Abstract
Hypertension is the foremost modifiable risk factor for cardiovascular and renal diseases, and overall mortality on a global scale. Genetic variants have the potential to alter an individual's drug responses. In the present study, we employed a comprehensive computational analysis to evaluate the structural and functional implications of deleterious missense variants to examine the influence of RAAS genes such as AT1R, AT2R, and MasR on susceptibility to hypertension. The objective of this research was to identify potentially deleterious missense variants within these target genes. A total of 13 in silico tools were used to identify deleterious missense SNPs. Protein stability, evolutionary conservation, and 3D structural modeling were assessed using tools like I-Mutant 3.0, MUpro, DynaMut2, ConSurf, and Project HOPE, while protein-protein interactions were analyzed via STRING. Our findings revealed three deleterious missense variants (rs397514687, rs886058071, rs368951368) in AT1R; two deleterious missense variants (rs3729979 and rs372930194) in AT2R; and three deleterious missense variants (rs768037685, rs149100513, and rs377679974) in MasR, all of which exhibited significant damaging effects as determined by the 13 Computational tools employed. All these deleterious missense variants adversely affected protein stability and were found to be highly conserved. Notably, these variants altered the charge, size, and hydrophobicity of the amino acids, with a predominant occurrence in alpha helix regions, with the exception of rs377679974 in MasR. The computational analysis and structural comparisons conducted in this study indicate that these deleterious missense variants have a discernible impact on the structure and function of the target proteins. However, it is essential to conduct experimental validation to verify the detrimental effects of the missense variants identified through this computational analysis. Therefore, we may conduct future experimental analyses to validate these findings. This research will aid in the identification of candidate deleterious markers that may serve as potential targets for therapeutic strategies and disease diagnosis.