Predicting the impact of deleterious single point mutations in SMAD gene family using structural bioinformatics approach.

Functional alteration in SMAD proteins leads to dis-regulation of its mechanism results in possibilities of high risk diseases like fibrosis, cancer, juvenile polyposis etc. Studying single nucleotide polymorphism (SNP) in SMAD genes helps understand the malfunction of these proteins. In this study, we focused on deleterious effects of nsSNPs in both structural and functional level using publically available bioinformatics tools. We have mainly focused on identifying deleterious nsSNPs in both structural and functional level in SMAD genes by using SIFT, PolyPhen, SNPs&GO, I-Mutant 3.0, MUpro and PANTHER. Structure analysis was carried out with the major mutation that occurred in the native protein coded by SMAD genes and its amino acid positions (R358W, K306S, R310G, S433R and R361C). SRide was used to check the stability of the native and mutant modelled proteins. In addition, we used MAPPER to identify SNPs present in transcription factor binding sites. These findings demonstrate that the in silico approaches can be used efficiently to identify potential candidate SNPs in large scale analysis.