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Published on: August 24, 2013
Computational and structural analysis of deleterious functional SNPs in ARNT oncogene
C George Priya Doss1, Rao Sethumadhavan
1School of Biotechnology, Chemical and Biomedical Engineering, Vellore Institute of Technology University, Vellore, 632014, Tamil Nadu, India.
This study identifies harmful Single Nucleotide Polymorphisms (SNPs) in the ARNT gene linked to acute myeloblastic leukemia using computational analysis. These findings aid in understanding disease mechanisms and developing targeted therapies.
Area of Science:
- Genetics and Genomics
- Computational Biology
- Cancer Research
Background:
- Distinguishing neutral mutations from disease-causing ones is crucial in human genetics.
- Cancer genetics focuses on mutations driving tumorigenesis, offering therapeutic and diagnostic targets.
- Single Nucleotide Polymorphisms (SNPs) are key to understanding disease susceptibility and treatment response variations.
Purpose of the Study:
- To computationally identify deleterious SNPs in the ARNT gene affecting its expression and function in acute myeloblastic leukemia.
- To explore the application of computational tools for analyzing functional genetic variation across structure, expression, evolution, and phenotype.
Main Methods:
- Utilized SIFT (evolutionary) and PolyPhen (structure-based) scoring systems.
- Performed structural analysis on ARNT gene mutations (F363L and R430Q) in the native protein (1X0O).
- Analyzed solvent accessibility and secondary structures of native and mutant proteins to assess stability.
Main Results:
- Identified specific ARNT gene mutations (F363L, R430Q) with potential functional impact.
- Computational models were developed for predicting deleterious non-synonymous SNPs (nsSNPs).
Conclusions:
- The developed computational models can predict deleterious nsSNPs in the ARNT gene.
- Findings support further genotype-phenotype research and pharmacogenetics studies in acute myeloblastic leukemia.
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