PUNAR ALİYEVA, BEYZA YİLMAZ, DORUK ALP UZUNARSLAN, VİLDAN ENİSOGLU ATALAY
The European Chemistry and Biotechnology Journal - 2025;2(4):14-23
The recent outbreak of a new coronavirus disease known as COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is a highly contagious and pathogenic viral infection that has spread worldwide. Coronaviruses are known to cause disease in hu-mans, other mammals, and birds. Although specific therapeutics and vaccines require efforts in this direction, reaching the world’s population with mutations of the virus can be a difficult target. The major proteases of coronavirus play a critical role during the spread of the disease and therefore still represent an important target for drug discovery. As of now, there is still no official treatment for infected patients. In this study, bio-informatics-based molecular docking studies were performed to identify potent inhibitors of novel candidate molecules against the spike protein S of SARS-CoV-2. The affinities of ligand molecules thought to be ef-fective in the treatment of SARS-CoV-2 disease were investigated. For this purpose, 1,615 different FDA-approved drug ligand molecules were retrieved from ZINC15 database. Crystallographic structure of spike pro-tein S of SARS-CoV-2 was retrieved from Protein Data Bank (PDB). In-itial virtual screening was performed using qvina-w, an accelerated ver-sion of AutoDock Vina optimized for rapid docking, to evaluate binding affinities of all 1,615 compounds against the spike protein. The top 10 li-gands with the most favorable binding affinities were selected for further analysis. These ligands were docked to the target protein with Autodock Vina. The complexes were first solvated and then run through Molecular Dynamics (MD) simulations, utilizing NAMD. The binding energies were computed through these interactions, which are used to compare the af-finities of the ligands to the target protein. Ultimately, 10 different ligands capable of inhibiting the spike protein of SARS-CoV-2 were selected and compared based on their affinities.
