pharmacophore model [Poulsen et al., J. Comput.-Aided Mol. Design, 16 (2002) 273] (Model 1). Model 2 as well as Model 3 are described by seven pharmacophore elements: three hydrophobic groups, three hydrogen bond acceptors and a hydrogen bond donor. Model 1 contains the same hydrophobic groups and hydrogen bond donor as Models 2 and 3, but only one hydrogen bond acceptor. The hydrogen bond acceptors and donor are represented as vectors. Two of the hydrophobic groups are always aromatic rings whereas the other hydrophobic group can be either aromatic or aliphatic. In Model 1 the antagonists bind in an extended conformation with two aromatic rings in a parallel displaced and tilted conformation. Model 2 has the same two aromatic rings in a parallel displaced conformation whereas Model 3 has the rings in an edge to face conformation. The pharmacophore models were evaluated using both a structure (NK receptor homology models) and a ligand based approach. By use of exhaustive conformational analysis (MMFFs force field and the GB/SA hydration model) and least-squares molecular superimposition studies, 21 non-peptide antagonists from several structurally diverse classes were fitted to the pharmacophore models. More antagonists could be fitted to Model 2 with a low RMS and a low conformational energy penalty than to Models 1 and 3. Pharmacophore Model 2 was also able to explain the NK1, NK2 and NK3 subtype selectivity of the compounds fitted to the model. Three NK 7TM receptor models were constructed, one for each receptor subtype. The location of the antagonist binding site in the three NK receptor models is identical. Compounds fitted to pharmacophore Model 2 could be docked into the NK1, NK2 and NK3 receptor models after adjustment of the conformation of the flexible linker connecting the head and tail. Models I and 3 are not compatible with the receptor models.

Pharmacophore Based Receptor Modeling: The Case of Adenosine A3 Receptor Antagonists. An Approach to the Optimization of Protein Models

“…Many studies have been published in which the docking procedure was used as an alignment tool for the development of 3D-QSAR models. [22][23][24] Furthermore, some recent studies have demonstrated that GPCR models of higher accuracy can be produced if homology modeling, based on the rhodopsin X-ray template, is supported by experimental structural constraints appropriate for active or inactive receptor conformations 25 or if crude models are optimized by including ligand-based information. 26 Regarding this last approach, Klebe and co-workers have recently developed the MOBILE method, where homology models are refined by including information about bioactive ligands as spatial restraints, 26 and have applied it to the discovery of neurokinin-1 receptor antagonists.…”

“…To design and synthesize a new generation of potent and selective antagonists with improved absorption, distribution, metabolism, and excretion (ADME) profiles as well as increased water solubility compared to previous analogues, we set up a drug design approach, in which ligand-based information and homology modeling were combined throughout to increase the success possibilities. Many studies have been published in which the docking procedure was used as an alignment tool for the development of 3D-QSAR models. − Furthermore, some recent studies have demonstrated that GPCR models of higher accuracy can be produced if homology modeling, based on the rhodopsin X-ray template, is supported by experimental structural constraints appropriate for active or inactive receptor conformations or if crude models are optimized by including ligand-based information . Regarding this last approach, Klebe and co-workers have recently developed the MOBILE method, where homology models are refined by including information about bioactive ligands as spatial restraints, and have applied it to the discovery of neurokinin-1 receptor antagonists …”

Current Progress in Structure-Based Rational Drug Design Marks a New Mindset in Drug Discovery

“…So far, antagonist/agonist ligands have been mostly identified using ligand based approaches consisting of filtering libraries of commercially available compounds with pharmacophore models [141–143]. Nevertheless, carefully constructed homology models of rhodopsin-like GPCRs are increasingly reported, with the concomitant use of docking, which allowed enrichment of screened compounds with actives with rates similar to those obtained with the ligand-based pharmacophore approaches [144–154].…”

Pharmacophore Modeling, Virtual Screening, and in Vitro Testing Reveal Haloperidol, Eprazinone, and Fenbutrazate as Neurokinin Receptors Ligands

“…Finally, compounds fitting to one of the models were docked into the receptor models. 18 Recently, Di Fabio et al 19 described an NK 1 R pharmacophore, based on the structural information of 10 NK 1 R antagonists from six structural classes, including orvepitant. This model consists of two HPFs, one ARF, and one HBAF.…”