In-silico analysis of deleterious non-synonymous SNPs in the human AVPR1a gene linked to autism.

Abstract

Single nucleotide polymorphisms are the most prevalent type of DNA variation occurring at a single nucleotide within the genomic sequence. The AVPR1a gene exhibits genetic polymorphism and is linked to neurological and developmental problems, including autism spectrum disorder. Due to the difficulties of studying all non-synonymous single nucleotide polymorphisms (nsSNPs) of the AVPR1a gene in the general population, our goal is to use a computational approach to identify the most detrimental nsSNPs of the AVPR1a gene. We employed several bioinformatics tools, such as SNPnexus, PROVEAN, PANTHER, PhD-SNP, SNP & GO, and I-Mutant2.0, to detect the 23 most detrimental mutants (R85H, D202N, E54G, H92P, D148Y, C203G, V297M, D148V, S182N, Q108L, R149C, G212V, M145T, G212S, Y140S, F207V, Q108H, W219G, R284W, L93F, P156R, F136C, P107L). Later, we used other bioinformatics tools to perform domain and conservation analysis. We analyzed the consequences of high‑risk nsSNPs on active sites, post-translational modification (PTM) sites, and their functional effects on protein stability. 3D modeling, structure validation, protein-ligand binding affinity prediction, and Protein-protein docking were conducted to verify the presence of five significant substitutions (R284W, Y140S, P107L, R149C, and F207V) and explore the modifications induced due to these mutants. These non-synonymous single nucleotide polymorphisms can potentially be the focus of future investigations into various illnesses caused by AVPR1a malfunction. Employing in-silico methodologies to evaluate AVPR1a gene variants will facilitate the coordination of extensive investigations and the formulation of specific therapeutic approaches for diseases associated with these variations.