key: cord-280819-z6ucnwk0
authors: Achilonu, Ikechukwu; Iwuchukwu, Emmanuel Amarachi; Achilonu, Okechinyere Juliet; Fernandes, Manuel Antonio; Sayed, Yasien
title: Targeting the SARS-CoV-2 main protease using FDA-approved Isavuconazonium, a P2-P3 α-ketoamide derivative and Pentagastrin: an in-silico drug discovery approach
date: 2020-09-02
journal: J Mol Graph Model
DOI: 10.1016/j.jmgm.2020.107730
sha: 
doc_id: 280819
cord_uid: z6ucnwk0

The SARS-CoV-2 main protease (M(pro)) is an attractive target towards discovery of drugs to treat COVID-19 because of its key role in virus replication. The atomic structure of M(pro) in complex with an α-ketoamide inhibitor (Lig13b) is available (PDB ID:6Y2G). Using 6Y2G and the prior knowledge that protease inhibitors could eradicate COVID-19, we designed a computational study aimed at identifying FDA-approved drugs that could interact with M(pro). We searched the DrugBank and PubChem for analogs and built a virtual library containing ∼33000 conformers. Using high-throughput virtual screening and ligand docking, we identified Isavuconazonium, a ketoamide inhibitor (α-KI) and Pentagastrin as the top three molecules (Lig13b as the benchmark) based on docking energy. The ΔG(bind) of Lig13b, Isavuconazonium, α-KI, Pentagastrin was -28.1, -45.7, -44.7, -34.8 kcal/mol, respectively. Molecular dynamics simulation revealed that these ligands are stable within the M(pro) active site. Binding of these ligands is driven by a variety of non-bonded interaction, including polar bonds, H-bonds, van der Waals and salt bridges. The overall conformational dynamics of the complexed-M(pro) was slightly altered relative to apo-M(pro). This study demonstrates that three distinct classes molecules, Isavuconazonium (triazole), α-KI (ketoamide) and Pentagastrin (peptide) could serve as potential drugs to treat patients with COVID-19.

With over 4.53 million infections and 307108 deaths today (15 th May 2020), the world is witnessing a calamitous viral pandemic caused by a new strain of a coronavirus, scientifically referred to as SARS-Cov2, the causative agent of corona virus disease .

Retroviral SARS-CoV-2 shares ~82% genome similarity to the SARS coronavirus [1, 2] . The viral infection is believed to have originated in China with the initial epicentre in Wuhan, a city in the Hubei Province of China [3, 4] . The actual date of origin of the infection and first patient is still unknown; however, it is believed that this virus may have crossed from wild animals to humans, a possible zoonotic virus that is originally from bats, akin to the African Ebola and the Lassa fever viruses [5] . Over the course of time the epicentre, the epicentre has moved from China to Italy and currently the epicentre in the USA. At the time of writing this article there is no known vaccine or treatment option available. Even though the death rate is lower than the historical coronavirus-associated SARS, the recovery rate seems to be relatively protracted. This has resulted in straining health care systems globally and sub-Saharan Africa may become the next epicentre if the infection is not controlled effectively. In most countries around the world, the only means of control is by instating a nationwide lockdown. Countries, such as South Africa, that took this initiative, however disastrous it was on the economy, have witnessed a slower infection rate, from 45% to less than 4% (between 27 th March 2020 and 10 th of April 2020).

As viral disease outbreaks are not often foreseen, COVID-19 is a global emergency and there is a race against time to produce either a vaccine and/or effective drugs to curb the global plague of COVID-19 [6] . Several drug treatments have been proposed worldwide.

Chloroquine/hydroxychloroquine, an FDA-approved drug that was used to treat malaria has been proposed, in combination with zinc, to be effective in eradicating SARS-CoV-2 in patients [7] [8] [9] [10] [11] [12] . This is because chloroquine is an endocytosis blocker and acts as an ionophore that facilitates the entry of Zn 2+ into cells such as T-lymphocytes and Zn 2+ is known to inhibit coronavirus and arterivirus RNA polymerase [13] . Other antiviral agents including remdesivir, lopinavir, ritonavir and interferon α, have also been proposed as possible treatment alternatives against COVID-19 [14, 15] . Currently, several countries have begun chloroquine and antiretroviral treatment trials; however, some of the clinical information emanating from these trials is still anecdotal and cannot be justified as the treatment of choice [16] . As the world continues to grapple with the outbreak of the SARS-Cov-2 virus, the most logical approach to treating this infection is by accelerated rational J o u r n a l P r e -p r o o f drug discovery [17] using a combination of computational modelling and empirical studies [18, 19] , such as in autoimmune disease drug discovery [20] . However, this is only possible if there are empirically determined crystal structures of key druggable targets in the virus. In this instance, M pro , a viral protease, represents a prime target because it is critical for processing viral polyproteins and viral maturation inside infected host cells [21] . Of concern are unconfirmed reports in Asia of people who have recently recovered from COVID-19 infection and have tested positive for a second time. This may point to the fact that vaccines alone may not be the best strategy for dealing with this pandemic. This, therefore, underscores the need for a rational approach to the discovery of drugs to treat the current scourge of COVID-19.

Proteases are attractive targets in a rational approach to COVID-19 drug discovery. This is because most retroviruses depend on key enzymes, such as proteases, for processing of their polyprotein precursors [22] [23] [24] . Zhang et al. [21] recently published a paper on the structure of the SARS-CoV-2 main protease (SARS-Cov-2 M pro ) with accession code 6Y2G and deposited it in the Protein Data Bank in March 2020 [21] . A notable feature of this atomic structure is that a derivative of an α-ketoamide inhibitor is bound to the enzyme and this must, therefore, serve as the seeding point that drives the rational drug design approach towards anti-SARS-CoV-2 drug discovery. This atomic structure, in addition to the other ~112 PDB-deposited structures related to COVID-19, can serve as a template to discover other potential inhibitors using computational/machine learning (artificial intelligence) studies. Currently, several databases are curating several million compounds, some of which are FDA-approved drugs as well as drugs that are at the final stages of clinical trials with published outcomes. Databases such as DrugBank [25, 26] and PubChem [27, 28] offer structural biologists the opportunity to X-ray millions of compounds that can be validated theoretically as having potential bioactivity towards SARS-CoV2 viral enzymes.

Drug repurposing, which describes the identification and development of an existing drug for a new indication [29] [30] [31] , is rationalised by computer-aided drug design. Computer-aided drug design and discovery has become progressive in recent times, primarily due to state-ofthe-art advances in algorithms that simulate, to near-reality, the structure and function (behaviour) of biomolecules, especially regarding biomolecular interactions. This also has been aided with the development of supercomputing technologies with incredible capacities to perform these calculations at an astonishing speed. Several studies have described the success of computer-aided drug repurposing towards the discovery of a new generation of J o u r n a l P r e -p r o o f anti-cancer therapeutics [30] . In the event of an outbreak of disease at epidemic or pandemic scale, drug-repurposing is an answer to accelerated ("warp-speed") discovery of drugs to mitigate the devastating effect of highly communicable disease, such as the Ebola virus outbreak and SARS-CoV-2 pandemic. Studies have also suggested that drug-repurposing will greatly enhance and enable the discovery of existing drugs that can treat the clinical stage of tSARS-CoV-2 infections [32] [33] [34] [35] [36] . A recent study by Wan et al. [32] using analysis based on decade-long structural studies of the coronavirus suggested that remdesivir could be repurposed for the treatment of the virus, targeting the Main-protease. Hoffmann et al. [37] also recently suggested that a known protease inhibitor can be repurposed to block the entry the SARS-CoV-2 virus, an event that depends on ACE2 (angiotensin-converting enzyme 2) and TMPRSS2 (transmembrane protease, serine 2) receptors. Most of these studies have suggested repurposing FDA-approved drugs; hence, empirical studies backed by computational studies may be ongoing to prove this concept.

This study typifies the contribution of Computational Biology and Chemistry in the race towards successful rational drug design discovery. Our work showcases the value of biocomputational studies and that it may be used effectively and reliably as a tool to validate the results obtained using other biochemical and biophysical techniques. In light of this, our study validated the study by Zhang et al. [21] and other studies that proposed to repurposing [38] of currently available drugs such as such as remdesivir, a drug that was initially designed, although ineffective, against the Ebola virus [18, 19] . We could have screened the entire DrugBank and other publicly available small molecule databases using high-throughput virtual screening (HTVS), but we chose to begin with two classes of protease inhibitors (i) the ketoamide inhibitors and (ii) known antiretrovirals including remdesivir, saquinavir, atazanavir using computational modelling approaches. We used HTVS, induced-fit ligand docking and molecular dynamics simulation studies to identify additional classes of plausible FDA-approved drugs as possible drug candidate to treat COVID-19. The conceptual framework of our study is illustrated in Fig. 1 . 

The α-ketoamide derivative (Lig13b) by Zhang et al. [21] and eleven FDS-approved antiretrovirals (Table S1 ) were submitted to the PubChem and DrugBank databases for analog search. Similar compounds were extracted in a structured data file (SDF) format and submitted to the LigPrep module implemented in Maestro v12 for ligand preparation, which involves energy minimisation using OPLS 2005 force field. The algorithm was set to generate possible states of the molecules at pH 7.0±2, while accurately predicting the pKa of these states at the set pH using the Epik module of the algorithm. The ligands were also desalted and possible tautomeric states (~32 tautomers/ligand) were further generated at pH 7.0±2. Additionally, specific chiral centres were retained (for molecules with multiple chiral centres), while other chiral centres were varied during the ligand preparation to return chemically sensible structures. These generated molecules were saved as a compressed Maestro file. The atomic coordinate for the SARS-CoV-2 M pro (PDB ID 6Y2G) was extracted from the RCSB-PDB database and submitted to the Protein Preparation Wizard module implemented in Maestro. The entire structure was energy-minimised by assignment of accurate protonation state at physiological pH and hydrogen atoms were added to the crystal structure using the default parameters. The stereochemistry of the side chains was checked to ensure that no major perturbations were induced while preparing the structure.

A grid file of the receptor was prepared using Maestro for the HTVS. More than 33000 molecules were prepared using the LigPrep algorithm and were submitted to the highthroughput virtual screening (HTVS) module implemented in Maestro. Three steps of the virtual screening workflow were used, beginning with the HTVS, the standard protocol (SP) J o u r n a l P r e -p r o o f and finally the extend protocol. The option for MM/GBSA was not applied at this step. The Lipinski ADME filtering was not applied, but the QikProp filtering was applied during the HTVS. The ligand docking step in the HTVS performed initial docking of the entire set of more than 33000 molecules and 10% of the HTVS-docked ligands were further subjected to SP-docking protocol. This rigorous and systematic process generated docked potential hits that were scored using Glide docking scores.

Top scoring ligands in each class of drug were extracted and re-submitted to the induced-fit docking (IFD) module implemented in the Maestro v12 algorithm, which employs a mixed molecular docking and dynamic protocol. Briefly, the standard IFD protocol was applied to the selected (centroid) amino acid side chains (19-29, 38-54, 85, 114-119, 126, 136-147, 161-175, 181, 185-193) in an implicit solvent model using the OPLS_2005 force field. H-bond and metal ion constraints were applied to both the initial and re-docking stages. Ring conformational sampling with a 2.5 kcal/mol energy barrier, as well as a non-planar conformation penalty on amide bonds was applied to the IFD protocol. The scaling for both receptor and ligand was set at 0.5 with a maximum of 20 allowable poses per ligand.

Residues within 5 Å of the docked ligand were further refined using Prime Refinement algorithm implemented in Maestro v12. Prime energy was used to rank the refined proteinligand complexes. The receptor structures within 30 kcal/mol of the minimum energy structure were submitted for a final round of Glide docking and scoring. Each ligand was redocked into every single refined low-energy receptor structure in the subsequent second docking step using the default Glide XP settings.

Molecular dynamics simulation was carried out using GPU-enabled Desmond [39] [40] [41] engine implemented in Maestro v12. The complex corresponding to the top-scoring pose for each ligand or the un-complexed (Apo) protein was saved as a PDB file and submitted to the Linux (Ubuntu) computer for the Desmond high-performance molecular dynamics simulations studies. This study has two main phases; namely, system building (solvation and ionisation) and production. The System Builder module implemented in the Desmond algorithm was used to solvate the system using the TIP3P explicit solvent model with the OPLS_2005 force field. The model was placed in an orthorhombic water box (distance from the box face to the outermost protein/ligand atom = 10 Å, box angle α = β = γ = 90°). The box J o u r n a l P r e -p r o o f volume was minimised, and counter ions added to neutralise the system, making sure the ions are placed at least 20 Å from each ligand. The system was physiologically conditioned by adding 0.15 M NaCl into the solvent box. After the solvation and ionisation phase in the explicit solvent model was completed, the system was submitted to the molecular dynamics production phase. This phase of MD simulation is divided into eight distinct stages with specified parameters. The first seven stages involve the equilibration phase and is composed of short simulation steps.

Step 8 is a final, long simulation stage. A total of 50 ns production stage was carried out. In the first stage, the type and parameters of the solvated system were detected. In stage 2, a 100 ps simulation was carried out using Brownian Dynamics under NVT conditions at 10 K, while placing restraints on solute heavy atoms. Stage 3 involved a 12 ps simulation under NVT conditions at 10 K with restraints on heavy atoms. Stages 4, 6 and 7 (the pocket solvation at stage 5 was omitted) employed short simulation steps (12, 12 and 24 ps, respectively) under NPT conditions (at 10 K and restraints on heavy atoms for stages 4 and 6). No restraints were placed on heavy atoms at stage 7. The final production stage at constant temperature (300 K) was carried out at stage 8, for 50 ns.

Binding free energy (ΔG bind ) calculations were carried out using the molecular mechanics/generalised Born solvent area (MM/GBSA) method [42] [43] [44] implemented in Amber 18 in order to gain more insight into the binding of the ligands to M pro . Briefly, the free energy of binding of Lig13b, α-KI, Pentagastrin and Isavuconazonium to M pro were calculated by averaging 2000 snapshots of the simulated complexes (from 20 ns molecular dynamics simulation). ΔG bind of ligands at the M pro active site was calculated using (1)

where ΔG RL , ΔG R , and ΔG L represent the free energies of complex, receptor, and the ligand, respectively. The free energy (G) of each state was calculated using the following equations:

The FF14SB force field terms were used to estimate the gas phase energy (E gas ), which is the sum of the internal energy (E int ); Coulomb energy (E ele ) and the van der Waals energies (E vdW ). The energy contribution from the polar states (G GB ) and non-polar states (G SA ) were employed to evaluate the solvation free energy (ΔG sol ). The solvent accessible surface area (SASA in Å 2 ) was also used to derive the non-polar solvation energy (G SA ) using a water probe radius of 1.4 Å, while the contribution from polar solvation (G GB ) was determined by solving the Generalised Born equation, where the total entropy of the solute and temperature is represented by S and T, respectively. To obtain the contribution of each residue to the total binding free energy profile between the peptidomimetics and M pro , per-residue free energy decomposition was carried out at the atomic level for imperative residues using the MM/GBSA method in Amber 18.

In order to further study the effect of ligand binding on dynamics of the Cα atoms, we 

The rationale behind performing molecular docking is to make a systematic prediction of the ideal pose or conformation of a ligand in a protein's binding site, which could be taken Table S1 and Fig. S1 ). The Table 1 .

We will focus mainly on the four peptidomimetics (indicated in Table 1 ) that were subsequently submitted for MD simulation studies. Fig. S4 shows the perresidue energy decomposition plots of the four ligands. Isavuconazonium appears to have more amino acid residues contributing towards its stability within the active site when compared to the other three ligands.

Each of the complexes between M pro and the four peptidomimetic ligands, as well as the non- (Table S2) showed that the overall total energy, potential energy, temperature and volume were stable throughout the 50 ns simulation period (average slope ± 0.002-unit/ps). All the M pro -ligand complexes showed comparable Cα RMSD in comparison with the non-liganded system (Fig. 3A) . Overall, the system reached equilibrium around 10 ns with an average Cα RMSD of 1.66 Å (± 0.14 Å) after an initial rapid increase from 0 to 2.5 ns (Fig. S5) . The system converged and remained stable beyond 10 ns. This is further affirmed by the representative 3D structures of the complexes superimposed on the apo-M pro (Fig. 3B) . The shown the inhibitor (an α-ketoamide inhibitor derivative) to be effective against the virus.

Using 6Y2G as a template, we aimed to generate theoretical extrapolations on plausible inhibitors of SARS-CoV-2 M pro capable of being introduced as experimental drugs for the treatment of COVID-19. We have utilised accessible databases, including PubChem and DrugBank, to search for analogs of 11 antiretrovirals and ketoamide inhibitors that can serve as (i) a template for rational drug design and (ii) FDA-approved drugs that can be introduced immediately to treat the disease and (iii) a proof of a concept of using any known drug to treat this viral pandemic. Molecular modelling has, in the past, proved to be a viable option in accelerating drug discovery and complements experimental studies in drug discovery [47] [48] [49] .

Our hypothesis is linked to the fact that a crystal structure of a protein provides a wealth of opportunity for a computational approach to drug design [50, 51] . In addition, a druggable target must also be critical for the biochemistry of the target pathogen-in this case, the SARS-CoV-2. We did not initially intend to specifically select protease and non-nucleoside reverse transcriptase inhibitors. We used prior conception on the fact that studies [52] seem have suggested that antiretroviral drugs (ARVs) may be capable of treating SARS-CoV-2 virus, itself being a retrovirus. Hence, we used 11 classes of PIs and NNRTI as search templates to identify candidate analogs in the molecules databases including Drug Bank and PubChem.

To cast our net as wide as possible in searching for this best-fit molecule, we extracted over 5000 molecules, generated over 33000 conformers (isomers) and critically assed more that15 initial hits using induced-fit ligand docking. We tested the stability of the top-scoring (using Lig13b as the benchmark) molecules with a 50 ns molecular dynamics simulation. We used the Glide Emodel parameter implemented in Maestro v12 to score and rank our ligands. Our HTVS protocol in the Maestro modelling algorithm is designed to rationalise and filter the J o u r n a l P r e -p r o o f number of ligands that were carried over to the second step, which is IFD followed by free binding energy calculation using MM/GBSA and MD simulation. We did not apply

Lipinski's Rule of Five [53] penalties because most of these molecules are peptidomimetics, which are often larger than 500 g/mol and may not pass the absorption parameter of this rule due to their molecular weights [54] . However, we excluded molecules with reactive functional groups and ensured that the molecules conformed with the physiological conditions in terms of the pKa at pH ~7.2. This is because ligands with reactive functional groups may form covalent bonds with M pro , therefore resulting in a false positive interaction [55] [56] [57] . The Although the coordinates of 6Y2G indicate that M pro is a homodimer with two active sites per subunit; for this study, we used a single subunit because we observed that it is only Ser 1 in subunit A that is within possible non-bonding interaction distance (3.9-4.1 Å) with the ligand in subunit B (and vice versa). There was no initial indication that Ser 1 makes any interaction with the active site, which largely depends on the nature of the ligand. From the architecture of the M pro active site, one is tempted to predict that several water molecules will be involved in binding of any ligand at this site. The structure of 6Y2G illustrates that two water molecules form either direct or indirect interactions with the Lig13b.

Our IFD studies showed that some of the known antiretrovirals and their analogs may interact with M pro based on docking scores and Emodel energies. However, Remdesivir, which is proposed as a potential COVID-19 drug, had binding energy values more than -75.9

kcal/mol, which is higher when compared with Pentagastrin, P2-P3 ketoamide derivative (α-KI), and Isavuconazonium. These drugs are FDA-approved and may be optional protease [21] . The presence of catalytic histidine residues in proteases is well-documented [58] [59] [60] [61] .

One remarkable feature of the IFD algorithm is the ability to predict a realistic interaction that is comparable with experimental models [62] [63] [64] . A comparison of the modelled interaction between M pro and Lig13b with the experimental interaction with the experimental interaction (6Y2G) shows that the RMSD between the experimental pose and the theoretical pose is 0.27 Å. Therefore, we have strong reasons to accept the results generated in this study as "close to reality" as possible and allow rational inferences to be made from this study. All the top-scoring ligands bind within 4 Å of the catalytic Cys 145, which could be a target for covalent inhibition [65, 66] of the SARS-CoV-2 M pro .

A large amount of useable and inferable information can be obtained from a MD simulation study. MD simulation gives us first-hand information on the stability of the ligand within the ligand-binding site and how the binding of these ligands impact on the conformational dynamics of the receptor, especially when compared with apo-M pro (unliganded M pro ). In order to validate our study, we used the Desmond molecular dynamics engine to generate trajectories of M pro in a complex with Lig13b, Pentagastrin, α-KI, and Isavuconazonium.

Although it is advisable to test all the ligands listed in Table 1 contact with the catalytic His 41 and Cys 145 residues. Therefore, our top candidate drugs may be a competitive inhibitor of M pro . Our results further affirm that the binding of the ligand affects the dynamics between these two residues and each ligand exerted a specific landscape of dynamics between these two residues. Cys 145 and His 41 may both be involved in the binding and stabilisation of the ligands within the M pro catalytic site [21] because these two catalytic residues are within 4 Å of the ligands in the binding pocket. This substantial piece of information may be a molecular insight into the mode of inhibition of this enzyme.

In conclusion, we have used a computational approach which includes HTVS, IFD, MM/GBSA free binding energy calculations and MD simulation to study potential drug candidates for COVID-19. We also used PCA to deconvolute the dynamics between the Cα atoms as a function of ligand binding. Zhang et al. [21] laid the foundation of this study by depositing the coordinates of the atomic structure of SARS-CoV-2 M pro (6Y2G) in complex with a derivative of an α-ketoamide inhibitor (Lig13b Table S1 . Six analogs of Lig13b [21] are Isavuconazonium, P2-P3 Ketoamide derivative (α-KI), Pentagastrin, Bromocriptine, Ceftolozane and Cobicistat (see Table 1 ).

J o u r n a l P r e -p r o o f J o u r n a l P r e -p r o o f Green bars represent a fraction of time for H-bond (categorised into backbone acceptor; backbone donor; side-chain acceptor; side-chain donor). Hydrophobic interactions are categorised into π-cation; π-π*; and other, non-specific interactions. The stacked bar charts are normalised throughout the 50 ns trajectory. Therefore, the fraction of interaction can be construed to be percentage time, which could be more than 100%. 

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Targeting the SARS-CoV-2 main protease using FDA-approved Isavuconazonium, a P2-P3 α-ketoamide derivative and Pentagastrin: an in-silico drug discovery approach Ikechukwu Achilonu 1 *

*Corresponding author email: Ikechukwu.Achilonu@wits.ac.za

I.A conceived the study, performed the parts computational studies and wrote the manuscript. A.E.I performed MM/GBSA and per-residue energy decomposition analysis, O.J.A performed and interpreted the PCA, DCCM and other statistical analysis using the R statistical environment. Y.S. co-conceived the study. M.F assisted in setting up the Desmond molecular dynamics simulation engine and analysed the results. All authors reviewed the manuscript.

The authors declare no competing interests.

Supplementary information accompanying this article can be found at https://... • This article describes the application of computational modelling towards the identification of potential SARS-CoV-2 main protease FDA-approved inhibitors that may potentially be employed as an experimental drug for the treatment of COVID-19.• We hereby state that we have had no prior discussions or consultations with any board member of your journal regarding this publication.• The authors wish to declare that we have no competing financial and/or non-financial interests in relation to the work described in this manuscript as stated by Journal of Molecular Graphics and Modelling policy on competing for interest.