Computational evaluation of 2-arylbenzofurans for their potential use against SARS-CoV-2: A DFT, molecular docking, molecular dynamics simulation study
Natural compounds obtained from various sources have been used in the treatment of many diseases for many years and are very important compounds for drug development studies. They can also be an option to treat COVID-19, which is affecting the whole world and not curable with medication, yet. In this study, two 2-arylbenzofuran derivatives from Sesbania cannabina which are newly entered the literature were investigated computationally with the assistance of computational techniques including DFT calculations, molecular docking calculation and molecular dynamics simulations. The study consists of four parts, in the first part of the study DFT calculations were performed on the 2-arylbenzofurans, and geometry optimizations, vibrational analyses, molecular electrostatic potential (MEP) map calculations, frontier molecular orbital (FMO) calculations and Mulliken charge analyses were carried out.In the second part, molecular docking calculations were performed to investigate the interactions between the molecules and two potential target, SARS-CoV-2 main protease (SARS-CoV-2 Mpro) and SARS-CoV-2 spike receptor binding domain – human angiotensin converting enzyme 2 complex (SARS-CoV-2 SRBD – hACE2). In the third part, MD simulations were performed on the top-scoring ligand – receptor complexes to investigate the stability of the complex and the interactions between ligands and receptors in more detail. Finally, drug-likeness analyses and ADME (adsorption, desorption, metabolism, excretion) predictions were performed on the investigated compounds. Results showed that investigated natural compounds effectively interacted with the target receptors and gave comparable results to the reference drug molecules.
ACE2; COVID-19; Drug-likeness; In silico investigation; SARS-CoV-2 main protease
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