A computational approach for investigating the mutational landscape of RAC-alpha serine/threonine-protein kinase (AKT1) and screening inhibitors against the oncogenic E17K mutation causing breast cancer.

Breast cancer (BC) is the most commonly diagnosed cancer among females worldwide, and among the BC-associated mutations in various proteins, mutations in the RAC-alpha serine/threonine-protein kinase (AKT1) remain the most dominant. We thus attempted to understand the potential molecular pathogenicity profile of the mutations in AKT1 using a comprehensive computational protocol involving analyses of biochemistry-disruption and destabilizing properties and conservation. Our predictions revealed that E17K, R67W, V164G, E319G, R391G, D32Y, L52H, L52R, and W80R were the most pathogenic mutations. In addition, the change of glutamate to lysine at position 17 of AKT1 (E17K) was found to be highly predominant. An extensive two-step molecular dynamics (simple and complex) simulation (MDS) using GROMACS (GROningen MAchine for Chemical Simulations) was then initiated to analyze and understand the structural impact of the E17K mutation on the function of AKT1. The simple MDS analysis revealed that the E17K mutation decreases the compactness and intramolecular hydrogen bonds of the protein. We also performed a virtual screening analysis with 19 AKT inhibitors obtained from the Selleck Chemicals website those satisfied the Lipinski rule of 5. Among these 19 compounds, Akti-1/2 exhibited the best binding affinity with both native AKT1 and the E17K mutant. The molecular interaction study also revealed that the co-crystallized AKT1 inhibitor N-(4-(5-(3-acetamidophenyl)-2-(2-aminopyridin-3-yl)-3H-imidazo [4,5-b]pyridin-3-yl)benzyl)-3-fluorobenzamide (12j) exhibited a better interaction with native AKT1 compared with the E17K mutant AKT1 protein, whereas, Akti-1/2 exhibited the opposite effects, i.e., a better interaction with the E17K mutant AKT1 than the native AKT1. These findings from the interaction analysis were further supported by the complex MDS, which measured the compactness and intermolecular hydrogen bonds of the proteins. The results obtained in this study suggest that Akti-1/2 might be a better inhibitor for the treatment of BC caused by the E17K mutation in AKT1.

Impact of missense mutations in survival motor neuron protein (SMN1) leading to Spinal Muscular Atrophy (SMA): A computational approach.

Spinal muscular atrophy (SMA) is a neuromuscular disorder caused by the mutations in survival motor neuron 1 gene (SMN1). The molecular pathology of missense mutations in SMN1 is not thoroughly investigated so far. Therefore, we collected all missense mutations in the SMN1 protein, using all possible search terms, from three databases (PubMed, PMC and Google Scholar). All missense mutations were subjected to in silico pathogenicity, conservation, and stability analysis tools. We used statistical analysis as a QC measure for validating the specificity and accuracy of these tools. PolyPhen-2 demonstrated the highest specificity and accuracy. While PolyPhen-1 showed the highest sensitivity; overall, PolyPhen2 showed better measures in comparison to other in silico tools. Three mutations (D44V, Y272C, and Y277C) were identified as the most pathogenic and destabilizing. Further, we compared the physiochemical properties of the native and the mutant amino acids and observed loss of H-bonds and aromatic stacking upon the cysteine to tyrosine substitution, which led to the loss of aromatic rings and may reduce protein stability. The three mutations were further subjected to Molecular Dynamics Simulation (MDS) analysis using GROMACS to understand the structural changes. The Y272C and Y277C mutants exhibited maximum deviation pattern from the native protein as compared to D44V mutant. Further MDS analysis predicted changes in the stability that may have been contributed due to the loss of hydrogen bonds as observed in intramolecular hydrogen bond analysis and physiochemical analysis. A loss of function/structural impact was found to be severe in the case of Y272C and Y277C mutants in comparison to D44V mutation. Correlating the results from in silico predictions, physiochemical analysis, and MDS, we were able to observe a loss of stability in all the three mutants. This combinatorial approach could serve as a platform for variant interpretation and drug design for spinal muscular dystrophy resulting from missense mutations.