A Computational Study on Selected Alkaloids as SARS-CoV-2 Inhibitors: PASS Prediction, Molecular Docking, ADMET Analysis, DFT, and Molecular Dynamics Simulations.

Despite treatments and vaccinations, it remains difficult to develop naturally occurring COVID-19 inhibitors. Here, our main objective is to find potential lead compounds from the retrieved alkaloids with antiviral and other biological properties that selectively target the main SARS-CoV-2 protease (Mpro), which is required for viral replication. In this work, 252 alkaloids were aligned using Lipinski's rule of five and their antiviral activity was then assessed. The prediction of activity spectrum of substances (PASS) data was used to confirm the antiviral activities of 112 alkaloids. Finally, 50 alkaloids were docked with Mpro. Furthermore, assessments of molecular electrostatic potential surface (MEPS), density functional theory (DFT), and absorption, distribution, metabolism, excretion, and toxicity (ADMET) were performed, and a few of them appeared to have potential as candidates for oral administration. Molecular dynamics simulations (MDS) with a time step of up to 100 ns were used to confirm that the three docked complexes were more stable. It was found that the most prevalent and active binding sites that limit Mpro'sactivity are PHE294, ARG298, and GLN110. All retrieved data were compared to conventional antivirals, fumarostelline, strychnidin-10-one (L-1), 2,3-dimethoxy-brucin (L-7), and alkaloid ND-305B (L-16) and were proposed as enhanced SARS-CoV-2 inhibitors. Finally, with additional clinical or necessary study, it may be able to use these indicated natural alkaloids or their analogs as potential therapeutic candidates.

Anti-lung cancer drug discovery approaches by polysaccharides: an in silico study, quantum calculation and molecular dynamics study.

Lung cancer (LC) is one of the major and risky health defects even the serious cause for death in concurrent era. But no potential drugs even chemotherapeutic agents have been discovered with approval of health safety although some non-toxic biological macromolecules, such as polysaccharides and polysaccharide-protein complexes, have obtained as anti-lung cancer properties. This study conveys the anti-lung cancer properties of 45 polysaccharide derivatives collected from PubChem database. Primarily, the PASS prediction was performed to depict their anti-cancer activity, and 37 compounds showed the desired results. Next, the chemical descriptors, such as HOMO, LUMO, softness, and hardness etc, were calculated through the density functional theory (DFT) for quantum properties. Secondly, the auto molecular docking was executed to delineate the protein-ligand interactions, binding ability and inhibition of active sites of proteins. Additionally, the compounds showed docking score more than -6.40 kcal/mol, and the highest binding affinity was at -10.00 kcal/mol even 15 compounds have higher binding score (-8.6 to -10.0) than approved drugs, Gemcitabine. Succeeding, the most common protein residue, VAL 647, was blocked by ligands for the main protein (1X2J). In addition, five protein's active sites were determined to make the relative study of protein-ligand interactions. As a result, the target docking against five proteins was performed, and it was found that the targeted docking score as the binding affinity is lower than auto docking. Finally, a comparative study between auto docking and targeted docking was performed for the most common five lung cancer proteins founded in three organisms.Communicated by Ramaswamy H. Sarma.