key: cord-0471156-jipujl09
authors: Piplani, Sakshi; Singh, Puneet; Petrovsky, Nikolai; Winkler, David A.
title: Computational screening of repurposed drugs and natural products against SARS-Cov-2 main protease (Mpro) as potential COVID-19 therapies
date: 2020-09-01
journal: nan
DOI: nan
sha: ccc0c5a4a53f3d1d3b54f28d58be92d9098a63b9
doc_id: 471156
cord_uid: jipujl09

There remains an urgent need to identify existing drugs that might be suitable for treating patients suffering from COVID-19 infection. Drugs rarely act at a single molecular target, with off target effects often being responsible for undesirable side effects and sometimes, beneficial synergy between targets for a specific illness. Off target activities have also led to blockbuster drugs in some cases, e.g. Viagra for erectile dysfunction and Minoxidil for male pattern hair loss. Drugs already in use or in clinical trials plus approved natural products constitute a rich resource for discovery of therapeutic agents that can be repurposed for existing and new conditions, based on the rationale that they have already been assessed for safety in man. A key question then is how to rapidly and efficiently screen such compounds for activity against new pandemic pathogens such as COVID-19. Here we show how a fast and robust computational process can be used to screen large libraries of drugs and natural compounds to identify those that may inhibit the main protease of SARS-Cov-2 (3CL pro, Mpro). We show how the resulting shortlist of candidates with strongest binding affinities is highly enriched in compounds that have been independently identified as potential antivirals against COVID-19. The top candidates also include a substantial number of drugs and natural products not previously identified as having potential COVID-19 activity, thereby providing additional targets for experimental validation. This in silico screening pipeline may also be useful for repurposing of existing drugs and discovery of new drug candidates against other medically important pathogens and for use in future pandemics.

. Spectrum of antiviral activity and nature of assessment for four antiviral hit drugs.

Simeprevir was reported to be an inhibitor of the 3CLPro protease by Abhithaj et al. 23 They used a pharmacophore search followed by grid-based ligand docking (GLIDE, Schrodinger) and binding energy estimates from the MMGBSA method of -81.7 kcal/mol. However, they did not use MD to simulate the interaction of Simeprevir in the 3CLPro binding site. Similarly, Sofosbuvir was reported to be a strong inhibitor of the protease by Lo et al. 24 Using a Vero E6 cellular infection model, they also reported that Simeprevir was the only drug among their prioritized candidates that suppressed SARS-CoV-2 replication at below 10 μM.

Dose-response studies showed that Simeprevir had an EC50 of 4 μM, and CC50 of 20 μM, similar to Remdesivir in their experiments. The potential protease inhibition properties of Lopinavir and Ritonavir were reported by Bolcato et al., who used supervised MD to calculate the trajectories of the ligands in the protease binding site. 25 Costanzo and colleagues likewise reported high protease binding for these two antiviral drugs. 26 They also reported updates on experimental drugs successfully employed in the treatment of the disease caused by SARS-CoV-2 coronavirus. Patient recovery has been reported after treatment with lopinavir/ritonavir (used to treat HIV infection) in combination with the antiflu drug oseltamivir. Muralidharan 31 In their assay, Simeprivir exhibited an IC50 of 14±3 µM.

The most interesting potential protease inhibitors from our study are the ergot alkaloids ergotamine, mergocriptine, the thrombopoietin receptor agonist eltrombopag (ranked 13 with ΔGMMPBSA=-28.2 kcal/mol, see Supplementary Table 1) , bemcentinib, PC786, and montelukast.

These drugs were predicted to have better binding energies than the antiviral drugs discussed above and have a higher degree of novelty.

Gurung et al. reported potential binding of ergotamine to the SAR-Cov-2 main protease in a preprint. 32 The employed AutoDock Vina but without subsequent MD simulation of the complex. They reported the binding energy as −9.4 kcal/mol for dihydroergotamine and -9.3 kcal/mol for ergotamine. Mevada et al. also reported the in-silico estimates of the binding of ergotamine to the protease using AutoDock Vina for the virtual screening. 33 They found the drug bound with an energy of -10.2 kcal/mol, calculated using Vina (no subsequent MD simulation). Gul et al. used a similar docking approach, this time with MD simulation, and identified ergotamine and its derivatives dihydroergotamine and bromocriptine to have high binding affinity to SARS-Cov-2 3CLpro. Ergotamine is an alpha-1 selective adrenergic agonist and vasoconstrictor that had a high docking binding energy against SARS-Cov-2 M pro of -8.6 kcal/mol. Dihydroergotamine, the 9,10-alpha-dihydro derivative of ergotamine, showed similar high affinity of -8.6 kcal/mol and bromocryptine -9.2 kcal/mol. Ergotamine has also been predicted to bind tightly to the SARS-Cov-2 spike (S) protein. 34 Figure 3 shows a LigPlot representation of the interactions of key functional groups in ergotamine and mergocriptine with protease active site residues. These are also listed in Supplementary Table 2 

Montelukast is a cysteinyl leukotriene receptor antagonist used treat asthma and allergic rhinitis.

It reduces pulmonary responses to antigen, tissue eosinophilia and IL-5 expression in inflammatory cells and decreases elevated levels of IL-1β and IL8 in viral upper respiratory tract infections. 35 Several computational have suggested putative binding to the terminal site of M pro .

Montelukast has been shown to inhibit at least one other protease, eosinophil protease. 36 Mansoor and colleagues proposed that it could bind to M pro on the basis of a simple molecular docking study. 37 Wu et al also reported putative binding of montelukast to M pro in a computational study using the same Internal Coordinate Mechanics modelling methods. 38 No accurate binding affinities were reported in either study. Figure 4 shows a LigPlot representation of the interactions of key functional groups in montelukast with protease active site residues.

These are also listed in Supplementary Table 2 for reference. 

Bemcentinib selectively inhibits AXL kinase activity, which blocks viral entry and enhances the antiviral type I interferon response. It's in vitro activity against SARS-Cov-2 has been assessed by several groups. In a Vero cell assay, Liu et al reported 10-40% protection at 50µM. 30 However, in an alternative assay using human Huh7.5 cells, 39 Bemcentinib exhibited an IC50 of 100nM and CC50 of 4.7µM. They also developed an assay in Vero cells and reported the IC50 was 470nM and CC50 was 1.6µM, considerably higher activity than that reported by Liu et al. As a result it is an investigational treatment for COVID-19 (www.clinicaltrialsregister.eu). Figure 5 shows a LigPlot representation of the interactions of key functional groups in bemcentinib with protease active site residues. These are also listed in Supplementary Table 2 for reference. 

PC786 targets the respiratory syncytial virus (RSV) L protein and is designed to be a topical inhalation treatment. There is very little published work on the SAR-Cov-2 efficacy or predicting binding affinity to M pro . Panda and coworkers reported a binding energy ΔGbind of PC786 of −179.79, tighter binding than calculated for lopinavir (−131.49 kJ/mol), using a combined docking and MD approach. 40 Like our study, they employed Autodock Vina to dock a molecular library into the active site of M pro , followed by MD simulation using GROMACS. Figure 6 shows a LigPlot representation of the interactions of key functional groups in PC786 with protease active site residues. These are also listed in Supplementary Table 2 for reference. 

The predicted binding energies of the 84 drugs in the short list are summarized in Supplementary   Table 1 . We have also reviewed the literature for other in silico studies that have also identified some of these hit compounds as potential M pro inhibitors and have listed experimental in vitro and in vivo results and clinical trials in progress for drugs on the list. Two thirds of the drugs on the list have been reported to be potential inhibitors of SARS-Cov-2 target proteins, largely M pro but also RdRp, spike, helicase, human ACE2, 2'-O-methyltransferase nsp16/nsp10 complex, nsp1, PL pro , nsp3, and nsp12. Satisfyingly, those with the best predicted binding affinity from our study have also been of greatest interest clinically, with more in vitro assay results and clinical trials for drugs with the highest binding affinities. This suggests that our screening and MD simulation methods are sufficiently robust and accurate to identify drugs for repurposing against SARS-Cov-2 and, more broadly, other coronaviruses. The 33% of drugs in the hit list that have no reported studies are therefore also of interest as novel drugs for COVID-19. We discuss some of the more interesting and novel hit compounds with higher binding affinities.

Eltrombopag is a TPO agonist that acts at the transmembrane domain of its cognate receptor C-Mpl via a histidine residue that occurs only in humans and apes. It scored highly in the docking studies, suggesting it could inhibit the 3CL protease and exhibit antiviral activity. Several other in silico screening studies also identified eltrombopag as a potential SARS-Cov-2 drug. Eltrombopag has also been proposed as a potential drug against SARS-CoV-2 spike protein on the basis of predicted strong binding to a pocket in the fusion cores of S2 domain. 41 Eltrombopag was also identified as a high binding affinity to human angiotensin converting enzyme 2 (ACE2), the primary binding site for the spike protein. Their virtual screen also used Autodock Vina, but no subsequent MD simulation was used for the top hit compounds from the screen. SPR was used to assess the binding of the drug to M pro . Figure 7 shows a LigPlot representation of the interactions of key functional groups in eltrombopag with protease active site residues. These are also listed in Supplementary Table 2 Eltrombopag is of particular interest as a M pro inhibitor lead because it is novel and is also a member of a large class of small molecular TPO receptor agonists that may also exhibit activity against the viral protease, and potentially the spike protein and human ACE2. 43 . However, given the clotting disorders that SAR-Cov-2 generates, the TPOR agonist activities would need to be minimized to prevent platelet enhancement, while retaining or enhancing the antiviral activities.

Apart from the drugs discussed above, several other drugs in the list in Supplementary Table   1 

Our virtual screening approach that applies Autodock Vina and MD simulation in tandem to calculate binding energies for repurposed drugs has identified 84 promising compounds for treating SARS-Cov2 infections. The screening was applied against the viral main protease M pro (3CLpro). The top hits from out study consisted of a mixture of antiviral agents , natural products and drugs that were developed for other applications and that have other models of action. The prognostic value of our computational approach has been demonstrated by the fact that it identified a diverse range of drugs that have been reported in other computational studies or that exhibit useful SARS-Cov-2 antiviral effects in vitro. The antiviral drugs simeprevir, sofosbuvir, lopinavir, ritonavir and remdesivir exhibit strong antiviral properties and several in in clinical trial or use against SARS-Cov-2. These drugs have been identified as binding to M pro also by numerous virtual screening studies and by in vitro assays. The more interesting and least studied lead drugs amongst our candidate list, bemcentinib, PC786, montelukast, ergotamine and mergocriptine, were predicted to have binding affinities equal to or greater than the antiviral drugs, and have also been shown to have in vitro antiviral activity against SARS-CoV-2. A few computational studies mostly using less rigorous methods than those we employed here, have also suggested that these drugs may bind to M pro .

This high validation success rate strongly suggests that this type of virtual screening approach is capable of identifying compounds with potentially useful activity against SARS-CoV-2 and, by analogy, other coronaviruses. In particular, the 28 drugs for which no SARS-CoV-2 activity has been yet reported may be of particular interest for in vitro screening. The results of the current drug repurposing study provides information that could be useful to identify additional candidate drugs for testing for use in the current pandemic, as well as a rational computational paradigm for identifying therapeutic agents for future viral pandemics.

The crystal structure of the COVID-19 M pro (Figure 8 ) was downloaded from the RCSB PDB (http://www.rcsb.org; refcode 6Y2F). 18 

The top screened compound complexes with protease were minimized with CHARMm force field.

The topology files of the ligands were prepared from Swissparam (http://www.swissparam.ch/) 57 and minimized in Gromacs2020 (http://www.gromacs.org/). 58 is sufficiently long for convergence. Duplicate production runs starting with different random seeds were also run to allow estimates of binding energy uncertainties to be determined.

The binding free energies of the protein-protein complexes were evaluated in two ways.

The traditional method is to calculate the energies of solvated SARS-Cov-2 protease and small molecule ligands and that of the bound complex and derive the binding energy by subtraction.

ΔG (binding, aq) = ΔG (complex, aq) -(ΔG (protein, aq) + ΔG (ligand, aq)

We also calculated binding energies using the molecular mechanics Poisson Boltzmann surface area (MM/PBSA) tool in GROMACS that is derived from the nonbonded interaction energies of the complex. The method is also widely used method for binding free energy calculations. 

where ΔG (R:L,solv), ΔG (R,solv) and ΔG (L,solv) are solvation free energies of complex, receptor and ligand, respectively.

While this manuscript was in preparation, Guterres and Im showed how substantial improvement in protein-ligand docking results could be achieved using high-throughput MD simulations. 60 As with our study, they also employed AutoDock Vina for docking, followed by MD simulation using CHARMM. The MD parameters they advocated were very similar to those used in our study.

Proteins were solvated in a box of TIP3P water molecules extending 10 Å beyond the proteins and the particle-mesh Ewald method was used for electrostatic interactions. Nonbonded interactions over 10 and 12 Å were truncated. Their systems were minimized for 5000 steps using the steepest descent method followed by 1 ns equilibration with an NVT setting. For each protein-ligand complex, they ran 3 × 100 ns production runs from the same initial structure using different initial velocity random seeds and an integration step size of 2 fs. Over 56 protein targets (of 7 different 

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We would like to thank Harinda Rajapaksha for assistance to optimise GROMACS for this project.We would also like to thank Oracle for providing their Cloud computing resources for the modelling studies described herein. In particular, we wish to thank Peter Winn, Dennis Ward, and Alison Derbenwick Miller from Oracle in facilitating these studies. The opinions expressed herein are solely those of the individual authors and should not be inferred to reflect the views of their affiliated institutions, funding bodies or Oracle corporation.

Petrovsky -conceived project, analysed data, contributed to manuscript; Piplani and Kumar Singh -performed the computations, analysed data, contributed to the manuscript; Winkler -analysed data and contributed to manuscript