Unknown

Dataset Information

0

DFT, molecular docking and molecular dynamics simulation studies on some newly introduced natural products for their potential use against SARS-CoV-2.


ABSTRACT: Throughout the history, natural products always give new paths to develop new drugs. As with many other diseases, natural compounds can be helpful in the treatment of COVID-19. SARS-CoV-2 main protease enzyme has an important role in viral replication and transcription. Therefore, inhibiting this enzyme may be helpful in the treatment of COVID-19. In this study, it is aimed to investigate eight natural compounds which have recently entered the literature, computationally for their potential use against SARS-CoV-2. For this purpose, first, density functional theory (DFT) calculations were performed on the investigated compounds, and energy minimizations, geometry optimizations, vibrational analyses, molecular electrostatic potential map calculations were carried out. After DFT calculations, geometry optimized structures were subjected to molecular docking calculations with the use of SARS-CoV-2 main protease (pdb id: 5r80) and top-scoring ligand-receptor complexes were obtained. In the next part of the study, molecular dynamics (MD) simulations were performed on the top-scoring ligand-receptor complexes to investigate the stability of the ligand-receptor complexes and the interactions between ligands and receptor in more detail. Additionally, in this part of the study, binding free energies are calculated with the use of molecular mechanics with Poisson-Boltzmann surface area (MM-PBSA) method. Results showed that, all ligand-receptor complexes remain stable during the MD simulations and most of the investigated compounds but especially two of them showed considerably high binding affinity to SARS-CoV-2 main protease. Finally, in the study, ADME (adsorption, desorption, metabolism, excretion) predictions and drug-likeness analyses were performed on the investigated compounds.

SUBMITTER: Erdogan T 

PROVIDER: S-EPMC8140653 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC8672856 | biostudies-literature
| S-EPMC9229637 | biostudies-literature
| S-EPMC9420677 | biostudies-literature
| S-EPMC9189813 | biostudies-literature
| S-EPMC9089900 | biostudies-literature
| S-EPMC8267125 | biostudies-literature
| S-EPMC11007768 | biostudies-literature
| S-EPMC10934104 | biostudies-literature
| S-EPMC8496942 | biostudies-literature
| S-EPMC8615632 | biostudies-literature