Identification of saquinavir as a potent inhibitor of dimeric SARS-CoV2 main protease through MM/GBSA.

Among targets selected for studies aimed at identifying potential inhibitors against COVID-19, SARS-CoV2 main proteinase (Mpro) is highlighted. Mpro is indispensable for virus replication and is a promising target of potential inhibitors of COVID-19. Recently, monomeric SARS-CoV2 Mpro, drug repurposing, and docking methods have facilitated the identification of several potential inhibitors. Results were refined through the assessment of dimeric SARS-CoV2 Mpro, which represents the functional state of enzyme. Docking and molecular dynamics (MD) simulations combined with molecular mechanics/generalized Born surface area (MM/GBSA) studies indicated that dimeric Mpro most significantly impacts binding affinity tendency compared with the monomeric state, which suggests that dimeric state is most useful when performing studies aimed at identifying drugs targeting Mpro. In this study, we extend previous research by performing docking and MD simulation studies coupled with an MM/GBSA approach to assess binding of dimeric SARS-CoV2 Mpro to 12 FDA-approved drugs (darunavir, indinavir, saquinavir, tipranavir, diosmin, hesperidin, rutin, raltegravir, velpatasvir, ledipasvir, rosuvastatin, and bortezomib), which were identified as the best candidates for the treatment of COVID-19 in some previous dockings studies involving monomeric SARS-CoV2 Mpro. This analysis identified saquinavir as a potent inhibitor of dimeric SARS-CoV2 Mpro; therefore, the compound may have clinical utility against COVID-19. Graphical abstract.

Molecular Docking and Virtual Screening Based Prediction of Drugs for COVID-19.

AIMS: To predict potential drugs for COVID-19 by using molecular docking for virtual screening of drugs approved for other clinical applications. BACKGROUND: SARS-CoV-2 is the betacoronavirus responsible for the COVID-19 pandemic. It was listed as a potential global health threat by the WHO due to high mortality, high basic reproduction number, and lack of clinically approved drugs and vaccines. The genome of the virus responsible for COVID-19 has been sequenced. In addition, the three-dimensional structure of the main protease has been determined experimentally. OBJECTIVE: To identify potential drugs that can be repurposed for treatment of COVID-19 by using molecular docking based virtual screening of all approved drugs. METHODS: A list of drugs approved for clinical use was obtained from the SuperDRUG2 database. The structure of the target in the apo form, as well as structures of several target-ligand complexes, were obtained from RCSB PDB. The structure of SARS-CoV-2 Mpro determined from X-ray diffraction data was used as the target. Data regarding drugs in clinical trials for COVID-19 was obtained from clinicaltrials.org. Input for molecular docking based virtual screening was prepared by using Obabel and customized python, bash, and awk scripts. Molecular docking calculations were carried out with Vina and SMINA, and the docked conformations were analyzed and visualized with PLIP, Pymol, and Rasmol. RESULTS: Among the drugs that are being tested in clinical trials for COVID-19, Danoprevir and Darunavir were predicted to have the highest binding affinity for the Main protease (Mpro) target of SARS-CoV-2. Saquinavir and Beclabuvir were identified as the best novel candidates for COVID-19 therapy by using Virtual Screening of drugs approved for other clinical indications. CONCLUSION: Protease inhibitors approved for treatment of other viral diseases have the potential to be repurposed for treatment of COVID-19.

A search for medications to treat COVID-19 via in silico molecular docking models of the SARS-CoV-2 spike glycoprotein and 3CL protease.

BACKGROUND: The COVID-19 has now been declared a global pandemic by the World Health Organization. There is an emergent need to search for possible medications. METHOD: Utilization of the available sequence information, homology modeling, and in slico docking a number of available medications might prove to be effective in inhibiting the SARS-CoV-2 two main drug targets, the spike glycoprotein, and the 3CL protease. RESULTS: Several compounds were determined from the in silico docking models that might prove to be effective inhibitors for SARS-CoV-2. Several antiviral medications: Zanamivir, Indinavir, Saquinavir, and Remdesivir show potential as and 3CLPRO main proteinase inhibitors and as a treatment for COVID-19. CONCLUSION: Zanamivir, Indinavir, Saquinavir, and Remdesivir are among the exciting hits on the 3CLPRO main proteinase. It is also exciting to uncover that Flavin Adenine Dinucleotide (FAD) Adeflavin, B2 deficiency medicine, and Coenzyme A, a coenzyme, may also be potentially used for the treatment of SARS-CoV-2 infections. The use of these off-label medications may be beneficial in the treatment of the COVID-19.