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Computational refinement of functional single nucleotide polymorphisms associated with ATM gene

C George Priya Doss1, B Rajith

  • 1Centre for Nanobiotechnology, Medical Biotechnology Division, School of Biosciences and Technology, Vellore Institute of Technology University, Vellore, Tamil Nadu, India. georgepriyadoss@vit.ac.in

Plos One
|April 25, 2012
PubMed
Abstract

Insights

Investigating single nucleotide polymorphisms (SNPs) in the ATM gene reveals their impact on cancer susceptibility. Computational analysis characterized functional variants, aiding understanding of genetic differences in disease risk.

Area of Science:

  • Genomics
  • Molecular Biology
  • Cancer Research

Background:

  • Understanding the molecular basis of disease is crucial in modern biology and medicine.
  • Single nucleotide polymorphisms (SNPs) can alter protein function and influence disease development.
  • The ATM gene is frequently altered by single amino acid substitutions, contributing to various cancers, but their regulatory effects remain unclear.

Purpose of the Study:

  • To systematically analyze and characterize deleterious non-synonymous SNPs (nsSNPs) in the ATM gene.
  • To investigate how these nsSNPs affect gene regulation, protein function, and cancer susceptibility.
  • To explore the potential influence of nsSNPs on protein Post-Translational Modifications (PTMs) in the ATM gene.

Main Methods:

  • Utilized various computational methods for analyzing deleterious nsSNPs in the ATM gene.
  • Developed an integrative scoring system combined with amino acid residue sequence conservation for nsSNP analysis.
  • Extended the analysis to include SNPs potentially affecting ATM gene's protein Post-Translational Modifications.

Main Results:

  • Identified and characterized functional nsSNPs within both coding and non-coding regions of the ATM gene.
  • Demonstrated that specific nsSNPs can alter the expression and function of the ATM gene.
  • Provided insights into how nsSNPs may influence protein Post-Translational Modifications.

Conclusions:

  • The in silico characterization of nsSNPs affecting ATM gene function is essential for understanding their role in disease.
  • This systematic analysis provides a foundation for comprehending genetic variations that influence cancer susceptibility.
  • The findings contribute to a better understanding of the molecular underpinnings of ATM-related disorders.

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