Year : 2012  |  Volume : 3  |  Issue : 2  |  Page : 139-146

Identification and analysis of biomarkers for mismatch repair proteins: A bioinformatic approach

Department of Biotechnology and Bioinformatics, Jaypee University of Information and Technology, Waknaghat, Solan, H.P., India

Correspondence Address:
Tiratha Raj Singh
Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan-173234, H.P.
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0976-9668.101887

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Introduction: Mismatch repair is a highly conserved process from prokaryotes to eukaryotes. Defects in mismatch repair can lead to mutations in human homologues of the Mut proteins and affect genomic stability which can result in microsatellite instability (MI). MI is implicated in most human cancers and majority of hereditary nonpolyposis colorectal cancers (HNPCCs) are attributed to defects in MLH1. Materials and Methods: In our study we analyzed MLH1 protein and the associated nucleotide and other protein sequences. The protein sequences involved in mismatch repair in different organisms have been found to be evolutionary related. Several other related proteins to MLH1 have also been identified through protein-protein interactions. All associated proteins are either mismatch repair proteins or associated with MLH1 in various pathways. Pathways information was also confirmed through MMR and other pathways in KEGG. QSite Finder showed that the active site of MLH1 protein involves residues from the conserved pattern and is involved in ligand-protein interactions and could be a useful site. To analyze linkage disequilibrium (LD) and common haplotype patterns in disease association, we performed statistical haplotype analysis on HapMap genotype data of SNPs genotyped in population CEU on chromosome 3 for MLH1. Results: Various markers have been found and LD plot was also generated. Two distinct blocks have been identified in LD plot which can be independent region of action, and there is involvement of 7 and 17 markers in first and second blocks, respectively. Conclusion: Overall correlation of 0.95 has been found among all interactions of genotyped SNPs which is significant.

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