Biomedical Letters

Computational detection, analysis and interpretations of genomic variants in human diseases associated GENEMDM 2

Muqadas Baksh 1, Muhammad Sarfaraz Iqbal 1,2, Farkhanda Yasmin 3*, Saiema Suleman 4, Majeeda Rasheed 5, Waqas Farooq 6, Talha Javed 7

1 Department of Bioinformatics & Computational biology, Virtual University of Pakistan

2 Department of Bioinformatics & Biotechnology, GC University Faisalabad, Pakistan

3 Department of Biosciences, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan

4 Institute of Molecular Biology & Biotechnology, University of Lahore, Lahore

5 Department of Life Sciences, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan

6 Department of Biochemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan

7 Department of Biotechnology & Bioinformatics, Virtual University of Pakistan

Abstract

Most of the mutations described in human MDM2 are tolerated without significantly disrupting the corresponding structural or molecular function. However, some of them are associated with a variety of human diseases, including cancer. Numerous computational methods have been developed to predict the effects of missense single nucleotide variants (SNVs). The non-synonymous single nucleotide polymorphisms affect the function of XRCC1, which impairs the ability to repair DNA and therefore increases the risk of diseases such as cancer. In this study, sequence and structure-based computational tools were used to screen the total listed coding SNPs of the MDM2 gene in order to recognize and describe them. The potential 6 ns SNP of MDM2 were identified from 29 ns SNP by consistent analysis using computational tools PolyPhen 2, SIFT, PANTHER and cSNP. The computational methods were used to systematically classify functional mutations in the regulatory and coding regions that modify the expression and function of the MDM2 enzyme. The HOPE project also made it possible to elaborate the structural effects of the substitutions of amino acids. In silico analysis predicted that rs759244097 is harmful. This study concluded that identifying this SNP will help to determine an individual's cancer susceptibility, prognosis and further treatment. Furthermore, current high-throughput sequencing efforts and the need for extensive interpretation of protein sequence variants requires more efficient and accurate computational methods in the coming years.