ABSTRACT
The absence of designated remedies for coronavirus disease 19 (Covid-19) and the lack of treatment protocols drove scientists to propose new small molecules and to attempt to repurpose existing drugs against various targets of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in order to bring forward efficient solutions. The main protease (Mpro) is one of the most promising drug targets due to its crucial role in fighting viral replic-ation. Several antiviral drugs have been used in an attempt to overcome the pandemic, such as hydroxychloroquine (HCQ). Despite its perceived positive outcomes in the beginning of the disease, HCQ was associated with several drawbacks, such as insolubility, toxicity, and cardiac adverse effects. There-fore, in the present study, a structure-based virtual screening approach was performed to identify structurally modified ligands of the chloroquinoline (CQ) scaffold with good solubility, absorption, and permeation aimed at eventually suggesting a more dependable alternative. PDB ID:7BRP Mpro was chosen as the most reliable receptor after cross-docking calculation using 30 crystal struc-tures. Then, a SiteMap analysis was performed and a total of 231,456 structur-ally modified compounds of the CQ scaffold were suggested. After Lipinski criteria filtration, 64,312 molecules were docked and their MM-GBSA free binding energy were calculated. Next, ADME descriptors were calculated, and 12 molecules with ADME properties better than that of HCQ were identified. The resulting molecules were subjected to molecular dynamics (MD) simul-ation for 100 ns. The results of the study indicate that 3 molecules (CQ_22;CQ_2 and CQ_5) show better interactions and stability with the Mpro receptor. Binding interaction analysis indicates that GLU143, THR26, and HIS41 amino acids are potential binding hot-spot residues for the remaining 3 ligands.
ABSTRACT
BACKGROUND: COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed. PURPOSE: This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases. METHODS: A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles. RESULTS: In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks. CONCLUSION: Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.