Computational prediction of nsSNPs effects on protein function and structure, a prioritization approach for further in vitro studies applied to bovine GSTP1

Abstract

The development of high-throughput technologies in the last decade produced an exponential increase in the amount of biological data available. The case of redox biology and apoptosis is not an exception, and nowadays there is a need to integrate information from multiple “omics” studies. Therefore, validation of proposed discoveries is essential. However, the study in biological systems of the effect of the massive amounts of sequence variation data generated with next-generation sequencing (NGS) technologies can be a very difficult and expensive process. In this context, the present study aimed to demonstrate the advantages of a computational methodology to systematically analyze the structural and functional effects of protein variants, in order to prioritize further studies. This approach stands out for its easy implementation, low costs and low time consumed. First, the possible impact of mutations on protein structure and function was tested by a combination of tools based on evolutionary and structural information. Next, homology modeling was performed to predict and compare the 3D protein structures of unresolved amino acid sequences obtained from genomic resequencing. This analysis applied to the bovine GSTP1 allowed to determine that some of amino acid substitutions may generate important changes in protein structure and function. Moreover, the haplotype analysis highlighted three structure variants worthwhile studying through in vitro or in vivo experiments.