Investigation of angucycline compounds as potential drug candidates against SARS Cov-2 main protease using docking and molecular dynamic approaches

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Identification of lead compounds from large natural product library targeting 3C-like protease of SARS-CoV-2 using E-pharmacophore modelling, QSAR and molecular dynamics simulation

COVID-19 is a novel disease caused by SARS-CoV-2 and has made a catastrophic impact on the global economy. As it is, there is no officially FDA approved drug to alleviate the negative impact of SARS-CoV-2 on human health. Numerous drug targets for neutralizing coronavirus infection have been identified, among them is 3-chymotrypsin-like-protease (3CL^pro), a viral protease responsible for the viral replication is chosen for this study. This study aimed at finding novel inhibitors of SARS-CoV-2 3C-like protease from the natural library using computational approaches. A total of 69,000 compounds from natural product library were screened to match a minimum of 3 features from the five sites e-pharmacophore model. Compounds with fitness score of 1.00 and above were consequently filtered by executing molecular docking studies via Glide docking algorithm. Qikprop also predicted the compounds drug-likeness and pharmacokinetic features; besides, the QSAR model built from KPLS analysis with radial as binary fingerprint was used to predict the compounds inhibition properties against SARS-CoV-2 3C-like protease. Fifty ns molecular dynamics (MD) simulation was carried out using GROMACS software to understand the dynamics of binding. Nine (9) lead compounds from the natural products library were discovered; seven among them were found to be more potent than lopinavir based on energies of binding. STOCK1N-98687 with docking score of -9.295 kcal/mol had considerable predicted bioactivity (4.427 µM) against SARS-CoV-2 3C-like protease and satisfactory drug-like features than the experimental drug lopinavir. Post-docking analysis by MM-GBSA confirmed the stability of STOCK1N-98687 bound 3CL^pro crystal structure. MD simulation of STOCKIN-98687 with 3CL^pro at 50 ns showed high stability and low fluctuation of the complex. This study revealed compound STOCK1N-98687 as potential 3CL^pro inhibitor; therefore, a wet experiment is worth exploring to confirm the therapeutic potential of STOCK1N-98687 as an antiviral agent.