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Genomics Inform > Volume 10(2); 2012 > Article
Oh: Characteristics in Molecular Vibrational Frequency Patterns between Agonists and Antagonists of Histamine Receptors

Abstract

To learn the differences between the structure-activity relationship and molecular vibration-activity relationship in the ligand-receptor interaction of the histamine receptor, 47 ligands of the histamine receptor were analyzed by structural similarity and molecular vibrational frequency patterns. The radial tree that was produced by clustering analysis of molecular vibrational frequency patterns shows its potential for the functional classification of histamine receptor ligands.

Introduction

G protein-coupled receptors (GPCRs) are regarded to take up more than one-fourth of marketed human medicines [1, 2]. Drugs targeting GPCRs account for the majority of the best-selling drugs and about 40% of all prescription pharmaceuticals in the marketplace [3].
Histamine receptors belong to one family of rhodopsin-like class A GPCRs, and four subtypes are named in chronological order as H1, H2, H3, and H4. Histamine exerts its effects through the activation of these four histamine receptors. Each type of histamine receptor reacts to subtype-specific ligands into an active or inactive form.
GPCRs are integral membrane proteins that consist of 7 transmembrane segments connected by 3 intracellular and 3 extracellular loops of variable length. The crystal structures of GPCRs with their binding ligands have revealed the features of the ligand binding pockets and extracellular loops [4-6]. A ligand of GPCR activates the receptor by changing the receptor structure to the active form.
The biogenic amine histamine [2-(1H-imidazol-5-yl) ethanamine] (Fig. 1) is produced by decarboxylation of L-histidine and acts as a chemical mediator and neurotransmitter in central and peripheral tissues. It acts as an important pharmacological modulator involved in the processes of allergy, inflammation, neurophysiology, and cancer [7-10].
The molecular understanding of ligand-receptor interactions of GPCR remains unclear and is still the subject of investigations. There are several theories explaining the ligand-receptor interaction mechanism, such as shape theory, binding theory, and vibration theory [11, 12]. The structures of histamine receptor ligands are so variable that we can not easily classify the pharmacological function of the ligand. To find any other characteristic in the molecular patterns between agonists and antagonists of histamine receptors, a computational approach to molecular vibration was carried out in an attempt to find a bit of the molecular interaction mechanisms.

Methods

Dataset

The simplified molecular-input line-entry system (SMILES) and 3-dimensional structure data format (SDF) files of the dataset were downloaded from the PubChem Compound Database in National Center for Biotechnology Information (NCBI) and used in the further analyses. All 47 ligand molecules in the dataset, comprising 9 histamine agonists and 38 histamine antagonists, are shown in Table 1.

Structure comparison of histamine receptor ligands

The molecular similarities between histamine and other 46 chemicals were calculated from SMILES of the chemicals. The structural similarity was calculated and represented as the Tanimoto distance of each molecule from histamine. The Tanimoto coefficient for pairwise comparison of molecules is the most widely used measure of molecular structural similarity. This coefficient is defined as Tc = Nab/(Na + Nb - Nab), with Nab being the number of common bits, Na the unique bits in molecule a, and Nb the unique bits in molecule b, using a molecular fingerprint [13]. In this study, the molecular similarity was calculated as the Tanimoto coefficient using the 38-bit set.

Geometry optimization and calculation of molecular vibrational frequency

In order to calculate molecular vibrational frequency, the structure of a chemical must first be geometrically optimized. Since each provided theoretical 3-D conformer SDF is not at an energy minimum and may not represent the lowest energetic form in a vacuum, solvent, or a binding pocket, each SDF file of a ligand molecule underwent conversion to a single low-energy conformation using the general atomic and molecular electronic structure system (GAMESS) program package [14]. Restricted Hartree-Fock (RHF) calculations using Becke's exchange and Lee-Yang-Parr's correlation functionals (BLYP) density functional theory (DFT) method with 6-31G basis set were performed to optimize the geometries of the molecules. Each result was taken as the representative conformation of the molecule, although the calculation of molecular vibrational frequency has some dependence on conformation. Each geometry optimization result was subjected to the calculation step for the vibrational frequency with RUNTYP of HESSIAN in the GAMESS program.

Hierarchical clustering of the corralled intensity of molecular vibrational frequency (CIMVF)

For the simplified molecular comparison, the calculated vibrational frequencies of a molecule were then sorted in increasing order and taken into the corrals, the step size of which was 5. The intensities of each frequency in the same corral were summed up as the representative of the corral in the frequency range of 0-5,000 cm-1. As a final outcome, this potential molecular descriptor of each molecule was displayed in a 1-dimensional vector containing 1,000 elements. Finally, the similarity matrix, comprising the descriptors of 47 ligands of histamine receptor, was then subjected to hierarchical clustering in the agglomerative manner. In this study, the similarity matrix was finally clustered to make an unrooted tree of 47 vertices. The calculations of CIMVF were performed by in-house scripts, written in Python.

Results and Discussion

The Tanimoto coefficients between histamine and other ligand molecules are shown in Table 2. Typically, a Tanimoto coefficient > 0.85 is considered highly similar, and a coefficient > 0.75 is considered similar for the purpose of clustering molecules that may have similar biological activity profiles [15]. The highest Tanimoto coefficient among agonists was 0.45 (imetit), and this is not high enough to be considered a molecule that has potential agonist efficacy. Moreover, the lowest value of Tanimoto coefficient among agonists was 0.08 (dimaprit and SKF91488) and is also a lower value as an antagonist. It seems that there is no related pattern between Tanimoto coefficients and the functional types of molecules in the case of histamine receptor agonists or antagonists.
To search for a novel characteristic for the classification of histamine receptor ligands, a kind of molecular calculation using agglomerative hierarchical clustering was adopted in this work. The result of the hierarchical clustering of the similarity matrix from CIMVF is shown in Fig. 2. As shown in the figure, eight agonists were located nearby (the part in the dotted circle), except impromidine, and all antagonists were clustered close to each other in the radial tree. We can tell the regional difference between agonists and antagonists in the tree and also find that the information from the molecular vibrational frequency may play a role in the classification of agonists/antagonists for histamine receptor as a possible molecular descriptor. For these methods, clustering with CIMVF shows the more proper result in the case of histamine receptor ligands. With a more concentrated study on the relationship between the molecular vibrational frequency and pharmacological function of a ligand, the vibrational spectrum of a molecule may shed light on the field of ligand-receptor interaction mechanisms.

Acknowledgments

The author would like to express sincere appreciation to Prof. C. H. Choi for the use of his cluster computer and his valuable advice. This work was supported by the 2010 Inje University research grant.

References

1. Bleicher KH, Bohm HJ, Müller K, Alanine AI. Hit and lead generation: beyond high-throughput screening. Nat Rev Drug Discov 2003;2:369–378. PMID: 12750740.
crossref pmid
2. Zheng CJ, Han LY, Yap CW, Ji ZL, Cao ZW, Chen YZ. Therapeutic targets: progress of their exploration and investigation of their characteristics. Pharmacol Rev 2006;58:259–279. PMID: 16714488.
crossref pmid
3. Drews J. Drug discovery: a historical perspective. Science 2000;287:1960–1964. PMID: 10720314.
crossref pmid
4. Cherezov V, Rosenbaum DM, Hanson MA, Rasmussen SG, Thian FS, Kobilka TS, et al. High-resolution crystal structure of an engineered human beta2-adrenergic G protein-coupled receptor. Science 2007;318:1258–1265. PMID: 17962520.
crossref pmid pmc
5. Chien EY, Liu W, Zhao Q, Katritch V, Han GW, Hanson MA, et al. Structure of the human dopamine D3 receptor in complex with a D2/D3 selective antagonist. Science 2010;330:1091–1095. PMID: 21097933.
crossref pmid pmc
6. Shimamura T, Shiroishi M, Weyand S, Tsujimoto H, Winter G, Katritch V, et al. Structure of the human histamine H1 receptor complex with doxepin. Nature 2011;475:65–70. PMID: 21697825.
crossref pmid pmc
7. Medina VA, Rivera ES. Histamine receptors and cancer pharmacology. Br J Pharmacol 2010;161:755–767. PMID: 20636392.
crossref pmid pmc
8. Schwartz JC, Arrang JM, Garbarg M, Pollard H, Ruat M. Histaminergic transmission in the mammalian brain. Physiol Rev 1991;71:1–51. PMID: 1846044.
crossref pmid
9. Hill SJ. Distribution, properties, and functional characteristics of three classes of histamine receptor. Pharmacol Rev 1990;42:45–83. PMID: 2164693.
pmid
10. Hill SJ, Ganellin CR, Timmerman H, Schwartz JC, Shankley NP, Young JM, et al. International Union of Pharmacology. XIII. Classification of histamine receptors. Pharmacol Rev 1997;49:253–278. PMID: 9311023.
pmid
11. Keller A, Vosshall LB. A psychophysical test of the vibration theory of olfaction. Nat Neurosci 2004;7:337–338. PMID: 15034588.
crossref pmid
12. Takane SY, Mitchell JB. A structure-odour relationship study using EVA descriptors and hierarchical clustering. Org Biomol Chem 2004;2:3250–3255. PMID: 15534702.
crossref pmid
13. Godden JW, Xue L, Bajorath J. Combinatorial preferences affect molecular similarity/diversity calculations using binary fingerprints and Tanimoto coefficients. J Chem Inf Comput Sci 2000;40:163–166. PMID: 10661563.
crossref pmid
14. Schmidt MW, Baldridge KK, Boatz JA, Elbert ST, Gordon MS, Jensen JH, et al. General atomic and molecular electronic structure system. J Comput Chem 1993;14:1347–1363.
crossref
15. Keseru GM, Makara GM. The influence of lead discovery strategies on the properties of drug candidates. Nat Rev Drug Discov 2009;8:203–212. PMID: 19247303.
crossref pmid
Fig. 1
The chemical structure of histidine.
gni-10-128-g001.jpg
Fig. 2
Radial tree of corralled intensity of molecular vibrational frequency (CIMVF) clustering using the complete linkage method. Antagonists are tagged with "t" to their chemical names as a prefix, whereas agonists are not. Agonists of histamine receptors except impromidine are located in a cluster (the part in the dotted circle).
gni-10-128-g002.jpg
Table 1.
List of agonists and antagonists used in the present study
PubChem ID Compound name Receptor Function type PubChem ID Compound name Receptor Function type
87653 2-Thiazoleethanamine HRH1 Agonist 3957 Loratadine HRH1 Antagonist
2366 Betahistine HRH1 Agonist 4034 Meclozine HRH1 Antagonist
75919 Demethylbetahistine HRH1 Agonist 4761 Pheniramine HRH1 Antagonist
7741 Betazole HRH2 Agonist 4927 Promethazine HRH1 Antagonist
3077 Dimaprit HRH2 Agonist 5002 Quetiapine HRH1 Antagonist
3692 Imetit HRH3 Agonist 3032915 Burimamide HRH2 Antagonist
126688 Amthamine HRH3,H4 Agonist 2756 Cimetidine HRH2 Antagonist
41376 Impromidine HRH4 Agonist 5282136 Lafutidine HRH2 Antagonist
5227 SKF91488 HR Agonist 3033637 Nizatidine HRH2 Antagonist
2267 Azelastine HRH1 Antagonist 5282450 Pibutidine HRH2 Antagonist
2678 Cetirizine HRH1 Antagonist 3001055 Ranitidine HRH2 Antagonist
2725 Chlorpheniramine HRH1 Antagonist 5105 Roxatidine HRH2 Antagonist
26987 Clemastine HRH1 Antagonist 50287 Tiotidine HRH2 Antagonist
6726 Cyclizine HRH1 Antagonist 9954017 A-349821 HRH3 Antagonist
124087 Desloratadine HRH1 Antagonist 9818903 ABT-239 HRH3 Antagonist
33036 Dexchlorpheniramine HRH1 Antagonist 2366 Betahistine HRH3 Antagonist
21855 Dimetindene HRH1 Antagonist 6422124 Ciproxifan HRH3 Antagonist
3100 Diphenhydramine HRH1 Antagonist 3035746 Iodophenpropit HRH3 Antagonist
667477 Doxepin HRH1 Antagonist 9948102 Pitolisant HRH3 Antagonist
3162 Doxylamine HRH1 Antagonist 3035905 Thioperamide HRH3,H4 Antagonist
3191 Ebastine HRH1 Antagonist 2790 Clobenpropit HRH3 Antagonist
19105 Embramine HRH1 Antagonist 4908365 JNJ-7777120 HRH4 Antagonist
3348 Fexofenadine HRH1 Antagonist 10446295 VUF6002 HRH4 Antagonist
1549000 Levocetirizine HRH1 Antagonist
Table 2.
The molecular similarity between histamine and each ligand molecule in Tanimoto coefficient
Compound Tanimoto coefficient Function type Compound Tanimoto coefficient Function type
Histamine 1 Agonist
Imetit 0.45 Agonist Loratadine 0.13 Antagonist
Burimamide 0.43 Antagonist JNJ-7777120 0.13 Antagonist
Thioperamide 0.41 Antagonist Pitolisant 0.12 Antagonist
Iodophenpropit 0.3 Antagonist Embramine 0.12 Antagonist
Demethylbetahistine 0.3 Agonist Cyclizine 0.11 Antagonist
Ciproxifan 0.3 Antagonist Dimetindene 0.11 Antagonist
Impromidine 0.29 Agonist Meclozine 0.11 Antagonist
Clobenpropit 0.29 Antagonist Nizatidine 0.1 Antagonist
Betahistine 0.27 Antagonist Promethazine 0.1 Antagonist
Amthamine 0.25 Agonist Azelastine 0.1 Antagonist
2-Thiazoleethanamine 0.25 Agonist Cetirizine 0.1 Antagonist
Cimetidine 0.24 Antagonist Levocetirizine 0.1 Antagonist
Betazole 0.22 Agonist Tiotidine 0.09 Antagonist
VUF6002 0.19 Antagonist ABT-239 0.09 Antagonist
Doxylamine 0.18 Antagonist Doxepin 0.09 Antagonist
Pheniramine 0.18 Antagonist Dimaprit 0.08 Agonist
Chlorpheniramine 0.18 Antagonist A-349821 0.08 Antagonist
Dexchlorpheniramine 0.18 Antagonist Pibutidine 0.08 Antagonist
Fexofenadine 0.16 Antagonist SKF91488 0.08 Agonist
Desloratadine 0.14 Antagonist Roxatidine 0.08 Antagonist
Diphenhydramine 0.14 Antagonist Ranitidine 0.07 Antagonist
Clemastine 0.13 Antagonist Lafutidine 0.07 Antagonist
Ebastine 0.13 Antagonist Quetiapine 0.07 Antagonist

The Tanimoto coefficients between histamine and other ligand molecules are listed in descending order.



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