Identification of potential Akt activators: A ligand and structure-based computational approach (preprint)

The Akt pathway plays an important role in cell metabolism, growth, proliferation, and survival. Akt is the central protein whose phosphorylation controls many downstream pathways. In many diseases like Alzheimer’s, Parkinson, and diabetes, there is downregulation of the Akt pathway. It is proven that the binding of small molecules to the PH domain of Akt facilitates its phosphorylation in the cytoplasm. In the current study, to identify Akt activators, ligand-based approaches like fingerprint-based 2D-QSAR, shape, and pharmacophore-based screening were used, followed by structure-based approaches like docking, MM-GBSA, ADME prediction, and MD simulation. Using the 2D-QSAR activity of the Asinex gold platinum database was predicted, and the top twenty-five molecules found to be active using most models were selected for shape-based and pharmacophore-based screening. Later docking was performed using the PH domain of Akt1 (PDB: 1UNQ), and 197105, 261126, 253878, 256085, and 123435 were selected based on docking score and interaction with Lys 14, Arg 23, Arg 25, Asn 53, and Arg 86. The selected molecules were druggable and formed a stable protein-ligand complex. MD simulations of 261126 and 123435 showed better stability and interaction with key residues. To further investigate the SAR of 261126 and 123435, derivates were downloaded from PubChem, and structure-based approaches were employed. The MD simulation of derivates 12289533, 12785801, 83824832, 102479045, and 6972939 was performed in which 83824832 and 12289533 showed interaction with key residues for a longer duration of time. Therefore, 83824832 and 12289533 may act as Akt activators, and further in-vitro and in-vivo experiments must be performed to support the study.

Multi-Targeting Approach in Selection of Potential Molecule for COVID-19 Treatment (preprint)

Background: Coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic which began in Wuhan province of China. Drug discovery teams around the globe are in a race to develop a medicine for its management. For a novel molecule to enter into the market it takes time and the ideal way is to exploit the already approved drugs and repurpose them to use therapeutically.Methods: In this work, we have attempted to screen selected molecules that have shown an affinity towards multiple protein targets of COVID-19 using Schrödinger suit. Molecules were selected from approved antiviral, anti-inflammatory or immunomodulatory classes. The viral proteins selected were angiotensin-converting enzyme 2 (ACE2), main protease (Mpro) and spike protein. Computational tools such as molecular docking, prime MM-GBSA, induced-fit docking (IFD) and molecular dynamics (MD) simulations were used to identify the most suitable molecule that forms a stable interaction with the selected viral proteins.Results: The ligand-binding stability for the viral proteins PDB-IDs 1ZV8 (spike protein), 5R82 (Mpro) and 6M1D (ACE2), was in the order of Nintedanib>Quercetin, Nintedanib>Darunavir, Nintedanib> Baricitinib respectively. The MM-GBSA, IFD, and MD simulation studies infer that the drug nintedanib has the highest binding stability among the shortlisted molecules towards the selected viral target proteins. Conclusion: Nintedanib, which is primarily used for idiopathic pulmonary fibrosis, can be considered for repurposing and used in the management of COVID-19.