INITIAL STUDY ON SARS-COV-2 MAIN PROTEASE INHIBITION MECHANISM OF SOME POTENTIAL DRUGS USING MOLECULAR DOCKING SIMULATION

The infection by the new coronavirus SARS-CoV-2 (called as COVID-19 disease) is a worldwide emergency, however, there is no antiviral treatment or vaccine to date. 3C like protease (3CLpro) is the main protease of SARS-CoV-2 that involved in the process of translation of the polypeptide from the genomic RNA to protein components, which are required for virus replication. The crystal structure of this protease has been rapidly resolved and made publicly in the Protein Data Bank recently. Many efforts have been conducted by scientists including the use of several commercial medicines that are known for treatment of HIV and anti-malarial/antibiotic such as arbidol, chloroquine, hydroxychloroquine, azithromycin, darunavir, remdesivir and lopinavir/ritonavir. These drugs exhibited significant efficacy in clinical, however, the understanding at atomic level of how these compounds prevent SARS-CoV-2 protease is still lacking. Therefore, in this context docking protocol was employed to rapidly estimate the binding affinity and binding pose of six drugs on the main protease of SARS-CoV-2 virus. The obtained results might help to shed light on the interaction mechanism of these compounds toward the protein, and thus suggesting an efficient approach to drug discovery and treatments. © 2020, Publishing House of Natural Science and Technology, VAST. All rights reserved.

REVIEW: AUTODOCK 426 AS AN EFFECTIVE TOOL FOR MOLECULAR DOCKING STUDIES AGAINST SARS-COV-2 MAIN PROTEASE: A TUTORIAL USING MGLTOOLS

The worldwide pandemic caused by coronavirus SARS-CoV-2 (so called as COVID-19 disease) has affected 219 countries and territories, leading to numerous deaths and global financial crisis. The main protease (Mpro) of SARS-CoV-2 plays an important role in mediating the transcription and replication of virus, thus, one of the main therapeutic measures is to find compounds capable of inhibiting these enzymes as soon as possible. Nowadays, computer-aided drug design plays an important role in the field of drug discovery. In particular, molecular docking is one of the initial steps that effectively screen a numerous number of compounds for their interaction and binding affinity toward targeted enzyme, therefrom, suggesting a short list of potential inhibitors for further drug development processes. As part of our ongoing program to provide simple guideline for scientific community to utilize different docking tools for research purposes, in this article, a complete manual guideline of Autodock 4.2.6 is presented to demonstrate the simulation of interaction between compound PF-07321332 and the main protease of SARS-CoV-2, thereby suggesting an effective tool for scientists to conduct reseach on this disease. © 2022, Publishing House of Natural Science and Technology, VAST. All rights reserved.

In-silico Screening of Some Important Bioactive Compounds as Potential Inhibitors of 3CLpro Protein of SARS-CoV-2 and MERS-CoV Virus

Background: Coronavirus (SARS-CoV-2), the leading cause of the epidemic in 2019, also known as COVID-19, has raised ongoing global concerns. The most favourable target protein for this flu is 3CLpro (conserved 3-chymotrypsin-like protease), also known as Mpro. Covaxin and Covishield vaccination is going in India. Remdesivir, as well as some antimalarial drugs such as Hydroxychloroquine and Chloroquine, are used for extreme necessity. However, Hydroxychloroquine and Chloroquine and their derivatives are not convenient for those who are suffering from hypertension, diabetes, cardiac arrest, and many more. Objective: Here, we choose some bioactive compounds for docking studies with the Mpro of SARS CoV2 and MERS as it is used as the primary target for a comparative study. Methods: The docking process was carried out by preparing both 3CLpro proteins, i.e., 2YNA and 6LU7, and then the ligand molecules were downloaded from Pubchem, DrugBank, and Zinc15 databases. Furthermore, SwissAdme and pkCSM software were used for the determination of toxicity and Pharmacokinetic properties (ADMET) properties. Lastly, docking was carried out by the Autodock version 4.2 program, and the docking score was compared to the reference inhibitor Ritonavir. Results: Among 17 bioactive compounds used for docking studies, Quercetin, Trans-Resveratol, Kaemferol, and Theaflavin have top, binding affinity for target proteins, i.e., Theaflavin (-14.35 kcal/mol), Quercetin (-11.88kcal/mol), Kaempferol (-9.3 kcal/mol) and Trans-Resveratol (-9.31 kcal/mol) and also obey Lipinski's rule which makes them potential drug candidate against Covid-19 virus. Hence, the application of these plant-based bioactive compounds alone or along with scheduled vaccination may be the best therapeutic approach in the current scenario. © 2022 International Journal of Pharmaceutical Sciences and Nanotechnology. All right reserved.

Benchmarked molecular docking integrated molecular dynamics stability analysis for prediction of SARS-CoV-2 papain-like protease inhibition by olive secoiridoids.

Objectives: We performed a virtual screening of olive secoiridoids of the OliveNetTM library to predict SARS-CoV-2 PLpro inhibition. Benchmarked molecular docking protocol that evaluated the performance of two docking programs was applied to execute virtual screening. Molecular dynamics stability analysis of the top-ranked olive secoiridoid docked to PLpro was also carried out. Methods: Benchmarking virtual screening used two freely available docking programs, AutoDock Vina 1.1.2. and AutoDock 4.2.1. for molecular docking of olive secoiridoids to a single PLpro structure. Screening also included benchmark structures of known active and decoy molecules from the DEKOIS 2.0 library. Based on the predicted binding energies, the docking programs ranked the screened molecules. We applied the usual performance evaluation metrices to evaluate the docking programs using the predicted ranks. Molecular dynamics of the top-ranked olive secoiridoid bound to PLpro and computation of MM-GBSA energy using three iterations during the last 50 ps of the analysis of the dynamics in Desmond supported the stability prediction. Results and discussions: Predictiveness curves suggested that AutoDock Vina has a better predictive ability than AutoDock, although there was a moderate correlation between the active molecules rankings (Kendall's correlation of rank (τ) = 0.581). Interestingly, two same molecules, Demethyloleuropein aglycone, and Oleuroside enriched the top 1 % ranked olive secoiridoids predicted by both programs. Demethyloleuropein aglycone bound to PLpro obtained by docking in AutoDock Vina when analyzed for stability by molecular dynamics simulation for 50 ns displayed an RMSD, RMSF<2 Å, and MM-GBSA energy of -94.54 ± 6.05 kcal/mol indicating good stability. Molecular dynamics also revealed the interactions of Demethyloleuropein aglycone with binding sites 2 and 3 of PLpro, suggesting a potent inhibition. In addition, for 98 % of the simulation time, two phenolic hydroxy groups of Demethyloleuropein aglycone maintained two hydrogen bonds with Asp302 of PLpro, specifying the significance of the groups in receptor binding. Conclusion: AutoDock Vina retrieved the active molecules accurately and predicted Demethyloleuropein aglycone as the best inhibitor of PLpro. The Arabian diet consisting of olive products rich in secoiridoids benefits from the PLpro inhibition property and reduces the risk of viral infection.

Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2.

BACKGROUND: The main protease of SARS-CoV-2 (Mpro) is one of the targets identified in SARS-CoV-2, the causative agent of COVID-19. The application of X-ray diffraction crystallography made available the three-dimensional structure of this protein target in complex with ligands, which paved the way for docking studies. OBJECTIVE: Our goal here is to review recent efforts in the application of docking simulations to identify inhibitors of the Mpro using the program AutoDock4. METHODS: We searched PubMed to identify studies that applied AutoDock4 for docking against this protein target. We used the structures available for Mpro to analyze intermolecular interactions and reviewed the methods used to search for inhibitors. RESULTS: The application of docking against the structures available for the Mpro found ligands with an estimated inhibition in the nanomolar range. Such computational approaches focused on the crystal structures revealed potential inhibitors of Mpro that might exhibit pharmacological activity against SARS-CoV-2. Nevertheless, most of these studies lack the proper validation of the docking protocol. Also, they all ignored the potential use of machine learning to predict affinity. CONCLUSION: The combination of structural data with computational approaches opened the possibility to accelerate the search for drugs to treat COVID-19. Several studies used AutoDock4 to search for inhibitors of Mpro. Most of them did not employ a validated docking protocol, which lends support to critics of their computational methodology. Furthermore, one of these studies reported the binding of chloroquine and hydroxychloroquine to Mpro. This study ignores the scientific evidence against the use of these antimalarial drugs to treat COVID-19.