Keywords

Migraine, sleep disorder, serotonin receptor, G-protein-coupled receptor, myristicin, homology modeling, docking

Inroduction

HT2A receptor

The mammalian HT2A receptor is the main excitatory receptor subtype among the (G protein-coupled receptor) GPCRs for serotonin (HT) is a subtype of the HT2 receptor [1, 2]. HT2A may also have an inhibitory effect [3] on certain areas such as the visual cortex and the orbitofrontal cortex. Serotonin (5-hydroxytryptamine (HT)1) is a major neurotransmitter that is involved in multiple physiological functions such as the control of endocrine secretion, motor behavior, mood, pain, sleep, thermoregulation, and appetite. Also, a large number of psychoactive drugs, including non-classical antipsychotic drugs, hallucinogens, anxiolytics, and anti-depressants, mediate their action at least in part through activation of HT2 receptors [3-6]. These functions are mediated by a large number of receptors.

Migraine, a sleep disorder, triggered by abnormal sleep is caused by instability of serotonin and a lowering of dopamine levels [7, 8, 9].

Mutations in the gene, serotonin, are associated with susceptibility to migraine, schizophrenia [2] and obsessive-compulsive disorder, and are also associated with response to the antidepressant citalopram in patients with major depressive disorder (MDD) [10].

The major breakthrough restoring interest in the role of HT in migraine (and other neurological disorders) was the identification of numerous HT receptor subtypes and their extensive impact on multiple neurotransmitters and behaviours [7 – 11].

In this work HT2A receptor is virtually screened with the compounds of Valeriana wallichii, Asparagus racemosus and Acorus calamus.

Valeriana wallichi

Valeriana wallichii is native to India (Himalayas). It is an important Indian medicinal plant & is used for its benefits in calming down the nervous system. It relieves stress and anxiety and also fights depression. The compounds identified from the plant are acetoxy valerenic acid, acevaltrate, baldrinal, bornyl acetate, bornyl isovalerate, fenchene, β- sitosterol, calarene, homobaldrinal, isovaltrate, valeranone, valerenal, valerenic acid, valepotriate, valtrate, valtroxal and xanthorrhizol [12].

Acorus calamus

Acorus calamus or sweet flag has been long known for its medicinal value and is cultivated in India for this reason. The rhizome possesses anti-spasmodic, carminative and anthelmintic properties and is used for the treatment of epilepsy and mental ailments. The compounds identified from the plant are 1α, 2β, 3γ, 19α-tetrahydroxyurs-12en-28- oicacid-28-O{-β-D-glucopyranosyl (1→2)} β- Dgalactopyranoside, 2,3-dihydro-4,5,7-trimethoxy-1- ethyl-2-methyl-3-(2,4,5-trimethoxy phenyl)indene, 2,4,5-trimethoxy benzaldehyde, 2,6-diepishyobunone, 3β, 22α, 24, 29-tetrahydroxyolean-12-en-3-O-{-β-Darabinosyl(1→3)}-β-D-arabinopyranoside,4,5,8- trimethoxyxanthone-2-O-β-D-glucopyranosyl(1→2)-O- β-D-galactopyranoside, acoradin, acoragermacrone, acoramone(1,2,4 –trimethoxy-5(2-propanoyl) benzene, β-sitosterol, Calamusenone, cis-asarone(cis-1,2,4 – trimethoxy-5(2- propenyl) benzene, galangin, γ-cisasarone(cis-1,2,4 –trimethoxy-5(2- propenyl) benzene, isoeugenol methyl ether, isocalamendiol limonene, preisocalamendiol, shyobunone thujane and Z-3- (2,4,5-trimethoxy phenyl)-2-propenal. [13].

Asparagus racemosus:

Asparagus racemosus’s tubers are candied and eaten as a sweetmeat. It is one of the constituent of medicated oils for external application in nervous and rheumatic affections. The important constituents of Asparagus racemosus are sarsasapogenin, shatavarin, rhamnose, asparagamine and racemosol [14, 15].

Methodology

Predicting the 3D structure of HTR2A receptor

The HT2A sequence with accession number NP_001159419 was taken the National Center for Biotechnology Information (NCBI). Using Basic Local Alignment and Search Tool (BLAST) search engine against Protein Data Bank (PDB) the following template was selected and its crystal structure was downloaded from PDB.

  • 2VT4A: Turkey Beta1 Adrenergic Receptor With Stabilising Mutations And Bound Cyanopindolol [E-value-8e-28]
  • 2YOOA: Turkey Beta1 Adrenergic Receptor With Stabilising Mutations And Bound Partial Agonist Dobutamine [E-value-2e-28]
  • 3SN6R: Crystal Structure Of The Beta2 Adrenergic Receptor-Gs Protein Complex [E-value-2e-26] Now, using the above template, the 3D structure of HT2A protein was generated by modeller [16].

Model Verification

Modeller generated five models. Using SAVES server’s Ramachandran Plot Module, the best protein model was selected [17].

Ligand preparation

The 3d structures of the components of Valeriana wallichii, Asparagus racemosus and Acorus calamus were drawn using chemsketch [18] and saved as *.mol file.

Generating phase database

Now using Application→Phase→Generate Phase Database module of Maestro software phase database of the compounds of Valeriana wallichii, Asparagus racemosus and Acorus calamus was done [19].

Selection of ligands for HT2A receptor

Ligand-based pharmacophore model was selected by extracting the common features of the threedimensional structures of compounds which are known to interact with the target protein (known ligand) [17]. Known ligand, myristicin [20] were loaded in the Maestro workspace and by using Applications→Phase→Create Hypothesis module pharmacophore features of the known ligands were noted [21].

Docking

Protein Preparation

The modeler generated protein is not suitable for immediate use in docking or other molecular modeling calculations. By using Protein Preparation Wizard of Maestro9.1 the modeler generated protein was uploaded for optimization & energy minimization [22].

Active Site Generation

The active site position of the protein was determined by SiteMap module of Maestro [23].

Ligand Preparation

The above identified ligands were prepared using Ligprep module of Maestro. The ligands were opened in the workspace and saved as a single database file. This file was opened in LigPrep. LigPrep is tool to prepare high quality 3D structure for large number of molecules taking input as 2D or 3D structures and giving output as a single, low energy 3D structure [24].

Induced Fit Docking

Using module Workflows→Induced Fit Docking module of Maestro the receptor HT2A was docked with the identified ligands [25].

ADME screening

ADME is an acronym in pharmacokinetics and pharmacology for absorption, distribution, metabolism, and excretion. Using QikProp module the ADME properties of the above ligands was determined [26].

Results & discussion

Predicting the 3D structure of HT2A receptor

The 3d structure of HT2A protein was modelled. Using BLAST search against PDB templates or homologous proteins related to HTR2A were selected.

This best aligned template is taken for homology modeling studies by using modeler and Ramachandran plot of this model gave 92.7% residues in the core region, 5.3% in allowed region, 0.6% in generously allowed region and 1.4% disallowed region (Table 1, Fig. 1, 2).

Table 1

Values of HTR2A protein obtained in favoured, allowed and disallowed region using Ramachandran Plot (SAVES server).

Number of residues in most favoured region Number of residues in additional allowed region Number of residues in generously allowed region Number of residues in dis-allowed region
Model 1 314 (88.0%) 33 (9.2%) 7 (2.0%) 3 (0.8%)
Model 2 319 (89.4%) 32 (9.0%) 4 (1.1%) 2 (0.6%)
Model 3 331 (92.7%) 19 (5.3%) 2 (0.6%) 5 (1.4%) (selected)
Model 4 319 (89.4%) 31 (8.7%) 2 (0.6%) 5 (1.4%)
Model 4 328 (91.9%) 20 (5.6%) 3 (0.8%) 6 (1.7%)

Fig 1: Ramachandran Plot of the selected best HTR2A model.

Fig 2: 3d Structure of the selected best HT2A model.

The three dimensional structure provides valuable insight into molecular function and also enables the protein–protein interaction to be analyzed.

Ligand-based pharmacophore models are selected by extracting the common features of the threedimensional structures of the known ligands. To do this, possible conformers of compounds should be previously enumerated.

Then, we superpose our target compounds by overlapping the three-dimensional structures’ common substructures as molecular graphs among the other parts of compounds. So, in this method, since we do not have to enumerate all the conformers of a compound, we usually save much computational time by ligand-based pharmacophore modeling [27].

Known ligand Myristicin: Myristicin acting as HT2A inhibitor was loaded in the Maestro workspace. Applications→Phase→Create Hypothesis gave pharmacophore features of Myristicin as A1, A2, A3, H4, H5, R6 (Fig. 3, Table 2) [A=Acceptor, H=Hydrophobic, R=Aromatic Rings].

Fig 3: Pharmacophore features of Myristicin.

This pharmacophore features matched with the following compounds

Table 2

Ligands which matches with the pharmacophore of Myristicin and their fitness score

Identified ligands Fitness score Plant
Z-3-(2,4,5-trimethoxy phenyl)-2-propenal 1.831 Acorus calamus
Acoradin 1.655 Acorus calamus
4, 5, 8-trimethoxyxanthone-2-O-beta-D-glucopyranosyl(1-2)-O-beta-D-galactopyranoside 0.831 Acorus calamus

Induced Fit Docking

Using Protein Preparation Wizard of Maestro, our best modelled protein was uploaded and optimized and minimized. SiteMap was used to determine the active site region and as per the output sitemap_site_2 with SiteScore 1.004 (2nd highest score) and size 231 was used to determine the active site of the modelled protein. the sitemap_site_1 was not taken since its size was equivalent to the size of the protein (424) and hence contains non-specific active site residues.

SER305, ALA306, CYS313, LYS316, PHE299, TYR303, VAL291, PRO293, ALA290, THR302, LEU298, ASN300, LYS320, GLU317, ASN376, ASP375, ALA372, LYS301, ASN318, ASP378, PRO321, TYR286, ILE174, TYR170, THR173, MET166, THR106, ARG89, LEU172, ILE168, THR169, MET166, THR106, PHE109, ILE165, ALA192, ALA195, ARG191, LYS107, LEU110, SER104.

Using Ligprep, ligands were prepared. Using Induced fit docking, prepared protein and ligand was uploaded docking was initiated. As per the output of induced fit docking (Fig. 4, Table 3).

Fig 4: Docking results of HT2A receptor with the selected ligands

Table 3

Docking results of HT2A receptor with the selected ligands

Ligand Docking score/ Doner Distance Interaction
glide g score (kcal/mol) in
Å
Acoradin -9.205 ASN460 2.461 ASN460(OH)…O(UNK)
ASN110 2.086 ASN110(OH)…O(UNK)
4, 5, 8 – trimethoxy xanthone- 2 – O – beta – D -glucopyranosyl(1-2)-O-beta-D-galactopyranoside -13.112 ASN110 2.007 ASN110(OH)…O(UNK)
ASN465 1.808 ASN465(OH)…O(UNK)
ASN462 1.774 ASN462(OH)…O(UNK)
1.765
ASN460 2.404 ASN460(OH)…O(UNK)
2.207
Z-3-(2,4,5-trimethoxy phenyl)-2-propenal -6.924 ASN110 2.082 ASN110(OH)…O(UNK)

Comparing both the results of pharmacophore and induced fit docking, we find the compound Z-3-(2,4,5- trimethoxy phenyl)-2-propenal had the highest fitness score of 1.831 but the least docking score -6.924, again compound 4, 5, 8 – trimethoxy xanthone- 2 – O – beta – D -glucopyranosyl(1-2)-O-beta-D-galactopyranoside has the highest docking score of -13.112 but the least fitness score 0.831, but the compound acoradin has the second best fitness score 1.655 and second best docking score -9.205. So, the compound acoradin is selected as the best ligand and further in-vitro and invivo studies can be done with this compound with the HT2A receptor.

ADME screening

QikProp generated the following output (Table 4, 5)

Table 4

Principal descriptors calculated by Qikprop simulation [28] (Range 95% of Drugs)

Lead molecules Molecular weight a (g/mol) Molecular volume b PSA c HB d donors HB e acceptors Rotatable bonds f
(Å)
acoradin 416.513 1352.876 41.167 0 4.5 6

A * indicates a violation of the 95% range.

a Molecular weight of the molecule

b Total solvent-accessible volume in cubic angstroms using a probe with a radius of 1.4 Å

c Van der Waals surface areas of polar nitrogen and oxygen atoms

d Estimated number of hydrogen bonds that would be donated by the solute to water molecules in an aqueous solution. Values are averages taken over a number of configurations, so they can be non-integer

e Estimated number of hydrogen bonds that would be accepted by the solute from water molecules in an aqueous solution. Values are averages taken over a number of configurations, so they can be non-integer

f Number of rotatable bonds

Table 5

Physiochemical descriptors calculated by Qikprop simulation [28](Range 95% of Drugs)

Lead molecules QP log P(o/w)a QP log Sb QP PCacoc QP log HERGd QP PMDCKe % Human oral absorptionf
acoradin 4.617 -8.033 * 9906 -4.863 5899 100

A * indicates a violation of the 95% range.

An M indicates MW is outside training range.

a QP log P for octanol/water (−2.0, – 6.5)

b Predicted aqueous solubility, log S. S in mol dm–3 is the concentration of the solute in a saturated solution that is in equilibrium with the crystalline solid (−6.5, – 0.5)

c Apparent Caco-2 permeability (nm/s) (<25>500 great)

d log HERG, HERG K+channel blockage (concern below −5)

e Apparent MDCK permeability (nm/s) (<25>500 great)

f % Human oral absorption in GI (±20%) (<25% is poor)

Conclusions

As per the above results compound acoradin having fitness score 1.655 and docking score -9.205 is selected as the best ligand for the target HT2A. Also, 100% Human oral absorption makes this ligand an effective drug candidate. Hence, this ligand is selected for further in-vitro & in-vivo studies to prove its efficacy as a potential drug in treating the disorder Migraine.

Acknowledgement

SRM University, Chennai, India.