Multi-Step In Silico Discovery of Natural Drugs against COVID-19 Targeting Main Protease.

In continuation of our antecedent work against COVID-19, three natural compounds, namely, Luteoside C (130), Kahalalide E (184), and Streptovaricin B (278) were determined as the most promising SARS-CoV-2 main protease (Mpro) inhibitors among 310 naturally originated antiviral compounds. This was performed via a multi-step in silico method. At first, a molecular structure similarity study was done with PRD_002214, the co-crystallized ligand of Mpro (PDB ID: 6LU7), and favored thirty compounds. Subsequently, the fingerprint study performed with respect to PRD_002214 resulted in the election of sixteen compounds (7, 128, 130, 156, 157, 158, 180, 184, 203, 204, 210, 237, 264, 276, 277, and 278). Then, results of molecular docking versus Mpro PDB ID: 6LU7 favored eight compounds (128, 130, 156, 180, 184, 203, 204, and 278) based on their binding affinities. Then, in silico toxicity studies were performed for the promising compounds and revealed that all of them have good toxicity profiles. Finally, molecular dynamic (MD) simulation experiments were carried out for compounds 130, 184, and 278, which exhibited the best binding modes against Mpro. MD tests revealed that luteoside C (130) has the greatest potential to inhibit SARS-CoV-2 main protease.

Computational Prediction of the Potential Target of SARS-CoV-2 Inhibitor Plitidepsin via Molecular Docking, Dynamic Simulations and MM-PBSA Calculations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication depends on the interaction between the viral proteins and the human translation machinery. The cytotoxic peptide plitidepsin was found to inhibit CoV-2 up to 90 % at a concentration of 0.88 nM. In vitro studies suggest that this activity may be attributed to the inhibition of the eukaryotic translation elongation factor 1A (eEF1A). However, recent reports raised the potential for other cellular targets which plitidepsin may use to exert its potent antiviral activity. The lack of data about these potential targets represents a major limitation for its structural optimization. This work describes the use of a molecular modeling approach to rationalize the in vitro antiviral activity of plitidepsin and to identify potential cellular targets. The developed protocol involves an initial molecular docking step followed by molecular dynamics and binding free energy calculations. The results reveal the potential for plitidepsin to bind to the active site of the key enzyme SARS-CoV-2 RdRp. The results also highlight the importance of van der Waals interactions for proper binding with the enzyme. We believe that the results presented in this study could provide the grounds for the optimization of plitidepsin analogs as SARS-CoV-2 inhibitors.

In silico identification of potential SARS COV-2 2'-O-methyltransferase inhibitor: fragment-based screening approach and MM-PBSA calculations.

In the present era, there are many efforts trying to face the emerging and successive waves of the COVID-19 pandemic. This has led to considering new and unusual targets for SARS CoV-2. 2'-O-Methyltransferase (nsp16) is a key and attractive target in the SARS CoV-2 life cycle since it is responsible for the viral RNA protection via a cap formation process. In this study, we propose a new potential inhibitor for SARS COV-2 2'-O-methyltransferase (nsp16). A fragment library was screened against the co-crystal structure of the SARS COV-2 2'-O-methyltransferase complexed with Sinefungin (nsp16 - PDB ID: 6WKQ), and consequently the best proposed fragments were linked via a de novo approach to build molecule AP-20. Molecule AP-20 displayed a superior docking score to Sinefungin and reproduced the key interactions in the binding site of 2'-O-methyltransferase. Three molecular dynamic simulations of the 2'-O-methyltransferase apo structure and its complexed forms with AP-20 and Sinefungin were performed for 150 nano-seconds to provide insights on the dynamic nature of such setups and to assess the stability of the proposed AP-20/enzyme complex. AP-20/enzyme complex demonstrated better stability for the ligand-enzyme complex compared to Sinefungin in a respective setup. Furthermore, MM-PBSA binding free energy calculations showed a better profile for AP-20/enzyme complex compared to Sinefungin/enzyme complex emphasizing the potential inhibitory effect of AP-20 on SARS COV-2 2'-O-methyltransferase. We endorse our designed molecule AP-20 to be further explored via experimental evaluations to confront the spread of the emerging COVID-19. Also, in silico ADME profiling has ascribed to AP-20 an excellent safety and metabolic stability profile.

In silico identification of novel SARS-COV-2 2'-O-methyltransferase (nsp16) inhibitors: structure-based virtual screening, molecular dynamics simulation and MM-PBSA approaches.

The novel coronavirus disease COVID-19, caused by the virus SARS CoV-2, has exerted a significant unprecedented economic and medical crisis, in addition to its impact on the daily life and health care systems all over the world. Regrettably, no vaccines or drugs are currently available for this new critical emerging human disease. Joining the global fight against COVID-19, in this study we aim at identifying a potential novel inhibitor for SARS COV-2 2'-O-methyltransferase (nsp16) which is one of the most attractive targets in the virus life cycle, responsible for the viral RNA protection via a cap formation process. Firstly, nsp16 enzyme bound to Sinefungin was retrieved from the protein data bank (PDB ID: 6WKQ), then, a 3D pharmacophore model was constructed to be applied to screen 48 Million drug-like compounds of the Zinc database. This resulted in only 24 compounds which were subsequently docked into the enzyme. The best four score-ordered hits from the docking outcome exhibited better scores compared to Sinefungin. Finally, three molecular dynamics (MD) simulation experiments for 150 ns were carried out as a refinement step for our proposed approach. The MD and MM-PBSA outputs revealed compound 11 as the best potential nsp16 inhibitor herein identified, as it displayed a better stability and average binding free energy for the ligand-enzyme complex compared to Sinefungin.