Computational analysis of high-risk SNPs in human CHK2 gene responsible for hereditary breast cancer: A functional and structural impact

Abstract

Nowadays CHK2 mutation is studied frequently in hereditary breast and ovarian cancer patients in addition to BRCA1/BRCA2. CHK2 is a tumor suppressor gene that encodes a serine/threonine kinase, also involved in pathways such as DNA repair, cell cycle regulation and apoptosis in response to DNA damage. CHK2 is a well-studied moderate penetrance gene that correlates with third high risk susceptibility gene with an increased risk for breast cancer. Hence before planning large population study, it is better to scrutinize putative functional SNPs of CHK2 using different computational tools. In this study, we have used various computational approaches to identify nsSNPs which are deleterious to the structure and/or function of CHK2 protein that might be causing this disease. Computational analysis was performed by different in silico tools including SIFT, Align GVGD, SNAP-2, PROVEAN, Poly-Phen-2, PANTHER, PhD-SNP, MUpro, iPTREE-STAB, Consurf, InterPro, NCBI Conserved Domain Search tool, ModPred, SPARKS-X, RAMPAGE, Verify-3D, FT Site, COACH and PyMol. Out of 78 nsSNP of human CHK2 gene, seven nsSNPs were predicted functionally most significant SNPs. Among these seven nsSNP, p.Arg160Gly, p.Gly210Arg and p.Ser415Phe are highly conserved residues with conservation score of 9 and three nsSNP were predicted to be involved in post translational modification. The p.Arg160Gly and p.Gly210Arg may interfere in phosphopeptide binding site on FHA conserved domain. The p.Ser415Phe may interfere in formation of activation loop of protein-kinase domain and might interfere in interactions of CHK2 with ligand. The study concludes that mutation of serine to phenylalanine at position 415 is a major mutation in native CHK2 protein which might contribute to its malfunction, ultimately causing disease. This is the first comprehensive study, where CHK2 gene variants are analyzed using in silico tools hence it will be of great help while considering large scale studies and also in developing precision medicines related to these polymorphisms in the era of personalized medicine.

A computational in silico approach to predict high-risk coding and non-coding SNPs of human PLCG1 gene

“…These tools find out the possible conserved residues, mutations with the chance of most functionality, possible altered molecular mechanism, structural change in the protein, decreasing protein stability, post-translational modifications (PTM), and other predictable changes to recognize the most significant SNPs [17,18]. Now-a-days such computational research has become popular to find pathogenicity of genes, such as CSN3, RETN, FOXC2, CHK2 and so on [17][18][19][20]. Through our study, it may be possible to identify and predict new SNPs that can be associated with possible diseases.…”

Pathogenic nsSNPs that increase the risks of cancers among the Orang Asli and Malays

“…The GWAS database was used to identify nsSNPs associated with cancer risks as it is the most extensive SNPs database 20 . We only focused on nsSNPs as they are capable of altering protein function, structure, conformation, and interaction which cause the increased risk of cancer 8 – 10 , 56 – 58 . Out of the 80 nsSNPs associated with cancer risks from the GWAS dataset, a total of 52 nsSNPs were identified among the Orang Asli and Malays (43 in Orang Asli and 43 in Malays).…”

“…Previous studies have shown that nsSNPs cause numerous genetic disorders such as inflammatory and autoimmune disorders and cancers 8 – 10 . With the massive human genome sequence data now available and we are yet to know the functional effects of some of the SNPs, a more cost-effective approach is required to unravel the functions of the unknown SNPs effects.…”

Experimental Models as Refined Translational Tools for Breast Cancer Research

“…• using known pathways from public databases [194,216,217], which consists of the comparison of genetic modifications related to cancer with pathways already reported in the literature through the application of statistical and machine learning methods; • network-based methods [193,195,218], identifying cancer-associated genes and pathways related with interactions at the cellular and molecular level in biological networks; • learning cancer pathways de novo [196,219], making deductions on cancer genes and pathways based on the identification of co-occurrence patterns or mutual exclusivity between genetic aberrations, without replicating state-of-the-art knowledge.…”