Biomedical and Biotechnology Research Journal (Jan 2023)
In Silico functional network analysis for the identification of novel target associated with SCN1A gene
Abstract
Background: The aim of our study is to identify the novel targets for the SCN1A gene so that we can come up with the potential antiepileptic drugs (AEDs) with the least side effects and best efficacy. Methods: Literature review for candidate genes associated with febrile seizure, generalized epilepsy with febrile seizure plus, dravet syndrome and other idiopathic epilepsy subtypes was done using PubMed, PMC, Google Scholar, and Science Direct. Network analysis of selected candidate genes was done based on molecular function and biological processes using Cytoscape software. Selection of candidate proteins targets receptors for AEDs on the basis of first neighbor, structural retrieval analysis, and verification of selected receptors was done using Ramachandran plot analysis server (RAMPAGE) and Protein Data Bank sum server. Molecular docking calculation and analysis were performed using YASARA and BIOVIA Discovery Studio 2019 software. Results: We screened 157 epileptic genes among which 84 genes were classified as purely epileptic genes and 73 genes were classified as neurodevelopment-associated epilepsy genes. 62 childhood-onset and juvenile-onset epilepsy genes were screened excluding neonatal group due to in born errors of metabolism. In this investigation using SCN1A as a candidate gene, we found SCN9A, HCN2, and FGF12 gene-encoding proteins as potential target receptors. Further, the SCN1A protein receptor was used to screen suitable AEDs using molecular docking investigation. We got three novel AEDs against the SCN1A target gene. Conclusions: In silico network analysis has provided various best-screened target receptors 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.
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