Computational analysis of Alzheimer's disease-associated missense SNPs to understand underlying molecular mechanisms and identify diagnostic biomarkers

Abstract

BackgroundAlzheimer's disease (AD) is the most common and progressive type of brain disorder that affects parts of the brain responsible for memory, speaking, thinking, and many other important functions. Apart from its common risk factors such as aging, environment, and lifestyle elements, the risk of developing AD largely depends on gene variants, which present a promising opportunity for identifying novel diagnostic and therapeutic biomarkers. ObjectivesEarly studies have revealed numerous SNPs simultaneously associated with AD and other diseases such as Parkinson's disease, stroke, multiple sclerosis, and more. Therefore, it is important to conduct research on identifying single nucleotide missense mutations in certain genes specifically linked to AD to understand the prognosis and diagnosis of the disease. MethodsIn this research, we utilized multiple sequence-based computational tools and database servers to analyze specific missense single nucleotide polymorphisms and their potential effects on protein structure and stability. ResultsOur in-silico analysis revealed SNPs of 3 genes, specifically, ATP8B4, UBXN11, and TREM2, to be deleterious. The associating mutations were found to be destabilizing the protein structure and function of deleterious genes. ConclusionThree genes, including ATP8B4, UBXN11, and TREM2 and their associated SNPs, were found to be deleterious and are potentially linked to AD. Amino acid changes associated with these genes were found to affect their interactions, which are connected to specific biological processes and pathways that may trigger AD.