Structural and Functional Characterization of Pathogenic Non- Synonymous Genetic Mutations of Human Insulin-Degrading Enzyme by In Silico Methods

Insulin-degrading enzyme (IDE) is a key protease involved in degrading insulin and amyloid peptides in human body. Several non-synonymous genetic mutations of IDE gene have been recently associated with susceptibility to both diabetes and Alzheimer’s diseases. However, the consequence of these mutations on the structure of IDE protein and its substrate binding characteristics is not well elucidated. The computational investigation of genetic mutation consequences on structural level of protein is recently found to be an effective alternate to traditional in vivo and in vitro approaches. Hence, by using a combination of empirical rule and support vector machine based in silico algorithms, this study was able to identify that the pathogenic nonsynonymous genetic mutations corresponding to p.I54F, p.P122T, p.T533R, p.P581A and p.Y609A have more potential role in structural and functional deviations of IDE activity. Moreover, molecular modeling and secondary structure analysis have also confirmed their impact on the stability and secondary properties of IDE protein. The molecular docking analysis of IDE with combinational substrates has revealed that peptide inhibitors compared to small non-peptide inhibitor molecules possess good inhibitory activity towards mutant IDE. This finding may pave a way to design novel potential small peptide inhibitors for mutant IDE. Additionally by un-translated region (UTR) scanning analysis, two regulatory pathogenic genetic mutations i.e., rs5786997 (3’ UTR) and rs4646954 (5’ UTR), which can influence the translation pattern of IDE gene through sequence alteration of upstream-Open Reading Frame and Internal Ribosome Entry Site elements were identified. Our findings are expected to help in narrowing down the number of IDE genetic variants to be screened for disease association studies and also to select better competitive inhibitors for IDE related diseases. Keywords: Insulin-degrading enzyme, diabetes mellitus Type 2, Alzheimer’s disease, in silico analysis, genetic mutations, Protein-protein docking.