Computational screening for potential drug candidates against the SARS-CoV-2 main protease

Identification of and Mechanistic Insights into SARS-CoV-2 Main Protease Non-Covalent Inhibitors: An In-Silico Study

The indispensable role of the SARS-CoV-2 main protease (Mpro) in the viral replication cycle and its dissimilarity to human proteases make Mpro a promising drug target. In order to identify the non-covalent Mpro inhibitors, we performed a comprehensive study using a combined computational strategy. We first screened the ZINC purchasable compound database using the pharmacophore model generated from the reference crystal structure of Mpro complexed with the inhibitor ML188. The hit compounds were then filtered by molecular docking and predicted parameters of drug-likeness and pharmacokinetics. The final molecular dynamics (MD) simulations identified three effective candidate inhibitors (ECIs) capable of maintaining binding within the substrate-binding cavity of Mpro. We further performed comparative analyses of the reference and effective complexes in terms of dynamics, thermodynamics, binding free energy (BFE), and interaction energies and modes. The results reveal that, when compared to the inter-molecular electrostatic forces/interactions, the inter-molecular van der Waals (vdW) forces/interactions are far more important in maintaining the association and determining the high affinity. Given the un-favorable effects of the inter-molecular electrostatic interactions-association destabilization by the competitive hydrogen bond (HB) interactions and the reduced binding affinity arising from the un-compensable increase in the electrostatic desolvation penalty-we suggest that enhancing the inter-molecular vdW interactions while avoiding introducing the deeply buried HBs may be a promising strategy in future inhibitor optimization.

Natural therapeutics against SARS CoV2: the potentiality and challenges

The incidence of the COVID-19 pandemic completely reoriented global socio-economic parameters and human civilization have experienced the worst situation in the recent past. The rapid mutation rates in viruses have continuously been creating emerging variants of concerns (VOCs) which devastated different parts of the world with subsequent waves of infection. Although, series of antiviral drugs and vaccines were formulated but cent percent effectiveness of these drugs is still awaited. Many of these drugs have different side effects which necessitate proper trial before release. Plants are the storehouse of antimicrobial metabolites which have also long been utilized as traditional medicines against different viral infections. Although, proper mechanism of action of these traditional medicines are unknown, they may be a potential source of effective anti-COVID drug for future implications. Advanced bioinformatic applications have opened up a new arena in predicting these repurposed drugs as a potential COVID mitigator. The present review summarizes brief accounts of the corona virus with their possible entry mechanism. This study also tries to classify different possible anti COVID-19 plant-derived metabolites based on their probable mode of action. This review will surely provide useful information on repurposed drugs to combat COVID-19 in this critical situation.

DK, Ragunathan V, Tiwari P, A. S, P BD. Molecular docking, validation, dynamics simulations, and pharmacokinetic prediction of natural compounds against the SARS-CoV-2 main-protease

The study aims to evaluate the potency of two hundred natural antiviral phytocompounds against the active site of the Severe Acquired Respiratory Syndrome - Coronavirus - 2 (SARS-CoV-2) Main-Protease (Mpro) using AutoDock 4.2.6. The three- dimensional crystal structure of the Mpro (PDB Id: 6LU7) was retrieved from the Protein Data Bank (PDB), the active site was predicted using MetaPocket 2.0. Food and Drug Administration (FDA) approved viral protease inhibitors were used as standards for comparison of results. The compounds theaflavin-3-3'-digallate, rutin, hypericin, robustaflavone, and (-)-solenolide A with respective binding energy of -12.41 (Ki = 794.96 pM); -11.33 (Ki = 4.98 nM); -11.17 (Ki = 6.54 nM); -10.92 (Ki = 9.85 nM); and -10.82 kcal/mol (Ki = 11.88 nM) were ranked top as Coronavirus Disease - 2019 (COVID-19) Mpro inhibitors. The interacting amino acid residues were visualized using Discovery Studio 3.5 to elucidate the 2-dimensional and 3-dimensional interactions. The study was validated by i) re-docking the N3-peptide inhibitor-Mpro and superimposing them onto co-crystallized complex and ii) docking decoy ligands to Mpro. The ligands that showed low binding energy were further predicted for and pharmacokinetic properties and Lipinski's rule of 5 and the results are tabulated and discussed. Molecular dynamics simulations were performed for 50 ns for those compounds using the Desmond package, Schrödinger to assess the conformational stability and fluctuations of protein-ligand complexes during the simulation. Thus, the natural compounds could act as a lead for the COVID-19 regimen after in-vitro and in- vivo clinical trials.Communicated by Ramaswamy H. Sarma.

Selection of SARS-CoV-2 main protease inhibitor using structure-based virtual screening

Background: Conserved domains within SARS coronavirus 2 nonstructural proteins represent key targets for the design of novel inhibitors. Methods: The authors aimed to identify potential SARS coronavirus 2 NSP5 inhibitors using the ZINC database along with structure-based virtual screening and molecular dynamics simulation. Results: Of 13,840 compounds, 353 with robust docking scores were initially chosen, of which ten hit compounds were selected as candidates for detailed analyses. Three compounds were selected as coronavirus NSP5 inhibitors after passing absorption, distribution, metabolism, excretion and toxicity study; root and mean square deviation; and radius of gyration calculations. Conclusion: ZINC000049899562, ZINC000169336666 and ZINC000095542577 are potential NSP5 protease inhibitors that warrant further experimental studies.

In silico investigation of potential inhibitors to main protease and spike protein of SARS-CoV-2 in propolis

Docking analysis of propolis's natural compound was successfully performed against SARS-CoV-2 main protease (Mpro) and spike protein subunit 2 (S2). Initially, the propolis's protein was screened using chromatography analysis and successfully identified 22 compounds in the propolis. Four compounds were further investigated, i.e., neoblavaisoflavone, methylophiopogonone A, 3'-Methoxydaidzin, and genistin. The binding affinity of 3'-Methoxydaidzin was -7.7 kcal/mol, which is similar to nelfinavir (control), while the others were -7.6 kcal/mol. However, we found the key residue of Glu A:166 in the methylophiopogonone A and genistin, even though the predicted binding energy slightly higher than nelfinavir. In contrast, the predicted binding affinity of neoblavaisoflavone, methylophiopogonone A, 3'-Methoxydaidzin, and genistin against S2 were -8.1, -8.2, -8.3, and -8.3 kcal/mol, respectively, which is far below of the control (pravastatin, -7.3 kcal/mol). Instead of conventional hydrogen bonding, the π bonding influenced the binding affinity against S2. The results reveal that this is the first report about methylophiopogonone A, 3'-Methoxydaidzin, and genistin as candidates for anti-viral agents. Those compounds can then be further explored and used as a parent backbone molecule to develop a new supplementation for preventing SARS-CoV-2 infections during COVID-19 outbreaks.

Prospective Role of Peptide-Based Antiviral Therapy Against the Main Protease of SARS-CoV-2

The recently emerged coronavirus (SARS-CoV-2) has created a crisis in world health, and economic sectors as an effective treatment or vaccine candidates are still developing. Besides, negative results in clinical trials and effective cheap solution against this deadly virus have brought new challenges. The viral protein, the main protease from SARS-CoV-2, can be effectively targeted due to its viral replication and pathogenesis role. In this study, we have enlisted 88 peptides from the AVPdb database. The peptide molecules were modeled to carry out the docking interactions. The four peptides molecules, P14, P39, P41, and P74, had more binding energy than the rest of the peptides in multiple docking programs. Interestingly, the active points of the main protease from SARS-CoV-2, Cys145, Leu141, Ser139, Phe140, Leu167, and Gln189, showed nonbonded interaction with the peptide molecules. The molecular dynamics simulation study was carried out for 200 ns to find out the docked complex’s stability where their stability index was proved to be positive compared to the apo and control complex. Our computational works based on peptide molecules may aid the future development of therapeutic options against SARS-CoV-2.