In-silico analysis of non-synonymous SNPs of human LDLR gene and their impact on familial hypercholesterolemia

Abstract

Single nucleotide polymorphisms (SNPs) are the most regular type of genetic variation among human genome. The low-density lipoprotein receptors (LDLR) gene encoded receptor protein that specifically binds to low density lipoprotein (LDL), to act as prime cholesterol carriers in the blood. Unregulated removal of LDL from the circulation results from inherited mutations in the LDLR gene or, more rarely, in the apo B gene, the ligand for the LDLR causes familial hypercholesterolemia (FH). Several studies have shown that non-synonymous single nucleotide polymorphisms (nsSNPs) induce more than 50% genetic diseases. However, the growing number of SNPs in databases makes it difficult to evaluate the SNPs that come up with disease development. Therefore, we tried to identify the nsSNP within LDLR gene used mainly computational tools like: PROVEAN, Predict-SNP, SIFT, MAAP, SNAP, PhD-SNP, Polyphen v1, Polyphen v2, PANTHER and nsSNPAnalyzer. After analysing a total of 1114 missense SNPs, we found only 39 nsSNPs are most deleterious, whose conservation score ranges from grade ranges 7 to 9 that is assessed through ConSurf web server. Among 39 most significant nsSNPs, 11were predicted for involving in post translational modifications. Additionally, I-Mutant 2.0 and MUpro tool showed a decreased instability for these nsSNPs upon mutation. Protein structural analysis of these amino acid variants was performed by using I-Mutant 2.0. Stability analysis predicted 11 nsSNPs (C313W, F408K, A431P, A431S, A431T, D433H, D433Y, G546D, G546V, G546E and C697W), these are decreased protein stability. The in-silico analysis in current study may be a significant model for future research design studies.