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. 2017 Jul 6;7(1):4829.
doi: 10.1038/s41598-017-05058-w.

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

Ida Osborn Frandsen  1 , Michael W Boesgaard  1 , Kimberley Fidom  1 , Alexander S Hauser  1 , Vignir Isberg  1 , Hans Bräuner-Osborne  1 , Petrine Wellendorph  2 , David E Gloriam  3
Affiliations

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

Ida Osborn Frandsen et al. Sci Rep. .

Abstract

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.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Histamine H3 receptor pharmacophore model used in the virtual screening for new ligands. The pharmacophore was based on a receptor residue – ligand moiety fragment library derived from GPCR crystal structures (Supplementary Table 1), and built with Phase. The pharmacophore elements include three hydrogen bond donor (D1-3, light blue), three cation (P4-6, blue), three aromatic (R7, R9 and R10; orange) and one dual aromatic/hydrophobic (R8, orange) features.
Figure 2
Figure 2
Assessment of histamine H3 receptor Gq-coupled pharmacological assay using the IP1 accumulation assay measured by the HTRF IP-One assay. Concentration-dependent responses to histamine in H3 receptor expressing tsA201 cells transfected with (black) and without (grey) Gqi5. Data are means ± S.D. of a single representative experiment performed in triplicate. Two additional experiments gave similar results.
Figure 3
Figure 3
IP-One assay concentration-response curves. (A) Concentration-inhibition curves of the five identified neutral antagonists in the presence of histamine (EC80 concentration). (B) Inverse agonism of compound 76 (no histamine added). Data are normalized to the basal level of IP1 (fold response) in ligand buffer and are shown as means S.D. of a single representative experiment performed in triplicate. Due to solubility issues the lower plateau for the concentration-response curve of compounds 46, 57 and 67 was constrained to the buffer value during curve fitting.
Figure 4
Figure 4
(A) Radioligand competition binding assay validates hits at the H3 receptor. Representative curves of the competitive binding of [3H]N-α-methylhistamine (0.3 nM) in the presence of various concentrations of compounds. Data points are shown as the mean ± S.D. of triplicate measurements. (B) The obtained affinities correlate with the potencies obtained in the functional assay. The potencies of the six most potent compounds obtained from the IP-One assay (Functional) shown as pIC50 compared with their affinities as determined in the radioligand competition binding assay (Binding). All data points are shown as the mean ± S.E.M, n = 2–5.
Figure 5
Figure 5
Histamine receptor subtype selectivity hotspots. (A) Sequence alignment of the non-conserved histamine receptor binding cavity residues that offer the most viable contact points for achieving subtype-selective ligands. Below the consensus sequence is shown also the residue properties, which when unique for the target may guide the choice of ligand substitution. (B) Superposition of the H3 target (brown) with the most homologous receptor, H4 (green). Pharmacophore fragments are displayed as balls and sticks (cyano) and selectivity hotspot residues as tubes.
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References

    1. Pierce KL, Premont RT, Lefkowitz RJ. Seven-transmembrane receptors. Nature reviews. Molecular cell biology. 2002;3:639–650. doi: 10.1038/nrm908. - DOI - PubMed
    1. Venter JC, et al. The sequence of the human genome. Science. 2001;291:1304–1351. doi: 10.1126/science.1058040. - DOI - PubMed
    1. Lagerstrom MC, Schioth HB. Structural diversity of G protein-coupled receptors and significance for drug discovery. Nature reviews. Drug discovery. 2008;7:339–357. doi: 10.1038/nrd2518. - DOI - PubMed
    1. Pawson AJ, et al. The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands. Nucleic Acids Res. 2014;42:D1098–1106. doi: 10.1093/nar/gkt1143. - DOI - PMC - PubMed
    1. Drews J. Drug discovery: a historical perspective. Science. 2000;287:1960–1964. doi: 10.1126/science.287.5460.1960. - DOI - PubMed

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