INTRODUCTION
A vast number of genetic disorders are caused by sodium channelopathies.[1] In some of the studies shows no mutation in SCN1A and SCN1B, which indicates huge genetic heterogeneity.[2] Hence, these controversial results lead us to explore more about the genetics of idiopathic epilepsy subtypes. It helps a researcher to have deeper insights and better understanding of complex diseases.[3] This study represents the manual intervention so that the underlying mechanism of pathogenesis can be understood.[4] The aim of this research is to provide a systems biology approach to retrieve information from interaction networks of gene expression.
METHODS
Collection of candidate genes associated with Human epilepsies
A literature survey of candidate genes associated with human epilepsies was done using PubMed, PMC, Google Scholar, Science Direct, and other search engines[5] shown in Table 1 and the working methodology is given in Figure 1.
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Table 1: Candidate gene collection
Figure 1: Workflow of the proposed framework to infer key genes in epilepsy
Protein sequence retrieval and functional association network analysis
Sequences of proteins were retrieved using Universal Protein Resource (UniProt). UniProt is a freely accessible database which contains protein data.[6] The protein sequences were retrieved through their corresponding accession number. The retrieved FASTA sequences were further utilized and used for visualization of protein‒protein functional-associated network analysis using STRING server.[7] STRING search tool was used for inputting retrieved multiple FASTA sequences for the retrieval of interacting proteins of inputted protein sequences and Homosapiens database was used as the organism name.[8] Functional-associated network analysis of selected candidate proteins was performed on the basis of known interactions (from curated databases and experimentally determined), gene neighborhood, gene fusions, and gene co-occurrence.
Gene network analysis using Cytoscape
An open-source software Cytoscape 3.2.1 was also used for the visualization of molecular interaction networks along with the correspondingly associated biological processes and molecular function.[9] For all retrieved protein sequences, the molecular function and biological process were arranged in one excel file and visualized along with STRING protein‒protein data, and the most prominent functional network was detected and eventually a search was conducted to look for the closest neighbor. Identification of the first neighbor was done on the basis of a prominent network with visualize parameter map node size to edge count.
Structural retrieval and analysis (refinement)
Protein Data Bank (PDB) sum was put into use to know the structure of candidate proteins.[10] PDBsum is a database that provides an overview of the contents of each three-dimensional (3D) macromolecular structure deposited in the PDB. Quality assessment was also done using different verification servers. ERRAT, VERIFY 3D, RAMPAGE, and PROCHECK analysis were performed successively to look for more refined structures which are available in the database. The UniProt database whose structures were available with the least resolution was downloaded for verification and visualized using BIOVIA Discovery Studio 2019 shown in Figure 2. Those structures which were best in quality were further used for the molecular docking studies, but if the structure was not available for the identified protein was used for modeling and refinement using ModRefiner server.[11] Further subsequently, the refined file was uploaded to the PDB sum to generate server for reanalyzing structure quality before proceeding for the molecular docking process shown in Tables 2 and 3.
Figure 2: Visualization of 3D structure of HCN2 (3u10) using BIOVIA Discovery Studio
Table 2: Verification and validation using RAMPAGE
Table 3: Verification and validation using protein data banksum
Screening of potential ligands based on absorption, distribution, metabolism, excretion, and toxicity studies
Some important candidate antiepileptic compounds were retrieved from PubChem compound database. Further screening of potential compounds, i.e., absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies were conducted using ProTox II, Swiss ADME software,[12,13] OSIRIS tool, and BIOVIA Discovery Studio ADMET software.
Active site analysis
The method utilizes both the grid and probe sphere.[14] The receptors were used for receptor preparations and and those anti-epileptic drugs that were retrieved using PubChem database were optimized using BIOVIA Discovery Studio further molecular docking analysis was performed to screen candidate AED targets.
Molecular docking calculation and assessment
Molecular docking[15–17] was done using CDOCKER. The docking calculation was performed by the CDOCKER energy, which was calculated, based on the internal ligand strain energy and receptor-ligand interaction energy. CDOCKER interaction represents the energy of the nonbonded interaction that exists between the protein and the ligand. The finest docked complexes were selected on the basis of CDOCKER energy and CDOCKER interaction.[18]
RESULTS
Collection of candidate genes associated with human epilepsies
A hierarchical process is to identify the candidate genes and their regulatory biological functions in epilepsy which is followed by the proposition of the prognosis. This study produces the following outcomes ‒ 157 epilepsy genes were identified, 84 of which were classified as purely epileptic genes, and 73 as neurodevelopment-associated epilepsy genes. 62 childhood and juvenile-onset epilepsy genes were screened and the collection was done excluding the neonatal group[19] using various literature databases.
Protein sequence retrieval, functional association network analysis and gene network analysis using Cytoscape
SCN1A protein receptor was used to screen suitable AEDs using molecular docking investigation. From the gene-gene and protein‒protein network interaction analysis it has been elucidated that 37 number of genes being found to be in network interaction on the basis of Molecular Function and Biological Process shown in Figure 3. Protein sequence retrieval and functional enrichment analysis of 37 childhood and juvenile-onset protein-coding genes were performed using g: Profiler[20] shown in Figure 4, molecular function [Figure 5], cellular cytology [Figure 6]. In this investigation, we found that SCN1A, SCN9A, HCN2, and FGF12 proteins as a potential target receptors to be in interaction on the basis of first neighbor shown in Tables 4 and 5 using Cytoscape software.
Figure 3: PPIN retrieved from STRING database comprising 37 proteins (Childhood and juvenile epileptic genes). PPIN: Protein-protein interaction networks
Figure 4: Enrichment test of selected genes using g: Profiler
Figure 5: Functional enrichment of candidate genes associated with human epileptic gene using molecular function
Figure 6: Functional enrichment of candidate genes associated with human epileptic gene using cellular cytology
Table 4: Closest neighbor (molecular function)
Table 5: Closest neighbor (biological process)
Structural retrieval and analysis (refinement)
Verification and validation utilizing various verification servers were done. In RAMPAGE, we got the best result with 3u10 and its refined structure 3u10; the number of residues was found to be 99.0% and 98.5% in favored regions, respectively. Similar results were obtained with PDBsum server, 3u10, and its refined structure 3u10; the number of residues was found to be 92.9% and 95.1% in most favored regions, respectively.
Screening of potential ligands based on absorption, distribution, metabolism, excretion, and toxicity studies
After a thorough investigation of the results, HCN2 was found to be the best-screened receptor among the other four receptors. As a result of the above HCN2 receptor, by means of literature screening and ADMET studies performed using BIOVIA ADMET software, and Toxicity Model Report (Protox II) [Table 6], we selected some of the best AEDs from the literature and finally dock the receptor with different AEDs through molecular docking software, its calculation and assessment were done through BIOVIA Discovery STUDIO 2019 software.
Table 6: Toxicity report through protox II
Active site analysis and molecular docking calculation and assessment
Active site analysis of the best-screened receptor, i.e., HCN2 was done using POCASA software with all the best-screened AEDs out of eleven best-screened drugs from ADMET studies. Ten of which showed good CDOCKER energy interaction. Overall study on the basis of CDOCKER energy finally results only in three compounds that are best in regard to energy score which shows good docking results given in Table 7. We got POCASA generated A-site as a best site where all the three screened drugs were docked. Vigabatrin among those eleven drugs did not docked with any of our best-screened receptors. Finally, this study identified three novel drugs, namely, sodium valproate/valproic acid, pregabalin, and sultiame against HCN2 receptor.
Table 7: CDOCKER scores
DISCUSSION
SCN1A mutations are the top candidate player gene in genetic epileptic disorders ranging from mild febrile seizure to catastrophic dravet syndrome at the other end with huge genetic heterogeneity.[21] With the knowledge of bioinformatics tools and techniques, we are able to gain insights into the mechanistic understanding of the gene‒gene and protein‒protein network interaction. These interactions are necessary to carry out our several biological processes, information about several interacting proteins is crucial for understanding these biological functions, their pathways, and how they are regulated at the molecular level.
CONCLUSION
Insilico network study of SCN1A gene has provided various best-screened target receptors like SCN9A, HCN2, and FGF12 from the huge network interaction group of genes for AED targeting. This will help in better understanding of disease mechanisms, analysis, and knowledge of the molecular structure of protein. The study of receptor proteins through this can be explored and utilized further for curative purpose.
Limitation of the study
This work is only limited to the identification of novel AEDs that are SCN1A gene targets with the least side effects and highest efficacy; however, this work does not guarantee their application to human epileptic patients until further in vitro, invivo, and clinical drug trial studies approve these drugs. As a result, wet lab validation was omitted from the study.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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