Computational identification of significant missense mutations in AKT1 gene

Abstract

The AKT1 gene is of supreme importance in cell signaling and human cancer. In the present study, we aim to understand the phenotype variations that were believed to have the highest impact in AKT1 gene by different computational approaches. The analysis was initiated with SIFT tool followed by PolyPhen 2.0, I-Mutant 2.0, and SNPs&GO tools with the aid of 22 nonsynonymous (nsSNPs) retrieved from dbSNP. A total of five AKT1 variants such as E17K, E17S, E319G, L357P, and P388T are found to exert deleterious effects on the protein structure and function. Furthermore, the molecular docking study indicates the lesser binding affinity of inhibitor with the mutant structure than the native type. In addition, root mean square deviation and hydrogen bond details were also analyzed in the 10 ns molecular dynamics simulation study. These computational evidences suggested that E17K, E17S, E319G, L357P, and P388T variants of AKT1 could destabilize the protein networks, thus causing functional deviations of protein to some extent. Moreover, the findings strongly indicate that screening for AKT1, E17K, E17S, E319G, L357P, and P388T variants may be useful for disease molecular diagnosis and also to design the potential AKT inhibitors.