key: cord-256307-2b1vlda8
authors: Bhardwaj, Vijay Kumar; Singh, Rahul; Das, Pralay; Purohit, Rituraj
title: Evaluation of acridinedione analogs as potential SARS-CoV-2 main protease inhibitors and their comparison with repurposed anti-viral drugs
date: 2020-11-12
journal: Comput Biol Med
DOI: 10.1016/j.compbiomed.2020.104117
sha: 
doc_id: 256307
cord_uid: 2b1vlda8

BACKGROUND: The main protease (Mpro) of SARS-CoV-2 is involved in the processing of vital polypeptides required for viral genome replication and transcription and is one of the best-characterized targets to inhibit the progression of SARS-CoV-2 in infected individuals. METHODS: We screened a set of novel classes of acridinediones molecules to efficiently bind and inhibit the activity of the SARS-CoV-2 by targeting the Mpro. The repurposed FDA-approved antivirals were taken as standard molecules for this study. Long term (1.1 μs) MD simulations were performed to analyze the conformational space of the binding pocket of Mpro bound to the selected molecules. RESULTS: The molecules DSPD-2 and DSPD-6 showed more favorable MM-PBSA interaction energies and were seated deep inside the binding pocket of Mpro than the topmost antiviral drug (Saquinavir). Moreover, DSPD-5 also exhibited comparable binding energy to Saquinavir. The analysis of per residue contribution energy and SASA studies indicated that the molecules showed efficient binding by targeting the S1 subsite of the Mpro binding pocket. CONCLUSION: The DSPD-2, DSPD-6, and DSPD-5 could be developed as potential inhibitors of SARS-CoV-2. Moreover, we suggest that targeting molecules to bind effectively to the S1 subsite could potentially increase the binding of molecules to the SARS-CoV-2 Mpro.

A novel coronavirus (CoV) caused a cluster of pneumonia cases during the end of December 2019 in Wuhan city of China. The novel CoV was subsequently called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the infection caused by it was named COVID-19 by the World Health Organization [1] . The SARS-CoV-2 is a single-stranded positive-sense RNA virus containing at least six open reading frames [2, 3] , which belongs to the Coronaviridae family of viruses. The first open reading frame of the viral genome of SARS-CoV-2 encodes two polyproteins (replicase 1a and replicase 1ab), which are responsible for the mediation of vital processes like replication and transcription [4, 5] . The functional polypeptides required for replication and transcription are the end products of extensive proteolytic activity on these polyproteins. The central enzyme necessary to achieve cleavage activity is the main protease (Mpro) [6] . The Mpro, through its proteolytic activity, cleaves the polyproteins to produce nonstructural polypeptides. These polypeptides are required for the production of four essential structural proteins (Envelope, Spike, Membrane, and nucleocapsid proteins), and other subsidiary proteins [7, 8] . The Mpro inhibition would primarily halt the multiplication of the virus inside the host body by blocking crucial processes such as genome replication and transcription.

The Mpro of CoVs is one of the most studied and extensively explored targets for the development of specific inhibitors. The Mpro is also known as 3-Chymotrypsin-like protease (3CLpro). The Mpro exists in a catalytic homodimeric (protomer A and B) form. There are three domains in each protomer. The domain I (chymotrypsin) and II (picornavirus 3 C protease-like) are the catalytic domains containing a catalytic dyad formed by residues Cys145 and His41. The domains I and II consist of six-stranded anti-parallel β-barrel structures, while the domain III is made up of five α-helices forming an anti-parallel globular cluster [9] . The domain III regulates the assembly of the physiologically active dimeric form of Mpro by allowing salt bridge interactions between Glu290 and Arg4 of each protomer. The substrate-binding site is present in a crevice between domains I and II. The binding pocket consists of four subsites (S1, S1', S2, and S4) formed by the neighboring amino-acids [10] . The absence of Mpro homologs in human proteases qualifies it to be a suitable drug target with negligible cytotoxicity [11, 12] .

Drug re-purposing is a non-specific and short term approach to identify potential treatment strategies to cure COVID-19 patients [13] . Many studies have suggested the use of anti-HIV and anti-HCV drugs for the treatment of COVID-19 [13] [14] [15] [16] . However, no specific drug is yet approved J o u r n a l P r e -p r o o f for treating COVID-19 patients. The urgent demand for the development of efficient and potent drugs against SARS-CoV-2 necessities the use of in-silico tools for the rapid screening of potential lead molecules. Many computational studies have suggested potential inhibitors of different origins against different target proteins of SARS-CoV-2 [17] [18] [19] [20] .

A wide range of natural products has allowed enormous resources of information for basic sciences and drug discovery for pharmaceutical industries [21, 22] . A systemic approach led by the experience and inspirations of many researchers have played a decisive role in successful target identification, and a rapid progression in genomics and proteomics have made this grueling task possible [23] [24] [25] . Computational strategies could be employed to screen potential targets in a stipulated time. The most probable target predicted by in-silico approaches could easily be tested in in-vitro or in-vivo conditions [26] . In this study, we screened a set of DSPD molecules along with repurposed FDA approved anti-HIV (Atazanavir, Indinavir, and Saquinavir), and anti-HCV (Ciluprevir and Glecaprevir) to target the SARS-CoV-2 Mpro. The selected DSPD molecules (DSPD-1 to 6) were compared on different computational parameters (Docking energy, RMSD, protein-ligand interactions, MM-PBSA binding energy, Contribution energy, and SASA) to repurposed FDA approved antiviral drugs. The central objective of the computational approach was to identify and compare the potential of DSPD molecules with repurposed antiviral drugs to inhibit the SARS-CoV-2 Mpro.

The x-ray crystallographic structure having 1.95 Å resolution of the Mpro of SARS-CoV-2 (PDB ID: 6Y2F) was fetched from the Protein Data Bank [14] , and a cluster of FDA approved anti-HIV and anti-HCV drugs, and previously synthesized DSPD molecules were selected for the study. The protein structure was subjected to initial preparation by the "prepare protein" Wizard of the Discovery studio package [27] . The FDA approved anti-HIV drug molecules Atazanavir, Saquinavir, Indinavir, and anti-HCV Ciluprevir, Glecaprevir, were recovered from PubChem. The Gaussian16 optimized ligand geometry of every molecule was minimized by DFT protocols [28] .

The molecules proposed under this study as ligand DSPD-1 to 6 have been published in our earlier report [29] . Novel classes of acridinediones (DSPD-1-6) have been synthesized under this study following microwave assisted solvent free silica mediated green approaches from ethyl-3-(2,4dioxocyclohexyl) propanoate [30] and different amines through in situ formation of β-enaminoester.

J o u r n a l P r e -p r o o f 5 The diester functionality in ligand DSPD-1-6 is rare and gives a vast opportunity to outspread binding capacity and may be responsible for its best performance. The 2D representations of the selected molecules for MD simulations are shown in Figure 1 . Further to enhance the solubility of ligand DSPD-1 to 6 in hydrophilic or polar solvents, its acid salts also have been proposed under this study. 

The Discovery studio was used to access the drug likeliness properties and potential toxicity of novel molecules. The inbuilt published tools (ADMET and Toxicity Prediction by Komputer Assisted Technology (TOPKAT) module) and models such as the CYP2D6 Prediction, Hepatotoxic Prediction, PPB Prediction, Solubility Level, Absorption Level, 2D Polar Surface Area, AlogP98, Rat Female NTP Prediction, Rat Male NTP Prediction, Carcinogenic Potency TD50 Rat, Rat Oral LD50, Ames Prediction, DTP Prediction, Skin Irritant, and Skin Sensitization in the discovery J o u r n a l P r e -p r o o f studio package were used to calculate and analyze the pharmacokinetic profiles of DSPD molecules along with the selected FDA approved drugs [31, 32] .

The CDOCKER docking was performed in Discovery Studio to evaluate the binding poses of the selected ligands within the active site of SARS-CoV-2 Mpro. A CHARMm (Chemistry at Harvard Macromolecular Mechanics energy) [33] based docking mechanism is employed to execute the CDOCKER docking protocol. The receptor site is maintained to be rigid, while the ligand was 

Molecular dynamics (MD) simulations were carried out using the GROMACS 5.0.6 [34] [35] [36] . For MD simulations, the selected docked complexes were selected as the starting structures. The topology of the ligands were generated by using the GlycoBioChem PRODRG2 server [37] . The "pdb2gmx" of GROMACS was used to generate topologies for the Mpro protein. The GROMOS96 43a1 force field with spc water model were used for simulations. To the complexes at this stage, the addition of H-bonds was also done. The number of water molecules added to the simulation box were 30086, 30081, 30075, 30081, 30076, and 30074 for complexes with molecules DSPD-1 to DSPD-6, respectively. Similarly, for complexes with Saquinavir, Atazanavir, Indinavir, Ciluprevir, and Glecaprevir, the number of water molecules added were 30075, 30076, 30080, 30074, and 30076, respectively. For maintaining the electro-neutrality of the system, Na + ions were added to the simulation system using the "gmx genion" script. To complexes with DSPD-1 to DSPD-6, Atazanavir, Indinavir, and Glecaprevir, the number of Na + ions added were two. One Na + ion was J o u r n a l P r e -p r o o f added to the complex with Saquinavir, while three Na + ions were added to complex with Ciluprevir.

Energy minimization of the selected complexes was performed by the use of the steepest descent minimization algorithm. The MD equilibrations were further carried out in two steps: (i) Integration time of 2 fs at 300K temperature with 500 ps under NVT was the first step. (ii) In the second step, to equilibrate the size of the system, 1 ns NPT equilibration was employed at pressure 1 bar. As the backbone C-α atoms were restained but the solvent molecules were allowed to move freely in both the steps. During equilibration, the previous step ensured the adequate solvent equilibrium in the system. The equilibrated systems were then subjected to the production phase without any restrains for a period of 100 ns. To maintain the temperature at 300 K the V-rescale temperature coupling algorithm [38] was used, and the Parrinello-Rehman pressure coupling method [39] was used to maintain the pressure of the system at 1 atm. Applying the LINCS algorithm [40] , all the bonds were constrained, and on the other hand, a 10 Å cut-off was set for the non-bonded interactions. To calculate the long-range electrostatic interactions, the Particle-Mesh Ewald (PME) algorithm [41] was utilized. At every 5 ps, the energies were noted after setting a 2 fs time step. By the use of the GRID method, the neighbor list was updated after every 10 steps. The analysis was done by using various GROMACS in-built scripts such as "gmx rms" of the produced trajectories for calculating the root mean square deviations (RMSD) of backbone C-α atoms, the "gmx sas" for calculation of solvent accessible surface area (SASA). Furthermore, the "g_mmpbsa" script of GROMACS was utilized to perform the MM-PBSA studies [42] . MM-PBSA is one of the most popular endpoint insilico methods to calculate binding free energy. MM-PBSA is acknowledged for its effectiveness and exactness. The binding free energy computation presents a quantitative evaluation of proteinligand interactions that accommodate to apprehend its stability. In this method, the binding energy was calculated using the following equations:

Where, ΔG binding = the total binding energy of the complex, G receptor =the binding energy of the free receptor, and G ligand = unbounded ligand.

A data-set of 19 molecules (FDA-approved drugs and selected molecules) was docked against SARS-CoV-2 Mpro to obtain the molecules with the best interaction energy (Table S1 ). The ligand binding poses were sorted according to their -CDOCKER energy; therefore, the conformers J o u r n a l P r e -p r o o f possessing the lowest energy score (more negative) were examined for further computations. The -CDOCKER energy of molecules with the best interactions were listed in Table 1 . 

The docked poses possessing minimum energies (more negative) conformers of the five FDA approved drugs (Atazanavir, Saquinavir, Indinavir, Ciluprevir, and Glecaprevir) were analyzed, as shown in Figure Table S2 . Table S3 .

J o u r n a l P r e -p r o o f 

The ADMET properties of the DSPD molecules along with the FDA approved drugs were calculated, as summarized in J o u r n a l P r e -p r o o f Two of the ADMET descriptors (AlogP98 and 2D polar surface area) were used to develop a 2D plot (Figure 4 ). This plot was used to analyze the performance of molecules in intestinal absorption and penetration across blood brain barrier (BBB). The model included four eclipses of 99% and 95% confidence levels of BBB penetration and intestinal absorption. The results showed that DSPD-6 and Indinavir were inside eclipse of 95% confidence limit, and DSPD-4 was inside eclipse of 99% confidence limit. All other molecules were outside the eclipse of 99% confidence limit showing that these molecules were not adequate for intestinal absorption. However, the intestinal absorption could be increased by acid salts of the molecules without affecting their biological activity. The acid salts of these molecules showed good intestinal absorption ( Figure S1 ). Sensitization. The TOPKAT descriptors were summarized in Table 3 . All the molecules, along with the FDA approved drugs were non-carcinogenic in rat female/male NTP predictions, non-mutagenic in Ames predictions, and non-toxic in DTP predictions. DSPD-5, DSPD-3, Saquinavir, and Ciluprevir showed no skin irritancy, while the rest of the molecules were predicted to be mildly irritant. Except for Atazanavir and Indinavir, no molecules were predicted to show skin sensitization. We also compared the trajectories of the binding energy of the Mpro complex with Saquinavir and three topmost DSPD molecules with respect to time, as shown in Figure 8 . The binding energies with respect to the time of all the complexes are provided in Figure S4 .

J o u r n a l P r e -p r o o f 

We calculated the solvent-accessible surface area (SASA) for the four subsites of the binding pocket of Mpro in complex with Saquinavir, DSPD-2, DSPD-5, and DSPD-6, as shown in Figure   10 . In the S1 subsite, the repurposed FDA-approved drug showed an average SASA value around ~5.14nm 2 , while the selected DSPD molecules 2, 5, and 6 showed ~4.21nm 2 , ~4.13nm 2 , and ~4.52nm 2 respectively. The SASA of S1 subsite has decreased for the complexes having DSPD-2, DSPD-5, and DSPD-6 than for the complex having Saquinavir. Similarly, the SASA of S1' subsites has also reduced for the complexes having DSPD molecules when compared to complexes having Saquinavir. The SASA of subsite S2 remained unchanged for all the selected complexes. The SASA of S4 subsite was lowest for DSPD-3 (~1.07 nm 2 ), while the average SASA values were reported to be ~1.87 nm 2 , ~2.18 nm 2 , and ~2.31 nm 2 for complexes having Saquinavir, DSPD 5, and 6

respectively. Saquinavir (black), DSPD-2 (red), DSPD-5 (green), and DSPD-6 (blue).

In this study, we presented a computational approach to identify potential molecules that could inhibit the Mpro of SARS-COV2. The novel in-house synthesized acridinediones molecules, along with the FDA approved repurposed antiviral drugs, were screened for their potential to inhibit the Mpro of SARS-CoV-2. A number of recent studies have suggested potential anti-SARS-CoV-2 molecules based on computational results [17, [43] [44] [45] .

The structures of the DSPD molecules, along with the repurposed antiviral drugs, were subjected to energy minimization, followed by molecular docking with the Mpro of SARS-CoV-2.

Molecular docking generates and ranks the receptor-ligand poses according to their interaction energies [46, 47] . In all the docked complexes, the conserved residues GLU166, ASN142, CYS145, HIS41, HIS163, and GLN192, played a significant role in binding with the ligands as analyzed from the binding interaction analysis (Figure 2, 3) . Comparing the CDOCKER energy of the selected molecules with FDA approved drugs (Table 1) In order to predict the drug likeliness of the DSPD molecules, we calculated the ADMET properties of these molecules along with the FDA approved drugs, as summarized in Table 2 . The molecules were compared on six ADMET descriptors, such as cytochrome P450 2D6 (CYP2D6), hepatotoxicity, plasma protein binding (PPB), solubility, absorption, and AlogP98. The CYP2D6, hepatotoxicity, and PPB parameters are presented in two forms (True: inhibitor/toxic, or False: noninhibitor/non-toxic) to evaluate their toxicity or inhibitory actions [26] . Figure 4 ). To overcome this limitation, the acid salts of the DSPD molecules could be used, which showed higher absorption, as shown in Figure S1 .

Further, the drug likeliness of the molecules was accessed by the TOPKAT tool [32, 48] . TOPKAT provides both quantitative as well as qualitative predictions. The quantitative structure-activity relationship (QSTR) models are employed by the TOPKAT tool to quantitatively evaluate the toxicity. The QSTR models are then used to calculate the probability values and evaluate toxicity by optimal predictive space (OPS) [49] . The toxicity decreases with an increase in quantitative Carcinogenic potency TD50 Rat, Rat oral LD50 values, making the molecules more potent by increasing their safety index [26, 31, 49] . All DSPD molecules showed favorable TOPKAT results, as shown in Table 3 .

The crystallographic structures are static poses of three-dimensional structures of proteins.

MD simulations are employed to bring these static poses alive, providing insights into the actual protein motions in biological conditions [50] [51] [52] [53] . The investigation of protein structure and folding simulations are dependent on various computational tools that measure structural similarity. The RMSD is a well-accepted estimator of the structural similarity of optimally aligned proteins [54] .

The RMSD trajectories of all the protein-ligand complexes converged below 0.5 nm ( Figure 5 ). The subtle differences in the values of RMSD and the converged trajectories revealed that the simulation complexes were conformationally stable and equipotential to actual crystallographic protein structures. The conformations obtained from MD simulations were structurally stable and ideal for further computational analysis.

The protein-ligand trajectories of the well-equilateral structures were extracted at different time intervals to visualize the interaction throughout the simulation period. The DSPD molecules were seated deep inside the binding pocket throughout the simulation time. However, Atazanavir and Glecaprevir were present on the edge of the binding pocket ( Figure 6 ). These findings were additionally confirmed by determining the thermodynamic free energy calculations by the MM-PBSA method [42] . In the process of bond formation or protein and ligand association, energy is released and is described in the form of binding energy [55] . A negative correlation is observed between binding energy and the affinity between protein and ligand. The binding of a protein and ligand is inversely proportional to the total binding energy. The constituents of the total binding energy are polar solvation, van der Wall, SASA, and electrostatic energy [42] .

Our MM-PBSA calculations showed that the FDA approved drug Saquinavir showed the most favorable energy among other selected antiviral drugs with the active site of Mpro. However, two of the in-house synthesized molecules (DSPD-2 and DSPD-6) showed better interaction energies than Saquinavir (Figure 7, 8) . Moreover, the molecule DSPD-5 showed comparable energy with Saquinavir. Further, to get an in-depth insight into the contribution of each residue to the protein-ligand binding, we decomposed the binding free energy into per residue contribution energy [42, 56] .

The binding pocket of Mpro is formed by four subsites (S1, S1', S2, and S4) [10] . The S1

subsite residues (Phe140, Asn142, His163, and His172) of Mpro in complex with the selected DSPD molecules showed more favorable contribution energy than Saquinavir. In the S1 subsite, residues (Thr25 and Tyr54) of Mpro in complex with Saquinavir showed most high differences in favorable contribution energy than residues in complex with the selected DSPD molecules. Two out of three residues of the S2 pocket showed higher contribution energy in Mpro complexes having DSPD molecules than in complex with Saquinavir. However, both the residues of the S4 pocket (Phe185 and Gln192) contributed more favorably to the binding in complex with Saquinavir ( Figure   9 ). The results of per residue contribution energy showed that DSPD molecules could be developed as potential inhibitors for SARS-CoV-2 Mpro. The DSPD molecules interacted most favorably with the S1 subsite of Mpro binding pocket, which led to decreased binding energy (more favorable interaction). Hence, we can conclude that the molecules targeting the S1 subsite can inhibit Mpro more efficiently than ligands binding to other subsites.

In order to understand protein stability and drug design, the SASA is an important tool that quantifies the docking of ligands to proteins and packaging of residues [57] . SASA locates the exposed residues and interior seeking in a protein [27] . It is the amount of surface area available for a solvent in a biomolecule. Our analysis ( Figure 10 ) showed reduced SASA for subsites S1, S1', and S4 in complexes having selected DSPD molecules. These results showed that the ligand occupied most of the available area inside the subsites. These results also supported our ligandprotein interaction analysis, where our selected molecules were seated deep inside the binding pocket and explained the reason for increased MM-PBSA interaction energies of residues belonging to these subsites. anti-viral drugs. The selected DSPD molecules 2 and 6 showed better interaction energy with the binding site of SARS-CoV-2 Mpro than the topmost anti-viral drug Saquinavir. These findings confirmed that the chosen molecules possessed more significant potential than the anti-viral drugs in repressing the activity of SARS-CoV-2 Mpro. Moreover, the increased binding efficiency of DSPD molecules could be attributed to higher binding with the residues of the S1 subsite of the binding pocket. The binding potential of molecules to the binding site of SARS-CoV-2 Mpro could be increased by targeting the molecules to interact more efficiently with the residues S1 subsite of the pocket. To conclude, the molecules DSPD-2 and DSPD-6 could be developed as anti-SARS-CoV-2 agents to fight against the notorious virus.

The PR of China, under its obligations for International Health Regulations

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Structure of Mpro from COVID-19 virus and discovery of its inhibitors

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LINCS: A Linear Constraint Solver for molecular simulations

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g_mmpbsa -A GROMACS Tool for High-Throughput MM-PBSA Calculations

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Highlights • Different computational parameters (Docking energy, RMSD, protein-ligand interactions, MM-PBSA binding energy, Contribution energy, and SASA) were applied to identify potential lead for COVID19

• Repurposed FDA approved antiviral drugs were compared with a set of acridinedione analogs against main protease (Mpro) of SARS-CoV-2

• The acridinedione analogs have acceptable ADMET values and low toxicity profile. • In-house synthesized acridinedione analogs were showed good amount of interaction with Mpro of SARS-CoV-2

The authors declare no competing interests.

J o u r n a l P r e -p r o o f CONFLICT OF INTEREST The authors declare that they have no conflict of interest.