IN SILICO ANALYSIS OF NSSNPS AFFECTING STABILITY AND DYNAMICS OF P-GLYCOPROTEIN -A BREAST CANCER ASSOCIATED PROTEIN IDENTIFIED FROM GENE-ENVIRONMENT INTERACTION STUDIES

Abstract

The correct identification of functional SNPs of any gene is an important aspect in the study of genomics but loss of consistent genotype and phenotype data hampers any experiment to characterize the functional influence of all SNPs in humans. Therefore, in silico methods assist in providing useful information for characterizing functional aspect of SNPs. In this study, we have made an intense effort to identify potentially functional SNPs influencing protein function in environment susceptible genes discovered in Breast Cancer pathway. For this we used set of bioinformatic tools that utilize homology-based structure profile information, sequence-based conservation profile, and support vector algorithm in order to examine the nsSNPs found in the breast cancer patients. ABCB1 is one such environment susceptible gene coding for P-glycoprotein which is found to be overexpressed in tumour cells and is the root cause for drug efflux in breast cancer. Six different somatic missense mutations in the human ABCB1 gene in breast cancer patients have been reported in COSMIC database as of 2014. In this study we have applied a set of tools like PolyPhen 2.0, PhD-SNP, and MutPred to display with accurate prediction the disease-associated mutations in ABCB1 gene and their structural impact. Further, we have carried out molecular dynamic simulations (MDS) to study the molecular as well as structural role of predicted disease associated nsSNPs. MDS was used to observe the atomic interaction and motion trajectory of native and mutant (R538S and M701R) P-glycoprotein. Out of these six nsSNPs, two mutations R538S which is present in the ATP binding domain at NMD interface and M701R present in the TMD domain of P-gp have been predicted to be deleterious by our analysis.