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  • Title: Identification and structural comparison of deleterious mutations in nsSNPs of ABL1 gene in chronic myeloid leukemia: a bio-informatics study.
    Author: George Priya Doss C, Sudandiradoss C, Rajasekaran R, Purohit R, Ramanathan K, Sethumadhavan R.
    Journal: J Biomed Inform; 2008 Aug; 41(4):607-12. PubMed ID: 18243808.
    Abstract:
    Single nucleotide polymorphism (SNP) serve as frequent genetic markers along the chromosome. They can, however, have important consequences for individual susceptibility to disease and reactions to medical treatment. Also, genetics of the human phenotype variation could be understood by knowing the functions of these SNPs. Currently, a vast literature exists reporting possible associations between SNPs and diseases. It is still a major challenge to identify the functional SNPs in a disease related gene. In this work, we have analyzed the genetic variation that can alter the expression and the function in chronic myeloid leukemia (CML) by ABL1 gene through computational methods. Out of the total 827 SNPs, 18 were found to be non-synonymous (nsSNPs). Among the 30 SNPs in the untranslated region, 3 SNPs were found in 5' and 27 SNPs were found in 3' untranslated regions (UTR). It was found that 16.7% nsSNPs were found to be damaging by both SIFT and PolyPhen server. UTR resource tool suggested that 6 out of 27 SNPs in the 3' UTR region were functionally significant. The two major mutations that occurred in the native protein (1OPL) coded by ABL1 gene were at positions 159 (L-->P) and 178 (G-->S). Val (6), Ala (7) and Trp (344) were found to be stabilizing residues in the native protein (1OPL) coded by ABL1 gene. Even though all the three residues were found in the mutant protein 178 (G-->S), only two of them Val (6) and Ala (7) were acting as stabilizing residue in another mutant 159 (L-->P). We propose from the overall results obtained in this work that, both the mutations 159 (L-->P) and 178 (G-->S) should be considered important in the chronic myeloid leukemia caused by ABL1 gene. Our results on this computational study will find good application with the cancer biologist working on experimental protocols.
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