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Published on: December 9, 2015
Predicting the impact of deleterious single point mutations in SMAD gene family using structural bioinformatics
C George Priya Doss1, N Nagasundaram, Himani Tanwar
1Centre for Nanobiotechnology, Medical Biotechnology Division, School of Biosciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India. georgepriyadoss@vit.ac.in
Abstract:
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.
Insights
Investigating single nucleotide polymorphisms (SNPs) in SMAD genes reveals their detrimental structural and functional impacts. These findings highlight the utility of in silico methods for identifying disease-associated SNPs in large-scale genetic analyses.
Area of Science:
- Genetics and Bioinformatics
- Molecular Biology
- Disease Mechanisms
Background:
- SMAD proteins regulate crucial cellular mechanisms; their functional alterations are linked to diseases like fibrosis and cancer.
- Single nucleotide polymorphisms (SNPs) in SMAD genes can cause protein malfunction, contributing to disease risk.
Purpose of the Study:
- To identify deleterious non-synonymous SNPs (nsSNPs) in SMAD genes at both structural and functional levels.
- To evaluate the impact of specific SMAD mutations on protein stability and function using bioinformatics tools.
Main Methods:
- Utilized bioinformatics tools including SIFT, PolyPhen, SNPs&GO, I-Mutant 3.0, MUpro, and PANTHER to analyze nsSNPs.
- Performed structure analysis on SMAD proteins with mutations (e.g., R358W, K306S) and assessed protein stability using SRide.
- Employed MAPPER to detect SNPs within transcription factor binding sites.
Main Results:
- Identified several deleterious nsSNPs in SMAD genes affecting protein structure and function.
- Specific mutations like R358W, K306S, R310G, S433R, and R361C were analyzed for their impact.
- Demonstrated that in silico approaches are effective for large-scale SNP candidate identification.
Conclusions:
- In silico analysis provides an efficient strategy for identifying potentially harmful nsSNPs in SMAD genes.
- Understanding the structural and functional consequences of SMAD nsSNPs can aid in disease risk assessment and therapeutic target identification.
