Single-nucleotide polymorphisms (SNPs) play a major role in the understanding of the genetic basis of many complex human diseases. Also, the genetics of human phenotype variation could be understood by knowing the functions of these SNPs. It is still a major challenge to identify the functional SNPs in a disease-related gene. In this work, we have analyzed the genetic variation that can alter the expression and the function of the BRCA1 gene using computational methods. Of the total 477 SNPs, 65 were found to be nonsynonymous (ns) SNPs. Among the 14 SNPs in the untranslated region, 4 were found in the 5' and 10 were found in the 3' untranslated region (UTR). It was found that 16.9% of the nsSNPs were damaging, by both the SIFT and the PolyPhen servers. The UTR Resource tool suggested that 2 of 4 SNPs in the 5' UTR and 3 of 10 SNPs in the 3' UTR might change the protein expression levels. We identified major mutations from proline to serine at positions 1776 and 1812 of the native protein of the BRCA1 gene. From a comparison of the stabilizing residues of the native and mutant proteins, we propose that an nsSNP (rs1800751) could be an important candidate for the breast cancer caused by the BRCA1 gene.

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Single Nucleotide Polymorphisms (SNPs) are being intensively studied to understand the biological basis of complex traits and diseases. The Genetics of human phenotype variation could be understood by knowing the functions of SNPs. In this study using computational methods, we analyzed the genetic variations that can alter the expression and function of the CFTR gene responsible candidate for causing cystic fibrosis. We applied an evolutionary perspective to screen the SNPs using a sequence homology-based SIFT tool, which suggested that 17 nsSNPs (44%) were found to be deleterious. The structure-based approach PolyPhen server suggested that 26 nsSNPS (66%) may disrupt protein function and structure. The PupaSuite tool predicted the phenotypic effect of SNPs on the structure and function of the affected protein. Structure analysis was carried out with the major mutation that occurred in the native protein coded by CFTR gene, and which is at amino acid position F508C for nsSNP with id (rs1800093). The amino acid residues in the native and mutant modeled protein were further analyzed for solvent accessibility, secondary structure and stabilizing residues to check the stability of the proteins. The SNPs were further subjected to iHAP analysis to identify htSNPs, and we report potential candidates for future studies on CFTR mutations.

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In this study, we identified the most deleterious non-synonymous SNP of ERBB2 (HER2) receptors by its stability and investigated its binding affinity with herceptin. Out of 135 SNPs, 10 are nsSNPs in the coding region, in which one of the nsSNP (SNPid rs4252633) is commonly found to be damaged by I-Mutant 2.0, SIFT and PolyPhen servers. With this effort, we modelled the mutant HER2 protein based on this deleterious nsSNP (rs4252633). The modeled mutant showed less stability than native HER 2 protein, based on both total energy of the mutant and stabilizing residues in the mutant protein. This is due to a deviation between the mutant and the native HER2, having an RMSD of about 2.81 Å. Furthermore, we compared the binding efficiency of herceptin with native and mutant HER2 receptors. We found that herceptin has a high binding affinity with mutant HER2 receptor, with a binding energy of −24.40 kcal/mol, as compared to the native type, which has a binding energy of −15.26 kcal/mol due to six-hydrogen bonding and two salt bridges exist between herceptin and the mutant type, whereas the native type establishes four hydrogen bonds and two salt bridges with herceptin. This analysis portrays that mutant type has two additional hydrogen bonds with herceptin compared with the native type. Normal mode analysis also showed that the two amino acids, namely Asp596 and Glu598 of mutant HER2, forming additional hydrogen bonding with herceptin, had a slightly higher flexibility than the native type. Based on our investigations, we propose that SNPid rs4252633 could be the most deleterious nsSNP for HER2 receptor, and that herceptin could be the best drug for mutant compared to the native HER2 target. To cite this article: R.