A Combined Molecular Docking and Density Functional Theory Nuclear Magnetic Resonance Study of Trans-Dehydrocrotonin Interacting with COVID-19 Main Protease and Severe Acute Respiratory Syndrome Coronavirus 2 3C-Like Protease

For the development of drugs that treat SARS-CoV-2, the fastest way is to find potential molecules from drugs already on the market. Unfortunately, there is currently no specific drug or treatment for COVID-19. Among all structural proteins in SARS-CoV, the spike protein is the main antigenic component responsible for inducing host immune responses, neutralizing antibodies, and/or protecting immunity against virus infection. Molecular docking is a technique used to predict whether a molecule will bind to another. It is usually a protein to another or a protein to a binding compound. Natural products are potential binders in several studies involving coronavirus. The structure of the ligand plays a fundamental role in its biological properties. The nuclear magnetic resonance technique is one of the most powerful tools for the structural determination of ligands from the origin of natural products. Nowadays, molecular modeling is an important accessory tool to experimentally got nuclear magnetic resonance data. In the present work, molecular docking studies aimed is to investigate the limiting affinities of trans-dehydrocrotonin molecule and to identify the main amino acid residues that could play a fundamental role in their mechanism of action of the SARS-CoV spike protein. Another aim of this work is all about to evaluate 10 hybrid functionalities, along with three base pairs using computational programs to discover which ones are more reliable with the experimental result the best computational method to study organic compounds. We compared the results between the mean absolute deviation (MAD) and root-mean-square deviation (RMSD) of the molecules, and the smallest number between them was the best result. The positions assumed by the ligands in the active site of the spike glycoprotein allow assuming associations with different local amino acids.