An in-silico approach to identify novel Akt1 (protein kinase B- alpha) inhibitors as anticancer drugs

Abstract

Abstract Akt1 (protein kinase B) has become a major focus of attention due to its significant functionality in a variety of cellular processes and the inhibition of Akt1 could lead to a decrease in tumour growth effectively in cancer cells. In the present work, we discovered a set of novel Akt1 inhibitors by using multiple computational techniques, i.e., pharmacophore-based virtual screening, molecular docking, binding free energy calculations, and ADME properties. A five-point pharmacophore hypothesis was implemented and validated with AADRR38 including two hydrogen bond acceptors (A), hydrogen bond donor (D), and two aromatic rings (R). The obtained R2 and Q2 values are in the acceptable region with the values of 0.90 and 0.64 respectively. The generated pharmacophore model was employed for virtual screening to find out the potent Akt1 inhibitors. Further, the selected hits were subjected to molecular docking, binding free energy analysis, and refined using ADME properties. Also, we designed a series of 6-methoxybenzo[b]oxazole analogs by comprising the structural characteristics of the hits acquired from the database. Among the new series, 10 molecules were found to have strong binding interactions and binding free energy values which are comparative and even higher than the screened hits as well as higher than the active compound retrieved from the Asinex. In addition, Molecular dynamic simulation was performed to understand the conformational changes of protein-ligand complex. These results suggested that the newly designed molecules are extremely useful for further lead optimization to explore a greater number of compounds in the drug development process.