Alzheimer's Disease (AD) is a special class of neurodegenerative diseases demarcated as a progressive disorder affecting especially older adults globally. The AD-infected brain shows declination in cognitive functions, memory loss, and other exhausting symptoms. In this study, we focused on using advanced bioinformatics and next-generation sequencing to explore essential clusters of genes from various diversified Alzheimer's, Parkinson and Frontotemporal Dementia diseased cases. The significant differential expression analysis of genes (p-value ≤ 0.05, log fold change ≤ 0.05) was carried out, followed by meta-analysis, which resulted in the identification of 20 conserved genes across variable case studies. Out of 20 conserved genes, CASP8 and PTPN11 were observed to show essential regulatory mechanisms in AD metabolic pathways and proceeded further for docking analysis. Moreover, the natural compounds were screened for ligand library preparation based on extensive scientific literature and (ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity)) property check. Molecular docking was carried out with screened ligands and target receptors, resulting in the identification of Rosmarinic acid (RA) with CASP8 having docked score (∆G = -8.0 kcal/mol); Donepezil (FDA drug) dock score (∆G = -7.3 kcal/mol) (control). PTPN11 receptor with Carnosol ligand resulted in docking score (∆G = -9.1 kcal/mol) w.r.t Tacrine (FDA drug) docked score (∆G = -8.0 kcal/mol) followed by MD simulation. This research will aid in the identification of potential natural compounds that future researchers can use for further validation as a potential candidate drug in combating various neurodegenerative diseases highlighting AD.
Keywords: computational approach; docking; md simulation; neurodegenerative diseases; next generation sequencing.
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