Novel SARS-CoV-2, an etiological factor of Coronavirus disease 2019 (COVID-19), poses a great challenge to the public health care system. Among other druggable targets of SARS-Cov-2, the main protease (M pro ) is regarded as a prominent enzyme target for drug developments owing to its crucial role in virus replication and transcription. We pursued a computational investigation to identify M pro inhibitors from a compiled library of natural compounds with proven antiviral activities using a hierarchical workflow of molecular docking, ADMET assessment, dynamic simulations and binding free-energy calculations. Five natural compounds, Withanosides V and VI, Racemosides A and B, and Shatavarin IX, obtained better binding affinity and attained stable interactions with M pro key pocket residues. These intermolecular key interactions were also retained profoundly in the simulation trajectory of 100 ns time scale indicating tight receptor binding. Free energy calculations prioritized Withanosides V and VI as the top candidates that can act as effective SARS-CoV-2 M pro inhibitors.
【저자키워드】 Computational biology and bioinformatics, Molecular modelling, 【초록키워드】 COVID-19, coronavirus disease, public health, SARS-CoV-2, Coronavirus disease 2019, Health care, Transcription, molecular docking, protease, antiviral activity, inhibitors, binding affinity, free energy, Free energy calculations, trajectory, virus replication, target, novel, inhibitor, Health care system, Care, binding, compounds, Interaction, Receptor binding, antiviral activities, Hierarchical, enzyme, Compound, M pro, candidate, residues, effective, identify, etiological, retained, 【제목키워드】 Antiviral, docking, SARS-CoV-2 main protease, Molecular dynamics simulation, inhibitor, identification,