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

Alzheimer’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. Early 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 better understand the prognosis and diagnosis of the disease. In this research, we utilized multiple sequence-based computational tools and database servers to analyze specific AD-related missense single nucleotide polymorphisms and their potential effects on protein structure and stability. Our in-silico analysis revealed three genes, specifically, ATP8B4, UBXN11, and TREM2, and their associated SNPs to be deleterious and to be potentially linked to AD. The associating mutations were found to be destabilizing the protein structure and function of deleterious genes. 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.CitationAziza Abugaliyeva, Saad Rasool, Computational analysis of Alzheimer's disease- associated missense SNPs to understand underlying molecular mechanisms and identify diagnostic biomarkers, Brain Disorders, Volume 13, 2024, 100110, ISSN 2666-4593, https://doi.org/10.1016/j.dscb.2023.100110.