Computational screening of repurposed drugs and natural products against SARS-Cov-2 main protease (Mpro) as potential COVID-19 therapies
Sakshi Piplani, Puneet Singh, Nikolai Petrovsky, David A. Winkler
CComputational screening of repurposed drugs and natural products against SARS-Cov-2 main protease ( M pro ) as potential COVID-19 therapies Sakshi Piplani , Puneet Singh , Nikolai Petrovsky David A. Winkler * contributed equally as first authors bstract 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, M pro ). 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. ntroduction
The devastating impact of the COVID-19 pandemic caused by SARS coronavirus-2 (SARS-CoV-2) has stimulated unprecedented international activity to discover effective vaccines and drugs for this and other pathogenic coronaviruses such as SARS and MERS CoV.
Computational methods offer considerable promise for determining the affinities of small drug-like molecules for SARS-Cov-2 protein targets. Recent papers in Science have reported the application of computational de novo drug design based on the structures of the SARS-Cov-2 protease.
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Clearly, design of potent new drugs for coronaviruses is very important for future pandemic preparedness, given that the last three serious epidemics have been caused by coronaviruses. However, to make an impact on the current COVID-19 pandemic, it is only feasible to use drugs that are already registered (off label use), have been through at least phase 1 clinical trials to establish initial human safety, or are approved natural products. Any COVID-19 drug candidates identified in this way can then be used very quickly, as their safety and pharmacokinetics should be already well understood. Drugs that reduce viral replication primarily by targeting viral proteases and polymerases are classified as direct-acting antivirals and are the focus of the current work. Other studies have explored host-targeted drugs that inhibit cellular functions required for viral replication and thereby inhibit SARS-Cov-2 infection, albeit with more potential for host side effects. The SARS-Cov-2 genome encodes > 20 proteins, many of which are potential antiviral drug targets (Figure 1). Two proteases (PL pro and 3CL pro) are essential for virus replication. These enzymes cleave the PP1A and PP1AB polyproteins into functional components. 3-chymotrypsin-like protease (3CLpro, aka main protease, M pro ) catalytically self-cleaves a peptide bond between a glutamine at position P1 and a small amino acid (serine, alanine, or glycine) at osition P1'. This protease corresponds to non-structural protein 5 (nsp5), the main protease in coronaviruses. 3CL protease is crucial to the processing of the coronavirus replicase polyprotein (P0C6U8), cleaving it at 11 conserved sites. 3CL protease employs a cys-his catalytic dyad in its active site where the cysteine sulfur is the nucleophile and the histidine imidazole ring is a general base. Very recent research has shown that strong M pro inhibitors can substantially reduce SARS-Cov-2 virus titres, reduce weight loss and improve survival in mice, making M pro a promising drug target for structure-based drug discovery. Figure 1 . Virus entry and replicative cycle. M pro produces non-structural proteins, Nsps, that are essential for assembly of the viral replication transcription complex needed for RNA synthesis. Inhibitors bind to M pro , resulting in failure of virion assembly and inhibition of release of new functional virions. Adapted from Mengist et al. https://creativecommons.org/licenses/by/4.0/ Computational methods can rapidly and efficiently identify candidate drugs for repurposing in pandemic situations where speed is of utmost importance. Here we used molecular docking followed by high throughput molecular dynamics simulations to prioritize rom an initial large number of licensed or clinical trial drugs and natural products, a short list of the most promising candidates. Molecular dynamics calculations were used to predict the optimal binding poses and binding energies for 84 of the top hits from docking-based virtual screening to the SARS-CoV-2 M pro . Finally, the top candidates were ranked based on binding affinity and novelty, for COVID-19 repurposing. Results and discussion
The binding energies of the 84 top ranked ligands from the docking calculations are listed in Supplementary Table 1. The ten drugs with the tightest binding to M pro are summarized in Table 1, together with their GMXPBSA binding energies. The calculated binding energy of several of the antiviral drugs, namely, simeprevir, sofosbuvir, lopinavir, and ritonavir are very similar, within the uncertainties in calculated energies. Several of the top hits were antivirals identified in other in silico docking studies or wet-lab SARS-CoV-2 activity studies. This provides a degree of validation that our computational methods are appropriate and are yielding similar results to other published studies for some well-studied antiviral drugs. The web site DrugVirus.info provides a concise picture of the broad-spectrum antiviral activity of a range of drugs; a summary for four of the antiviral hits from our in silico screens is provided in Figure 2. igure 2 . 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. 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. 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 EC of 4 μM, and CC of 20 μM, similar to Remdesivir in their experiments. Table 1 . Binding energies of 10 top ranked predicted small molecule ligands to SARS-Cov-2 M pro . ID Structure Description ΔG
MMPBSA (ΔG bind ) (kcal/mol) C3809489 Bemcentinib Inhibitor of the kinase domain of AXL receptor. -34.7±2.6 (-30.7) C4291143 PC786 Respiratory syncytial virus (RSV) L protein polymerase inhibitor. -33.1±0.3 (-29.2)
NHNNN NH NNN
OSNHNN NO O O HNF
D Structure Description ΔG
MMPBSA (ΔG bind ) (kcal/mol) C787 Montelukast Leukotriene receptor antagonist used with cortico-steroids for asthma therapy. -32.7±0.2 (-20.6) C442 Ergotamine Alpha-1 selective adrenergic agonist used in migraine treatment. -31.5±0.3 (-28.7) D06290 Simeprevir Hepatitis C virus (HCV) NS3/4A protease inhibitor. -31.4±0.2 (-29.2) D08934 Sofosbuvir Nucleotide prodrug and HCV NS5B polymerase inhibitor -31.0±0.5 (-22.8) D01601 Lopinavir Antiretroviral protease inhibitor for treatment of HIV-1 -30.7±0.3 (-20.4) D00503 Ritonavir Peptidomimetic inhibitor of HIV-1 and HIV-2 proteases -30.5±0.5 (-21.3)
N ClS O OHOH
ONO N OOHNH O N NH
N O O NNHOO NHSO ON SO
OOHNPO O O N ONHOFHOO
OOHNNHONHNO OH
NS N O HN O NH HN O O NSOH
D Structure Description ΔG
MMPBSA (ΔG bind ) (kcal/mol) C2105887 Mergocriptine Synthetic ergot derivative, dopamine receptor agonist. -30.0±0.3 (-17.9) D14761 Remdesivir Viral RNA-dependent RNA polymerase inhibitor. -30.0±0.2 (-27.1) 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. Costanzo and colleagues likewise reported high protease binding for these two antiviral drugs. 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 anti-flu drug oseltamivir. Muralidharan et al. also used AutoDock (another docking program similar to Vina produced by the Scripps group) followed by MD simulations using the Generalised Amber Force Field (GAFF) in Amber16 to screen for repurposed drugs They reported AutoDock binding energies for lopinavir, oseltamivir and ritonavir of −4.1 kcal/mol, −4.65 kcal/mol, −5.11 kcal/mol, respectively but did not provide the binding energies from the MD calculations. The best-known antiviral drug, which has been the subject of several clinical trials for COVID-19, is Remdesivir. The potential inhibition of the protease by this drug has been reported by several computational screening studies. For example, Al-Khafaji and
NONHONO N OOH NH
OONHPO OONNNNH N HO HO O olleagues reported a combined computational docking and MD study of a range of antiviral drugs to the viral protease. They calculated a binding energy for remdesivir of −65.19 kcal/mol from a
GROMACS simulation and a MMGBSA binding energy calculation. Beck et al. reported a K d for binding of remdesivir to 3CLPro of 113 nM using a deep learning model. Liu et al reported an in vitro assay that exploited the pronounced cytopathic effects of SAR-Cov-2 on Vero cells and the ability of a range of antiviral drugs to protect cells against the virus. In their assay, Remdesivir exhibited an IC of 2.5µM and CC of 175µM, while Sofosbuvir, Lopinavir and Ritonavir were inactive. Similarly, Ma et al. reported a fluorescence resonance energy transfer (FRET)- based enzymatic assay for the SARS-CoV-2 M pro and applied it to screening a library of protease inhibitors. In their assay, Simeprivir exhibited an IC 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 ΔG MMPBSA =–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.
Ergot drugs
Gurung et al. reported potential binding of ergotamine to the SAR-Cov-2 main protease in a preprint. 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. 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 dentified 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. 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 for reference.
Figure 3 . LigPlot (left) and hydrophobic protein surface representation (right) of the main interactions between M pro and ergotamine (top) and mergocriptine (bottom).
Montelukast
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. 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. Mansoor and colleagues proposed that it could bind to M pro on the basis of a simple molecular docking study. Wu et al also reported putative binding of montelukast to M pro in a computational study using the same Internal Coordinate Mechanics modelling methods. 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.
Figure 4 . LigPlot (left) and hydrophobic protein surface representation (right) of the main interactions between M pro and montelukast.
Bemcentinib
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. However, in an alternative assay using human Huh7.5 cells, Bemcentinib exhibited an IC of 100nM and CC of 4.7µM. They also developed an assay in Vero cells and reported the IC was 470nM and CC Figure 5 . LigPlot (left) and hydrophobic protein surface representation (right) of the main interactions between M pro and bemcentinib.
PC786
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 ΔG bind of PC786 of −179.79, tighter binding than calculated for lopinavir (−131.49 kJ/mol), using a combined docking and MD approach. 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.
Figure 6 . LigPlot (left) and hydrophobic protein surface representation (right) of the main interactions between M pro and PC786.
Other novel putative M pro inhibitors from the short list of 84 drugs
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 o 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
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. Feng et al.’s studies suggested that eltrombopag bound not only to 3CL active site but also to the viral S-protein and to human ACE2. This potential synergistic polypharmacy could be particularly beneficial. Very little has been reported on the direct antiviral activity of eltrombopag. Recently, Vogel et al. reported direct inhibition of cytomegalovirus (CMV) by therapeutic doses of eltrombopag used to treat thrombocytopenia. They showed that eltrombopag inhibits the late stages of the HCMV replication cycle and reduces virus titres by 1.8 × 10 -fold at 10µM and by 15-fold at 500 nM. They suggested the mode of action was iron chelation and showed that eltrombopag was synergistic with ganciclovir in preventing viral replication. 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. 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 epresentation of the interactions of key functional groups in eltrombopag with protease active site residues. These are also listed in Supplementary Table 2 for reference.
Figure 7.
LigPlot (left) and hydrophobic M pro protein surface representation (right) of the main interactions between M pro and eltrombopag and M pro . 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. . 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 are of interest. There are several other ergot derivatives with good predicted binding affinities to M pro . Metergotamine and dihydroergocristine were predicted to have D G bind of –29 and –24 cal/mol respectively. Other drugs with binding energies stronger than –25 kcal/mol include galicaftor (clinical trial for cystic fibrosis), rolitetracycline (broad spectrum antibiotic), disogluside (natural product from Dioscorea nipponica Makino that reduces liver chronic inflammation and fibrosis), zafirlukast (leukotriene receptor antagonist for asthma), diosmin (a natural flavone for treating venous disease), AZD-5991 (clinical trial for relapsed or refractory haematologic malignancies), and ruzasvir (clinical trials for treatment of hepatitis C). Li et al. has reported predicted M pro binding for galicaftor. The protease binding of rolitetracycline has been reported by Durdagi et al. and Gul et al.
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Zhu and corkers measured the SARs-Cov-2 and M pro inhibition of zafirlukast. The IC for M pro was 24 µM and the EC for the virus >20µM. The potential of the natural product diosmin as an antiviral agent targeting Mpro has also been reported in several recent computational studies. Chakraborti et al. reported the potential of ruzasvir as a drug against SARS-Cov-2, although no data were provided. These drugs and natural products merit assessment in SAR-Cov-2 assays and M pro inhibition experiments.
Conclusions
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 seful 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.
Materials and Methods
Protein structure preparation and grid preparation
The crystal structure of the COVID-19 M pro Figure 8 . 3D structure of SARS-Cov-2 M pro AutoDock Tools (ADT) software was used to prepare the required files for Autodock Vina by assigning hydrogen polarities, calculating Gasteiger charges to protein structures and converting protein structures from the .pdb file format to .pdbqt format. . The surface area of the 3CLPro binding pocket is 335 Å , volume – 364.101 Å . Screening databases
Drugs database were downloaded from the DrugBank database (Wishart et al., 2018) and CHEMBL database (FDA approved) (Gaulton et al., 2017). A total of 8773 and 13,308 drugs were retrieved from Drugbank and CHEMBL database, respectively. The drugs were downloaded in sdf format and converted to .pdbqt format using Raccoon (Forli et al., 2016). ocking Methodology
Small molecule ligand structures were docked against protein structure using the AutoDock Vina (version 1.1.3) package. AutoDock Vina employs gradient-based conformational search approach and an energy-based empirical scoring function. AutoDock Vina is also flexible, easily scripted, extensively validated in many published studies with a variety of proteins and ligands and takes advantage of large multi-CPU or -GPU machines to run many calculations in parallel. The code has also been employed very successfully to dock millions of small molecule drug candidates into a series of protein targets to discover new potent drug leads. The package includes useful scripts for generating modified .pdb files required for grid calculations and for setting up the grid calculations around each protein automatically. The software requires the removal of hydrogens, addition of polar hydrogens, setting of the correct atom types, and calculation of atom charges compatible with the AutoGrid code. The algorithm generates a grid around each protein and calculates the interaction energy of a probe noble gas atom at each grid position outside and within internal cavities of the protein. The grid resolution was set to 1 Å, the maximum number of binding modes to output was fixed at 10, and the exhaustiveness level (controlling the number of independent runs performed) was set at 8. The docking employed a genetic algorithm to optimize the binding conformations of the ligands during docking to the protease site. Drugs were docked individually to the active site of M pro (3CLPro, refcode 6Y2F) with the grid coordinates (grid centre) and grid boxes of appropriate sizes generated by the bash script vina_screen.sh (Supplementary Information). The top scored compounds were identified with a python script script1.py (Supplementary Information) and subjected to molecular dynamic simulation. The docked structures were analysed using UCSF Chimera and LigPlot+ software to illustrate hydrogen-bond and hydrophobic interactions. A total of fifty top compounds selected from each f the Drugbank and CHEMBL compounds. Sixteen compounds were common to both database top hits. Molecular dynamics studies were conducted on the unique set of eighty-four compounds from both sets. Molecular Dynamics Simulation . Docked complexes of ligands and COVID-19 M pro protein were used as starting geometries for MD simulations. Simulations were carried out using the GPU accelerated version of the program with the CHARMm force field I periodic boundary conditions in ORACLE server. Docked complexes were immersed in a truncated octahedron box of TIP3P water molecules. The solvated box was further neutralized with Na+ or Cl− counter ions using the tleap program. Particle Mesh Ewald (PME) was employed to calculate the long-range electrostatic interactions. The cut-off distance for the long-range van der Waals (VDW) energy term was 12.0 Å. The whole system was minimized without any restraint. The above steps applied 2500 cycles of steepest descent minimization followed by 5000 cycles of conjugate gradient minimization. After system optimization, the MD simulations was initiated by gradually heating each system in the NVT ensemble from 0 to 300 K for 50 ps using a Langevin thermostat with a coupling coefficient of 1.0/ps and with a force constant of 2.0 kcal/mol·Å2 on the complex. Finally, a production run of 20 ns of MD simulation was performed under a constant temperature of 300 K in the NPT ensemble with periodic boundary conditions for each system. During the MD procedure, the SHAKE algorithm was applied for the constraint of all covalent bonds involving hydrogen atoms. The time step was set to 2 fs. The structural stability of the complex was monitored by the RMSD and RMSF values of the backbone atoms of the entire rotein. Calculations were also performed for up to 100 ns on few compounds to ensure that 20ns 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) (1) 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. MMPBSA calculations were conducted by GMXPBSA 2.1 a suite based on Bash/Perl scripts for streamlining MM/PBSA calculations on structural ensembles derived from GROMACS trajectories and to automatically calculate binding free energies for protein–protein or ligand–protein. GMXPBSA 2.1 calculates diverse MM/PBSA energy contributions from molecular mechanics (MM) and electrostatic contribution to solvation (PB) and non-polar contribution to solvation (SA). This tool combines the capability of MD simulations (GROMACS) and the Poisson–Boltzmann equation (APBS) for calculating solvation energy (Baker et., 2001). The g_mmpbsa tool in GROMACS was used after molecular dynamics simulations, the output files obtained were used to post-process binding free energies by the single-trajectory MMPBSA method. In the current study we considered 100 frames at equal distance from 20ns trajectory files. Specifically, for a non-covalent binding interaction in the aqueous phase the binding free energy, ΔG (bind,aq), is: – G (bind,aqu) = ΔG (bind,vac) + ΔG (bind,solv) (2) where ΔG (bind,vac) is the binding free energy in vacuum, and ΔG(bind,solv) is the solvation free energy change upon binding: – ΔG (bind,solv) = ΔG (R:L, solv) - ΔG (R,solv) - ΔG (L,solv) (3) 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. 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 protein classes) and 560 ligands this show 22% improvement in the area under receiver operating characteristics curve, from an initial value of 0.68 using AutoDock Vina alone to a final value of 0.83 when the Vina results were refined by MD. cknowledgements
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.
Author contributions
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 eferences
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J. Chem. Inf .Model. (4), 2189-2198. omputational screening of repurposed drugs and natural products against SARS-Cov-2 main protease (M pro ) as potential COVID-19 therapies Sakshi Pilani , Puneet Singh , Nikolai Petrovsky , David A. Winkler College of Medicine and Public Health, Flinders University, Bedford Park 5046, Australia Vaxine Pty Ltd, 11 Walkley Avenue, Warradale 5046, Australia La Trobe University, Kingsbury Drive, Bundoora 3042, Australia Monash Institute of Pharmaceutical Sciences, Monash University, Parkville 3052, Australia School of Pharmacy, University of Nottingham, Nottingham NG7 2RD. UK CSIRO Data61, Pullenvale 4069, Australia upplementary information
Table S1 . Binding energies and published SARS-Cov-2 data for 84 top ranked small molecule ligands Name ChEMBL (C)) or Drugbank (D) ID ΔG
MMPBSA kcal/mol SARS-Cov-2 data 1 Bemcentinib C 3809489 -33.9 Phase 2 clinical trial, ED predicted 2’-O-methyltransferase nsp16/nsp10 complex binding
2 PC786 C 4291143 -33.0 Predicted spike glycoprotein, M pro , and ACE2 binding
3 Montelukast C 787 -32.6 Significant reduction in SARS-CoV-2 infection in elderly asthmatic patients treated with MK. Several predicted M pro binding studies e.g.
6, 7
4 Ergotamine C 442 -31.6 Several predicted M pro binding studies e.g.
5 Simeprevir D06290 -31.5 In vitro EC pro binding studies e.g. , many predicted Mpro binding studies e.g. , predicted RdRp binding
15, 16
6 Sofosbuvir D08934 -31.0 In vitro EC values of 6.2 and 9.5 μM. Predicted RdRp
18, 19 binding 7 Lopinavir D01601 -30.7 In vitro EC Multiple single agent and combination human trials e.g.
21, 22 . In vitro EC Predicted M pro binding
8 Ritonavir D00503 -30.2 In vitro EC Multiple single agent and combination human trials e.g.
21, 27
Predicted M pro helicase and RdRp binding
9 Mergocriptine C2105887 -30.1 Predicted 2′-O-ribose methyltransferase binding 10 Remdesivir D14761 -29.9 Multiple human trials e.g.
30, 31 , in vitro EC predicted M pro and RdRp binding 11 Metergotamine C2106428 -29.1 Predicted 2′-O-ribose methyltransferase binding Name ChEMBL (C)) or Drugbank (D) ID ΔG MMPBSA kcal/mol SARS-Cov-2 data 12 Galicaftor D14894 -28.4 Predicted M pro binding
13 Eltrombopag C461101 -28.2 Predicted spike and RdRp binding 14 Saquinavir C114 -27.9 In vitro EC predicted ACE2, M pro , and RdRp binding
15 Rolitetracycline C 1237046 -27.6 Predicted M pro and spike binding 16 Disogluside C395414 -27.3 Predicted M pro binding 17 Zafirlukast D00549 -26.7 Predicted spike, M pro , and 2′-O-ribose methyltransferase binding 18 Diosmin D08995 -25.8 Predicted M pro
42, 43 binding 19 AZD-5991 D14792 -25.2 … 20 Ruzasvir C 3833385 -25.1 Predicted M pro and RdRp binding 21 Rebastinib C1738757 -24.3 Predicted 2'-O-ribose methyltransferase nsp16 binding
22 RSV-604 D15197 -24.3 … 23 Eravacycline D12329 -24.2 Predicted M pro
45, 46 binding 24 Lifitegrast C2048028 -24.1 Predicted nsp16/nsp10 complex and RdRp binding 25 10-Deoxymethynolide D07703 -24.1 … 26 Ledipasvir D09027 -23.8 Predicted M pro binding
27 Deldeprevir C3040582 -23.8 Predicted M pro binding
28 Rifamycin D11753 -23.8 … 29 Ethoxazorutoside C 2106047 -23.8 … 30 Dihydroergocristine C601773 -23.8 Predicted M pro binding 31 Gedatolisib C592445 -23.8 … 32 Lorecivivint D14883 -23.8 Predicted M pro and spike binding 33 MK-6325 C4297304 -23.6 … 34 Laniquidar C539378 -23.1 Predicted RdRp and spike binding 35 Tirabrutinib C 4071161 -22.9 EC >10µM in SARS-CoV-2-Nluc neutralization assay
36 3-(2-aminoquinazolin-6-yl)-4-methyl-N-[3- D06925 -22.9 … Name ChEMBL (C)) or Drugbank (D) ID ΔG
MMPBSA kcal/mol SARS-Cov-2 data (trifluoromethyl)phenyl]benzamide 37 Ensartinib D14860 -22.8 … 38 Anacetrapib C1800807 -22.4 … 39 Pazinaclone C2107504 -22.3 Predicted M pro binding
40 BMS-986142 D15291 -22.2 … 41 Phthalocyanine D12983 -22.2 Predicted nsp1, M pro , spike, nsp1, and 2′-O-methyltransferase binding 42 Umbralisib C3948730 -21.9 … 43 DNK333 C105060 -21.9 … 44 Midostaurin C608533 -21.7 Predicted M pro binding.
45 Umifenovir D13609 -21.6 Multiple clinical trials only show higher –ve rate of PCR on day 14 in adult COVID‐19 patients. Shorten the viral shedding interval. Predicted M pro binding.
46 Lumacaftor D09280 -21.5 Predicted M pro
47, 48, 56 and RdRp binding 47 TU-100 D12467 -21.3 … 48 Triamcinolone furetonide C2105791 -21.2 … 49 Zoliflodacin C3544978 -21.2 Predicted M pro and PL pro48 binding 50 KPT-9274 C4297467 -20.9 … 51 Atazanavir D01072 -20.8 Inhibits SARS-CoV-2 replication, predicted M pro and helicase binding 52 Mitratapide C2104975 -20.8 Predicted 2'-O-ribose methyl-transferase nsp16 binding
53 Tarloxotinib D14944 -20.5 … 54 Spergualin C1765508 -20.5 … 55 Moxidectin D11431 -20.5 … 56 PRI-724 D15034 -20.0 … 57 2-[3-(methyl[1-(2-naphthoyl)piperidin-4-yl] amino}carbonyl)-2-naphthyl]-1-(1-naphthyl)-2-oxoethyl phosphonic D04016 -19.8 … Name ChEMBL (C)) or Drugbank (D) ID ΔG
MMPBSA kcal/mol SARS-Cov-2 data acid 58 ASP-4058 D11819 -19.8 … 59 Beclabuvir DB12225 -19.7 Predicted RdRp and M pro binding 60 Ubrogepant C2364638 -19.5 Predicted to disrupt spike-ACE2 interaction
61 Dihydroergotamine D00320 -19.5 Predicted M pro
8, 9 and 2′-O-ribose methyltransferase binding 62 Lifirafenib C 4209157 -19.3 Predicted spike and nsp10– nsp16 complex binding 63 Golvatinib D11977 -18.8 Predicted M pro and RdRp binding 64 Tirilazad D13050 -18.6 Predicted M pro
25, 61 and nsp1 binding 65 4-[(10s,14s,18s)-18-(2-amino-2-oxoethyl)-14-(1-naphthylmethyl)-8,17,20-trioxo-7,16,19-triaza spiro[5.14]icos-11-en-10-yl]benzylphosphonic acid D03276 -18.6 … 66 Etamocycline C3989417 -16.1 67 Quarfloxin C3989407 -15.9 Predicted spike, PL pro48 and M pro
68 2'-(4-dimethylamino-phenyl)-5-(4-methyl-1-piperazinyl)-2,5'-bi-benzimidazole D04011 -15.7 … 69 Dihydrostreptomycin C 1950576 -15.6 Predicted nsp3 and nsp10– nsp16 complex binding
70 Rimegepant C2178422 -15.6 … 71 Bezitramide C2104149 -15.4 … 72 Flutroline C57241 -15.3 Predicted 2'-O-ribose methyltransferase nsp16 binding
73 Carfilzomib D08889 -15.2 Predicted M pro binding
74 IPI-549 C3984425 -15.0 … 75 Milademetan C4292264 -14.6 Predicted M pro binding
76 Nemiralisib C2216859 -14.4 Predicted M pro binding
77 Amrubicin C1186894 -14.3 Predicted M pro binding 78 Genz-10850 D04289 -13.1 Predicted nsp12 binding Name ChEMBL (C)) or Drugbank (D) ID ΔG
MMPBSA kcal/mol SARS-Cov-2 data 79 Penimepicycline C 3833378 -12.9 Predicted to M pro and spike binding 80 Tipifarnib C289228 -12.6 … 81 MK3207 C1910936 -12.1 Predicted M pro , PLpro, binding 82 Naldemedine C2105791 -12.1 Predicted M pro and spike RBD binding 83 Tariquidar D06240 -12.0 … 84 Netupitant C206253 -11.9 Predicted RdRp binding and M pro able S2 . Binding interactions with M pro binding site for top 10 ranked drugs. ID Drug Interacting Residues H-Bond CHEMBL7835 Bemcitinib THR26, LEU27, HIS41, ASN142, GLY143, SER144, CYS145, HIS164, MET165, GLU166, LEU167, PRO168, VAL186, ASP187, ARG188, GLN189, THR190, ALA191, GLN192 VAL186 (O-N8) 2.70 ARG188(O-N8) 2.65 GLN192 (NE2 -N7) 3.27 CHEMBL442 Ergotamine THR25, LEU27, HIS41, PHE140, LEU141, ASN142, GLY143, SER144, CYS145, HIS163, HIS164, MET165, GLU166, HIS172, VAL186, ASP187, ARG188, GLN189, THR190, GLN192 GLY143(N-O4) 2.68 HIS164 (O-O5) 3.29 MET165(SD-C12) 3.18 DB01601 Lopinavir THR26, HIS41, MET49, PHE140, LEU141, ASN142, CYS145, HIS163, HIS164, MET165, GLU166, HIS172, VAL186, ASP187, ARG188, GLN189, THR190, GLN192 ASN142 (OD1-O2) 2.59 CHEMBL4958 Mergocriptine HIS41, CYS44, MET49, ASN142, GLY143, SER144, CYS145, HIS163, HIS164, MET165, GLU166, LEU167, PRO168, VAL186, ASP187, ARG188, GLN189, THR190, GLN192 CYS145(SG-O3) 3.22 THR190 (O-N5) 3.01 CHEMBL4291143 PC-786 THR25, THR26, HIS41, CYS44, MET49, TYR54, PHE140, LEU141, ASN142, GLY143, SER144, CYS145, HIS163, HIS164, MET165, GLU166, HIS172, ASP187, ARG188, GLN189 GLY143(N-O4) 2.67 SER144(OG-F1) 2.59 SER144(N-O4) 2.85 CYS145 (SG-F1) 3.01 CYS145 (N-)4) 3.06 DB14761 Remdesivir HIS41, MET49, PHE140, LEU141, ASN142, GLY143, SER144, CYS145, HIS163, HIS164, MET165, GLU166, HIS172, VAL186, ASP187, ARG188, THR190, GLN192 PHE140(O-N5) 2.98 SER144(OG-N6) 3.14 HIS163(NE2-N6) 3.01 HIS164(O-O4) 2.67 D Drug Interacting Residues H-Bond DB00503 Ritonavir THR25, THR26, LEU27, HIS41, CYS44, THR45, SER46, MET47, PHE140, LEU141, ASN142, GLY143, SER144, CYS145, HIS163, HIS164, MET165, GLU166, LEU167, PRO168, HIS172, VAL186, ARG188, GLN189, THR190, GLN192 CYS145(SG-O3) 3.03 HIS164(O-O3) 2.88 DB06290 Simeprevir HIS41, CYS44, MET49, TYR54, PHE140, LEU141, ASN142, GLY143, SER144, CYS145, HIS163, HIS164, MET165, GLU166, LEU167, PRO168, THR169, GLY170, HIS172, VAL186, ARG188, GLN189, THR190, GLN192 HIS163(NE2-O4) 3.09 HIS164(O-N3) 3.18 CYS145(O-HO) 3.34 DB08934 Sofosbuvir HIS41, MET49, TYR54, PHE140, LEU141, ASN142, GLY143, SER144, CYS145, HIS163, HIS164, MET165, GLU166, LEU167, PRO168, HIS172, VAL186, ARG188, GLN189, THR190, GLN192, ALA193 SER144(OG-O9) 3.09 GLU166(N-O6) 3.28 CHEMBL4499 Montelukast THR25, THR26, LEU27, HIS41, MET49, TYR54, PHE140, LEU141, ASN142, GLY143, SER144, CYS145, HIS163, HIS164, MET165, GLU166, LEU167, PRO168, ASP187, ARG188, GLN189, THR190, GLN192 SER144 (N-O3) 2.95 SER144 (OG-O3) 2.89 CYS145 (N-O3) 3.17 cripts : receptor = 6Y2F.pdbqt center_x= 9.245 center_y= -0.788 center_z = 18.371 size_x = 50 size_y = 50 size_z = 50 num_modes = 10 exhaustiveness = 50 eferences
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