alexa Identification of Novel Drug Leads for Receptors Implicated in Migraine from Traditional Ayurvedic Herbs Using in silico and in vitro Methods | Open Access Journals
ISSN: 2329-6895
Journal of Neurological Disorders
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Identification of Novel Drug Leads for Receptors Implicated in Migraine from Traditional Ayurvedic Herbs Using in silico and in vitro Methods

Preenon Bagchi1,3,5*, Venkatramana DK2, Mahesh M1,5, Somashekhar R1,3,5 and Ajit Kar4,5
1Azyme Biosciences Pvt. Ltd., Bangalore, Karnataka, 560069, India
2Bhat Biotech India Pvt. Ltd., Bangalore, Karnataka 560100, India
3Career Point University, Kota, Rajasthan 324005, India
4Satsang Herbal Research Laboratory, Satsang, Deoghar, Jharkhand 814116, India
5Sarvasumana Association, Bangalore, Karnataka, 560069, India
Corresponding Author : Preenon Bagchi
Azyme Biosciences Pvt. Ltd., Bangalore
Karnataka, 560069, India
Tel: 00919886274603
E-mail: [email protected]
Received August 06, 2014; Accepted October 19, 2014; Published October 21, 2014
Citation: Bagchi P, Venkatramana DK, Mahesh M, Somashekhar R, Kar A (2014) Identification of Novel Drug Leads for Receptors Implicated in Migraine from Traditional Ayurvedic Herbs Using in silico and in vitro Methods. J Neurol Disord 2:185. doi: 10.4172/2329-6895.1000185
Copyright: © 2014 Bagchi P, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Abstract

Background: Migraine is a chronic neurological disorder characterized by headaches along with several physiological and autonomic nervous system symptoms. Research suggests that migraine is a result of multi-gene mutation in combination with psycho-social and environmental factors.
Methods: Mutated mammalian serotonin hydroxytryptamine receptor 2 (HTR2) implicated as factor causing migraine were retrieved from the National Centre for Biotechnology Information (NCBI), its 3D structure were determined by homology modelling. The 3D structures of phyto-compounds (from Ayurvedic herbs) were retrieved from various databases. The pharmacophore hypothesis was generated for the existing ligands and the phytocompounds were screened against the generated pharmocophoric hypothesis. Ligands were shortlisted based on their fitness score. The selected phytocompounds were screened against HTR2 receptor.
Results: The phytocompound having the best docking score and most interactions with the receptor are validated using receptor-ligand binding assay studies with HTR2 receptor in-vitro.
Conclusion: Phytocompounds selected as per receptor-ligand binding assay studies.

Keywords
Migraine; Serotonin; Ayurveda; Modeling; Pharmacophore; Docking; Rapid eye movement (REM); Binding assay
Background
The current manuscript is a continuation work of manuscript titled “Selecting the best ligand for Migraine Protein 5-hydroxytryptamine (serotonin) receptor 2A (HT2A) from the Compounds of Valeriana wallichii, Asparagus racemosus and Acorus calamus” by Somashekhar R, Bagchi P et al., 2014.
Rapid eye movement sleep behavior disorder (RBD) is a sleep disorder that involves abnormal behaviour during rapid eye movement (REM) sleep [1]. REM is the stage of sleep during which most vibrant dreaming occurs. The loss of motor inhibition leads to a wide spectrum of behavior during sleep. Migraine is a type of REM disorder [2]. Studies suggest that genetics, prenatal care with environment, combined with psychological and social factors are important causes of migraine. Research suggests that migraine is caused by actions of several mutated genes [3]. Migraine is the most frequent neurological disorder in the adult population worldwide. Headache is the primary clinical symptom and it has been associated with a hereditary or dependence of neurovascular reactions to cyclic changes in the central nervous system. Amongst the many neurotransmitters in the brain, the serotonergic (serotonin, 5-HT) system from the brainstem raphe nucleus has been most believably implicated in migraine pathophysiology [4]. The mammalian HT2 receptor is the main excitatory receptor subtype among the (G protein-coupled receptor) GPCRs for serotonin. HTR2 may also have an inhibitory effect [5,6] on certain areas such as the visual cortex and the orbitofrontal cortex. Serotonin (5-hydroxytryptamine (5-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 and is indicated as causal factor for several allied neuronal disorders [7,8].
Mutation in mammalian serotonin hydroxytryptamine receptor 2 (HTR2), implicated as factors causing migraine, is taken in this study [3,4]. The use of phytochemicals as novel, potential lead drug molecules for HTR2, a GPC receptor was tested in silico and in vitro in this study by matching the pharmacophoric features of a known ligand myristicin with the phytocompounds from Ayurvedic herbs (The psychotropic property of the herbs used in this work are based on practical studies carried out at Satsang Herbal Research Laboratory, Satsang, Deoghar, India) (Table 1). Myristicin is an agonist pertaining to HTR2 receptor [9]. It is a natural product isolated from parsley oil [10]. This volatile oil, myristicin, comprises a mixture of allylbenzene derivatives and terpines [11].
Methodology
Predicting the 3D structure of the receptors
The amino acid sequence of the HTR2 receptor was retrieved from the National Center for Biotechnology Information (NCBI). Using Basic Local Alignment and Search Tool (BLAST) search engine against Protein Data Bank (PDB) the homologous templates for the receptor was selected and their crystal structure was downloaded from PDB. Using these homologous templates, the 3D structure of the receptor was generated by modeler [12].
Model quality assessment
Modeller [12] generated five models. Using Structural Analysis and Verification Server (SAVES)’s PROCHECK Module, (this stereochemical check was applied to verify if the φ and ψ dihedral angles were in available regions of the Ramachandran plot) the best protein model was selected [13].
Phyto-compounds from traditional ayurvedic herbs
Ligand preparation
The 3d structures of the above phyto-compounds were downloaded from PubChem, a database of chemical molecules maintained by the NCBI and various other online databases.
Generating phase database
Now using Application→Phase→Generate Phase Database module of Maestro software phase database of the phyto-compounds was done [14].
Selection of ligands for HTR2 receptor
Ligand-based pharmacophore model was selected by extracting the common features of the three-dimensional structures of compounds which are known to interact with the target protein (known ligand). Known ligands were loaded in the Maestro workspace and by using Applications→Phase→Create Hypothesis module pharmacophore features of the known ligands were noted [14].
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 and energy minimization [14].
Binding site generation: The binding site position of the protein was determined by SiteMap module of Maestro [14].
Ligand preparation: The ligands were selected in Maestro workspace. Using ligprep, the ligands were minimised prepared for docking studies. 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 [14].
Receptor grid generation: The receptor was loaded in workspace. Using Glide → Receptor Grid Generation the binding site region of the receptor was specified and the receptor was prepared for docking.
Glide docking: Using module Glide → Ligand Docking module of Maestro the receptor was docked with the selected ligands [14].
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 [14].
Generation of stable cell line expressing HTR2
Synthetic HTR2 gene (Geneart, Germany) was cloned into pcDNA3.1 vector (Invitrogen) between BamHI and XbaI sites. Clones were confirmed by sequencing. Human Embryonic Kidney (HEK 293) cell line (National Centre for Cell Sciences, Pune, India) were transfected with pcDNA3.1-HTR2 and grown in the presence of1mg/ ml Geneticin (G418).Cells resistant to 1mg/ml G418 were selected, expanded and used for receptor ligand binding assay. The expression of HTR2 in stable cell line was confirmed by RT-PCR using specific primers.
Receptor-ligand binding assay studies
Binding efficacy of the phytocompounds colchicine and hypophyllanthanthin with the HTR2 receptor was tested in-vitro by measuring agonist stimulated calcium signalling using Fluo-4 Direct calcium assay kit (Invitrogen).5HT (Sigma), a known agonist of HTR2 was used as positive control for the test. 1×104 cells in 50 μl of DMEM (Dulbekos Modified Eagle’s Medium)-10% FBS (Fetal Bovine Serum) were seeded in 96 well tissue culture plate and grown overnight at 37°C/5% CO2 in humidified incubator. 50 μl of 2X Fluo-4 direct calcium reagent loading solution was added and incubated for 60 seconds at 37°C. Different concentrations of agonist were added in duplicate wells and incubated for 4 hours at 37°C. Fluorescence was measured with excitation at 494 nm and emission at 516 nm using Flurometer.
Results and Discussion
The HTR2 receptor was selected from the NCBI database (Table 2). The 3D structure of HTR2 receptor was modelled by Modeller [12].
Predicting the 3D structure of HTR2 receptor
The 3d structure of HTR2 receptor was modeled since the crystal structure of HTR2 receptor was not available in the PDB. Using BLAST search against PDB templates or homologous proteins related to HTR2 were selected.
This best aligned template is taken for homology modeling studies by using modeler (Table 2). Ramachandran plot analysis of the best generated 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 3, Figures 1 and 2). This model was selected as the best model since it had most residues in the favoured region.
The three dimensional structure provides valuable insight into molecular function and also enables the protein–protein interaction to be analyzed.
Pharmacophore studies
Ligand-based pharmacophore models are selected by extracting the common features of the three-dimensional 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 [15].
The pharmacophore features of all known 5-HT ligands, were generated but none (except myristicin) of the phytocompounds showed any common feature with the pharmacophore of the known 5HT ligands.
Myristicin
Myristicin is a known serotonin agonist, psychoactive drug, acting as an anticholinergic, and gets metabolised to 3-methoxy-4,5- methylenedioxyamphetamine (MMDA). Also, it has a weak monoamine oxidase inhibitor action and with elemicin that gets metabolised to an amphetamine-like compound which has hallucinogenic effects [16,17].
Myristicin was loaded in the Maestro workspace. Phase hypothesis gave pharmacophore features of Myristicin as A1, A2, A3, H4, H5 and R6 (Figure 3).
This pharmacophore features matched with the compounds in Table 4.
The above compounds were docked with HTR2 receptor.
Glide ligand docking
Sitemap module was used to determine the binding site residues of the HTR2 receptor and as per the output sitemap_site_2 with SiteScore 1.004 (2nd highest score) and size 231 was used to determine the binding site of the modelled protein.
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.
The binding site region of the receptor was assigned and grid for the receptor was generated using Receptor Grid Generation module.
The HTR2 receptor was docked with colchicine and hypophyllanthin (Figure 4 and Table 5) [18,19].
ADME screening
ADME is an acronym for absorption, distribution, metabolism, and excretion. QikProp is a quick, accurate, easy-to-use ADME prediction program designed by Professor William L. Jorgensen. QikProp predicts physically significant descriptors and pharmaceutically relevant properties of molecules [14].
QikProp generated the following output (Tables 6 and 7) [19]:
Receptor-ligand binding assay studies
HEK cells were transfected with pcDNA3 containing HTR2 (Figures 5 and 6).
RT-PCR from total RNA isolated from stable cell line HEK- HTR2 confirmed the presence of HTR2 gene in the stable cell line. A band of 301bp was observed as expected (Figure 7).
Binding assay studies was done with HTR2 receptor with 5HT (known agonist for HTR2 receptor) [17], Colchicine and Hypophyllanthin using calcium assay buffer (Fluo-4 Direct reagent) [20].
Fluorometer reading at excitation at 494 nm and emission at 516 nm (done at Natural Remedies Pvt. Ltd., Bangalore, India) is given in Figure 8 and Table 8.
Conclusion
Phytocompounds colchicine and hypophyllanthin binds to HTR2 receptor and exhibits activity. Colchicine exhibits maximum activity at 10μm concentration and hypophyllanthin exhibits maximum activity at 100μm. Hence, the phytocompounds colchicine and hypophyllanthin proves agonists to HTR2 receptor and further in vivo and clinical trials should be done for establishing these as drugs for migraine and other neurological disorders.
Future Perspective
In this work the compounds colchicine and hypophyllanthin are already tested HTR2 agonist by in silico and in vitro methods. Currently there is no animal model for migraine. Based on the present results, the authors would develop an animal model to test the compounds colchicine and hypophyllanthin to test in vivo.
Acknowledgements
1) In silico work was performed at Department of Bioinformatics, SRM University, Chennai, India – which is being gratefully acknowledged.
2) Natural Remedies Pvt. Ltd., Bangalore, India.
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