1. INTRODUCTION
About 70 million people around the globe suffer from epilepsy and 12 million reside from India [1]. Epilepsy exists as a chronic disorder causing unpredictable, recurrent seizures that have a serious effect on mental and physical functions. Seizures are expressed when there is an accumulation of multiple neurons caused due to neurons over activity which are released rhythmically [2]. Due to an imbalance transmission between excitatory glutamatergic and inhibitory gamma-aminobutyric acid ergic (GABAergic), seizures are caused [3]. Usually rather than the adult brain, the immature brain is more susceptible to seizures. These development processes are influenced by synaptic reduction, change in density of neurotransmitter receptors, modification of GABAA receptor or activity from an immature brain to mature nervous system and structure, and changes in the function of glutamate receptors [4].
Before the occurrence of seizures, few people experience different feelings, sensations, and changes in behavior. Some general symptoms that epilepsy patients endure are loss of consciousness, numbness, memory lapses, jerking movements, sweating, biting of the tongue, difficulty in breathing, and the physical injuries that they may face during seizures are broken bones and uncontrolled bladder [5]. Many pieces of evidence have proved that acquired epilepsies are influenced by the genetic effect [6]. Symptomatic epilepsies are proven to show changes in the epileptic brain. The variations that are developed are altered dendritic morphology, dysplastic neurons, and electrophysical evidence of abnormal synchronization and abnormal networks [7].
SLC6A1 encodes GAT1 protein. It is a voltage-dependent GABA transporter that is responsible for the reuptake of GABA from the synapse [8]. GABA is a main inhibitory neurotransmitter that counters balance neuronal excitation in the brain and is directly responsible for the regulation of muscles. Any disruption in regulation can cause seizures [9]. In a few studies, findings suggest that SLC6A1 can cause specific epilepsy syndrome myoclonic-astatic epilepsy that causes early abnormal development. These abnormalities can be caused due to GAT1 GABA transport [10].
The study aims were to explain the silico modeling of the low sodium-dependent dopamine transporter. The 3D model was generated by using MODELLER 9.21 modeling software and was validated by using Procheck. By using autodock4.2, protein-ligand binding studies were carried out on modeled protein.
2. METHODOLOGY
Homology modeling also known as comparative study of protein is used to build a target molecule from the unknown sequence by an online tool of Basic Local Alignment Search Tool (BLAST) search. Homologs are found during this analysis with the help of the BLAST, which helps to find the homology sequence of the target in the template. The BLAST analysis shows sequence similarities and identities of the target with the template [11]. Finally, the protein is created for further computational analysis. Sodium- and chloride-dependent GABA transporter 1 (Uniprot accession number: P30531) were taken from species Homo sapiens which was retrieved from the UniProtKB database. To select the template, a search was performed in BLAST [12]. X-ray structure of Drosophila dopamine transporter in complex with cocaine (4XP4) from species Drosophila melanogaster was selected. Using Modeller 9.21, three-dimensional structures were generated. Then respective templates were retrieved from protein databases like Protein Data Bank (PDB). While choosing the template, we should consider the sequence identity and resolution of the template. If the parameters are high, it would be good enough to allow structural and functional research in the resulting model [13].
To produce an adequate model, MODELLER 9.21 was used which is an automated approach to homology modeling by appropriate spatial restraints [14]. By using Sequence alignments, protein and template sequences were carried out using platforms like ClustalX and ClustalW is shown in Figure 1 [15]. The homology models of the selected protein were constructed using modeler programs like MODELLER 9.21. After manual reviewing of the alignment, the input file in MODELLER 9.21 has to match the query and template sequence, 20 models were generated [16, 17]. The lowest value of the Modeller Objective Function is decided as the best model. Software like PROCHECK analyzes the stereochemical quality of the specific model and which can be used for further structural or functional study. PROCHECK develops a Ramachandran plot which explains each residue organizing the in-depth calculation of Psi/Phi angles and the backbone conformation of the models. The root mean square deviation (RMSD) was calculated by superimposing (4XP4) specific model to access the accuracy and reliability by using SPDBV [18, 19].
3. DOCKING METHODOLOGY
3.1. Active Site Identification
The active site prediction was conducted in Tripo’s Sybyl 6.7. It showed three active site pockets. The amino acids present in one pocket are Leu136, Tyr139, Tyr140, Gln291, Phe294, Ser295 Tyr296, Leu300, Ser396, Ala455, Ser456, Leu460, Tyr60, Ala61, Gly63, Gly65, and Asn66.
A total of 22 natural compounds were retrieved from NCBI. Using Sybyl 6.7, all the molecules were sketched and minimized by adding Gasteiger–Huckel charges which are then saved in mol2 format. Molecular docking studies were executed on all the natural compounds separately by using the AutoDock4.2 program, Lamarckian Genetic Algorithm, and empirical free energy function was applied. The modeled Solute carrier family 2, facilitated glucose transporter member protein was loaded and hydrogens were added and saved in PDBQT format. Subsequently, the ligand was loaded and conformations were set and saved in PDBQT format. AutoGrid was used for selecting and calculating grid parameters. A grid-point spacing of 0.375 Å was applied and a grid map with 60 × 60 × 60 points was used for all the dockings. Coordinates X, Y, and Z were taken on the basis of the amino acids present in the active site predicted in the Sybyl6.7 biopolymer module. To run the Autodock, default parameters were used [20–22].
4. RESULTS AND DISCUSSION
4.1. Homology Modeling and Model Evaluation
The current study reports that the template protein (PDB ID: 4XP4) having a high degree of homology with P30531 protein was used as a template with a good atomic resolution of its crystal structure. The target sequence sodium- and chloride-dependent GABA transporter 1 (Uniport accession number: P30531) was retrieved from Homo sapiens species having 599 amino acids. By using BLAST, the template model PDB ID 4XP4 was identified and then the structure was modeled using Modeller9.21. The generated structure was checked using the protein structure and by PROCHECK. The secondary structure of the modeled protein is shown in Figure 2 and the Ramachandran plot is shown in Figure 4. By using SPDBV, RMSD was calculated for template and generated model. Both the models were loaded and superimposed using the alpha carbon and RMSD was calculated. RMSD value was 0.79 Å, which shows that the generated model has a similarity to the template (Fig. 3).
4.2. Molecular Docking Results
The ultimate goal of molecular docking was used for calculation of the protein–ligand interactions. It is an efficient method to predict potential ligand interactions. In this study, the native plant secondary metabolites (ligands) have been identified as Solute carrier family 2, facilitated glucose transporter members’ inhibitors. To assign the best binding conformation, AutoDock4.2 uses (genetic algorithm) binding free energy assessment. Additionally, the activity of docked ligand molecules was compared with the standard drugs which were used as controls. A total of 22 natural compounds were docked against modeled GABA transporter.
Among them, Morusin and Gallocatechin showed good interactions and lower free energy values, indicating them to be more favored thermodynamically. When compared with standard drugs, i.e., Seletracetam, Carisbamate, Brivaracetum, and Valrocemide, Morusin exhibited the highest binding energy. In Table 1 and Figure 5, the natural compounds were described with their corresponding interactions and binding energies. While the standard drugs which were used as controls were represented with their corresponding interactions and binding energies in Table 2 and Figure 6.
 | Figure 1: Alignment sequence of sodium- and chloride-dependent GABA transporter 1 and template 4XP4. [Click here to view] |
 | Figure 2: The cartoon model of sodium- and chloride-dependent GABA transporter 1(P30531). [Click here to view] |
 | Figure 3: Superimposed model of sodium- and chloride-dependent GABA transporter 1 (P30531) and template protein (4XP4). [Click here to view] |
 | Figure 4: Ramachandran plot of the modeled protein sodium- and chloride-dependent GABA transporter 1(P30531) exhibited 93.1% amino acid residues in the most favored region. In the allowed region, there are 33 amino acid residues present and there are no residues in the disallowed region. [Click here to view] |
 | Table 1: Protein–ligand interactions, binding energy of 22 natural compounds with the modeled protein. [Click here to view] |
 | Figure 5: Molecular docking interactions of 22 natural compounds against GABA protein (numbers are the same as Table 1). [Click here to view] |
.png) | Table 2: Protein–ligand interactions, with binding energy of four standard drugs. [Click here to view] |
 | Figure 6: Molecular docking interactions of four standard drugs (numbers are the same as Table 1). [Click here to view] |
5. CONCLUSION
For the study of Epilepsy mechanisms, sodium- and chloride-dependent GABA transporter 1 protein could be a potential target. Protein modeling and molecular docking studies were performed on sodium- and chloride-dependent GABA transporter 1 protein (SLC6A1). The modeled protein showed more than 90% of amino acid residues in the core region and molecular docking studies showed good binding energy and interactions with modeled protein when compared to already existing drugs. The study indicates natural compounds may be useful for the potential drug candidates for the treatment of epilepsy. With more enhanced drug designing, we could have more natural drugs which could be a wise treatment for epilepsy.
CONFLICT OF INTEREST
None
REFERENCES
1. Amudhan S, Gururaj G, Satishchandra P. Epilepsy in India I: epidemiology and public health. Ann Indian Acad Neurol 2015;18(3):263–77. CrossRef
2. Liao J, Tian X, Wang H, Xiao Z. Epilepsy and migraine—are they comorbidity? Gen Dis 2018;5(2):112–8. CrossRef
3. Fisher RS, Van Emde BW, Blume W, Elger C, Genton P, Lee P. Epileptic seizures and epilepsy: definitions proposed by the International League against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 2005;46(4):470–2. CrossRef
4. Sanchez RM, Jensen FE, Maturational aspects of epilepsy mechanisms and consequences for the immature brain. Epilepsia 2001;42(5):577–85. CrossRef
5. Robert SF, Acevedo S, Arzimanoglou A, Bogacz A, Helen JC. A practical clinical definition of epilepsy. Epilepsia 2014;55(4):475–82. CrossRef
6. Berkovic SF, Andermann F, Andermann E, Gloor P. Concepts of absence epilepsies: discrete syndromes or biological continuum. Neurology 1987;37(6):993–1000. CrossRef
7. Armstrong DD. The neuropathology of temporal lobe epilepsy. J Neuropathol Exp Neurol 1993;52(5):433–43. CrossRef
8. Durkin MM, Smith KE, Borden LA, Weinshank RL, Branchek TA, Gustafson EL. Localization of messenger RNAs encoding three GABA transporters in rat brain: an in situ hybridization study. Mol Brain Res 1995;33(1):7–21. CrossRef
9. Carvill GL, McMahon JM, Schneider A, Zemel M, Myers CT, Saykally J, et al. Mutations in the GABA transporter SLC6A1 cause epilepsy with myoclonic-atonic seizures. Am J Human Genet 2015;96(5): 808–15. CrossRef
10. Treiman DM, GABAergic mechanisms in epilepsy. Epilepsia 2001;42(3):8–12. CrossRef
11. Patrick T, Clifford JT. Deep homology in the age of next-generation sequencing. Philos Trans R Soc Lond B Biol Sci 2017;372(1713):20150475. CrossRef
12. Eswar N, John B, Mirkovic N, Fiser A, Ilyin VA, Pieper U, et al. Tools for comparative protein structure modeling and analysis. Nucleic Acids Res 2003;31(13):3375–80. CrossRef
13. Laskowski R, Jab?o?ska J, Pravda L, Va?eková RS, Thornton JM. PDBsum: structural summaries of PDB entries. Protein Sci 2018;27(1):129–34. CrossRef
14. Bohnuud T, Luo L, Wodak SJ, Bonvin AM, Weng Z, Vajda S, et.al. A benchmark testing ground for integrating homology modeling and protein docking. Proteins 2017;85(1):10–6. CrossRef
15. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, et al. Clustal W and Clustal X version 2.0. Bioinformatics. 2007;23:2947–48. CrossRef
16. Rowan H, David KB, Michael G, Ozlem TB. PRIMO: an interactive homology modeling Pipeline. PLoS One 2016;11(11):e0166698 CrossRef
17. Muhammad WS, Adeel R, Nazia N, Ahmed AQ, Nisar A. Homology modeling and docking analysis of ßC1 protein encoded by Cotton leaf curl Multan betasatellite with different plant flavonoids. Heliyon 2019;5(3):e01303. CrossRef
18. Thompson JD, Gibson TJ, Higgins DG. Multiple sequence alignment using ClustalW and ClustalX, current protocol in bioinformatics. Curr Protoc Bioinformatics 2002;2.3.1–2.3.22. CrossRef
19. Bhattacharya A, Tejero R, Montelione GT. Evaluating protein structures determined by structural genomics consortia. Proteins 2007;66(4):778–95. CrossRef
20. Schwede T, Kopp J, Guex N, Peitsch MC. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 2003;31(13):3381–5. CrossRef
21. El-Hachem N, Haibe-Kains B, Khalil A, Kobeissy FH, Nemer G. AutoDock and AutoDockTools for protein-ligand docking: beta-site amyloid precursor protein cleaving enzyme 1(BACE1) as a case study. Methods Mol Biol 2017;1598:391–403. CrossRef
22. Nataraj SP, Khajamohiddin S, Jack T. Software for molecular docking: a review. Biophys Rev 2017;9(2):91–102. CrossRef