Abstract
BackgroundA major area of effort in current genomics is to distinguish mutations that are functionally neutral from those that contribute to disease. Single Nucleotide Polymorphisms (SNPs) are amino acid substitutions that currently account for approximately half of the known gene lesions responsible for human inherited diseases. As a result, the prediction of non-synonymous SNPs (nsSNPs) that affect protein functions and relate to disease is an important task.Principal FindingsIn this study, we performed a comprehensive analysis of deleterious SNPs at both functional and structural level in the respective genes associated with red blood cell metabolism disorders using bioinformatics tools. We analyzed the variants in Glucose-6-phosphate dehydrogenase (G6PD) and isoforms of Pyruvate Kinase (PKLR & PKM2) genes responsible for major red blood cell disorders. Deleterious nsSNPs were categorized based on empirical rule and support vector machine based methods to predict the impact on protein functions. Furthermore, we modeled mutant proteins and compared them with the native protein for evaluation of protein structure stability.SignificanceWe argue here that bioinformatics tools can play an important role in addressing the complexity of the underlying genetic basis of Red Blood Cell disorders. Based on our investigation, we report here the potential candidate SNPs, for future studies in human Red Blood Cell disorders. Current study also demonstrates the presence of other deleterious mutations and also endorses with in vivo experimental studies. Our approach will present the application of computational tools in understanding functional variation from the perspective of structure, expression, evolution and phenotype.
Highlights
With rapid advances in high-throughput genotyping and generation sequencing technologies, a vast amount of genetic variation has been discovered and deposited in databases, with much more still to come [1]
Non-coding region contains 414 Single Nucleotide Polymorphism’’ (SNPs) (76%) in intronic region and 57 SNPs (10.5%) in mRNA untranslated regions of eukaryotic mRNAs (UTRs) region. It can be seen from the above results that vast majority of SNPs occur in the intronic region
For reliable information about the protein, a sequence variation is essential to gain insights into disease genotype–phenotype correlations. How is it possible to discriminate between amino acid substitution that are deleterious for the stability or for the function of the protein, leading to a disorder, and neutral variations that do not modify the phenotype? An increasing number of computational approaches to in silico analysis of substitutions available on the World Wide Web have been proposed to answer this question [4]– [14]
Summary
With rapid advances in high-throughput genotyping and generation sequencing technologies, a vast amount of genetic variation has been discovered and deposited in databases, with much more still to come [1]. A number of bioinformatics tools, based on recent findings from evolutionary biology (amino acid sequence), protein structure research and computational biology may provide useful information in assessing the functional importance of SNPs [4,5,6,7,8,9,10,11,12,13,14].
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