key: cord-331039-qgom2e3n
authors: Kavitha, Kuppuswamy; Sivakumar, Subramaniam; Ramesh, Balasubramanian
title: 1,2,4 triazolo[1,5-a] pyrimidin-7-ones as novel SARS-CoV-2 Main protease inhibitors: In silico screening and molecular dynamics simulation of potential COVID-19 drug candidates
date: 2020-09-22
journal: Biophys Chem
DOI: 10.1016/j.bpc.2020.106478
sha: 
doc_id: 331039
cord_uid: qgom2e3n

Discovery of a potent SARS-CoV-2 main protease (M(pro)) inhibitor is the need of the hour to combat COVID-19. A total of 1000 protease-inhibitor-like compounds available in the ZINC database were screened by molecular docking with SARS-CoV-2 M(pro) and the top 2 lead compounds based on binding affinity were found to be 1,2,4 triazolo[1,5-a] pyrimidin-7-one compounds. We report these two compounds (ZINC000621278586 and ZINC000621285995) as potent SARS-CoV-2 M(pro) inhibitors with high affinity (<−9 kCal/mol) and less toxicity than Lopinavir and Nelfinavir positive controls. Both the lead compounds effectively interacted with the crucial active site amino acid residues His41, Cys145 and Glu166. The lead compounds satisfied all of the druglikeness rules and devoid of toxicity or mutagenicity. Molecular dynamics simulations showed that both lead 1 and lead 2 formed stable complexes with SARS-CoV-2 M(pro) as evidenced by the highly stable root mean square deviation (<0.23 nm), root mean square fluctuations (0.12 nm) and radius of gyration (2.2 nm) values. Molecular mechanics Poisson-Boltzmann surface area calculation revealed thermodynamically stable binding energies of −129.266 ± 2.428 kJ/mol and − 116.478 ± 3.502 kJ/mol for lead1 and lead2 with SARS-CoV-2 M(pro), respectively.

Affairs, the world economy could contract by 0.9 percent in 2020 as opposed to a previous forecast of 2.5 percent growth [1] .

The SARS-CoV2 is comprised of a positive-strand RNA genome of size 29.7 kb and encodes a viral replicase that is associated with the novel genome synthesis and generation of a nested set of sub-genomic messenger RNAs, encoding both structural proteins present in all CoVs: Spike (S), Envelope (E), Membrane (M) and Nucleoprotein (N), and a group of proteins specific for SARS-CoV: 3a, 3b, 6, 7a, 7b, 8a, 8b, and 9b [2] .

So far, there is neither a drug nor a vaccine for COVID-19. The rapid development and identification of efficient interventions against SARS-CoV-2 remains a major challenge. Elfiky showed that Sofosbuvir, Ribavirin, Galidesivir, Remdesivir, Favipiravir, Cefuroxime, Tenofovir, and Hydroxychloroquine could bind to the RdRp active site tightly and supposed to be good candidates for clinical trials [3] . Recently, Stilbenoid analogues have been reported to be potential disruptors of the SARS-CoV-2 spike protein and human ACE2 receptor complex [4] . One study suggested hydroxychloroquine and azithromycin as a treatment for COVID-19 [5] and immediately refuted by others [6] . Remdesivir and chloroquine were shown to inhibit SARS-CoV-2 in vitro [7] . Lopinavir exhibited an anti-CoV effect in vitro and is tried for clinical treatment of COVID-19 [8, 9] . Nelfinavir was shown to inhibit replication of the SARS coronavirus (SARS-CoV), which could reduce the replication of virions from Vero cells [10] and was predicted to be a potential inhibitor of SARS-CoV-2 main protease [11] . Attention has been given to the development of furin inhibitors as a potential therapeutic platform against SARS-CoV-2 infection. However, furinlike enzymes contribute to several pathways and systemic inhibition may lead to some adverse effects [12] . Although repurposing of drugs is a good idea, when their effectiveness is not certain, novel drugs are to be designed and developed specifically for novel viruses like SARS-CoV-2 M pro . Structure-based virtual screening and molecular dynamics approaches are particularly suitable to identify novel SARS-CoV-2 inhibitors [13] .

The coronavirus main protease (M pro) is essential for the viral gene expression and replication by the proteolytic cleavage of replicase polyproteins, without which the virus replication is severely hampered and is an important target for anti-CoV drug design [14] . M pro has emerged as the most potent antiviral target because of its main role in self-maturation and subsequent maturation of polyproteins [15] . X-ray structures of the unliganded SARS-CoV-2 M pro and its complexes with various ligands have been reported. Since there are no human counterparts with similar cleavage specificity, inhibitors of SARS-CoV-2 M pro are unlikely to be toxic [16] .

SARS-CoV-2 M pro is a cysteine protease containing Cys-145 and His-41 catalytic dyad in its active center. The proteolytic process is believed to be dependant on active site cysteine (Cys-145) side chain thiolate nucleophile attack on amide bond of the substrate [17] . The -SH group of Cys145 is ion-paired with His41 forming Cys145-His41 catalytic dyad, which differs from most serine proteases that have a catalytic Ser-His-Asp triad in their active sites. In M pro , a stable water molecule occupies the Asp position of the typical serine protease triad [18] .

Both covalent and non-covalent inhibitors of M pro are of immense value as a potent drug against SARS-CoV-2. Covalent inhibitors establish a covalent bond (C-S) with the reactive thiol group of Cys145 and form favorable interactions with residues lining the substratebinding site [19] . Non-covalent inhibitors mainly act by binding to the active site stronger than the natural substrate by non-covalent bonds like hydrogen bonds, van der walls interactions, and electrostatic interactions. Covalent inhibitors are highly selective inhibitors. However, irreversible drug toxicity can be a real challenge related to this class of therapeutics. On the other hand, non-covalent inhibitors could never cause irreversible toxicity but might be less effective. It is evident that both classes have their merits [20] .

The objectives of this study were i) to identify evolutionarily important active site amino acids by structure-based sequence alignment of SARS-CoV-2 and SARS-CoV M pro enzymes ii) to identify potential non-covalent M pro inhibitors by screening protease-inhibitor-like compounds available in the ZINC database by molecular docking studies iii) prediction of absorption, distribution metabolism, excretion and toxicity properties of the top-scoring inhibitors using in silico methods iv) to validate the stable binding of the lead compounds with SARS-CoV-2 M pro by molecular dynamics (MD) simulations and v) to calculate thermodynamic binding energies for each lead compound -SARS-CoV-2 M pro complex using Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) calculations.

The three-dimensional structures of SARS-CoV-2 M pro (PDB IDs: 6LU7, 6Y84, 6YB7, 5RE4 and 6W63) were obtained from RCSB-PDB [21] . PDB structures of SARS-CoV M pro (5NH0, 1P9S and 2ZU2) with 95% structural similarities with SARS-CoV-2 M pro were selected using the jFATCAT-rigid algorithm [22] and retrieved. The sequences of all these structures were used for further structure-based sequence alignment of SARS-CoV-2 and SARS-CoV M pro enzymes. The crystal structure of SARS-CoV-2 M pro in complex with an inhibitor N3 determined by X-ray diffraction with 2.16 Å resolution PDB 6LU7 [23] was used as the drug target for molecular docking and molecular dynamics (MD) studies. Ton et al. [24] screened 1.3 billion compounds from the ZINC15 [25] library and identified 1,000 probable ligands for SARS-CoV-2M pro protein. The compounds were made publicly available for further research by the scientific community. All these 1000 ligands for the SARS-CoV-2 Mpro protein were downloaded in SDF format and used as the small molecular library for screening.

Structure-based sequence alignment was carried out to discern the amino acids that are conserved evolutionarily, particularly in the active site. The sequences of all M pro structures were exported into FASTA format and aligned using ClustalO [26] and their evolutionary relationship was inferred by the neighbour-joining method [27] using Mega X [28] . The bootstrap consensus tree resulting from 500 replicates represented the evolutionary history [29] . The Poisson correction method was used to calculate evolutionary distances [30] . This analysis involved 8 M pro sequences. Ambiguous positions were removed by the pairwise deletion option and finally 307 positions were included in the dataset. Structure-based alignment was performed and important features of the sequences and structures were deciphered using ESPript [31] .

The selected drug target PDB 6LU7 [23] was prepared at pH (7.0), water molecules and the inhibitor were removed from the structure and incomplete residues were fixed using UCSF Chimera Version 1.14 [32] and Swiss-PDB Viewer v4.1.0 [33] . Druggable binding pockets were predicted by the CASTp 3.0 server [34] . All the 1000 ligands were protonated, cleaned 3 dimensionally and exported to PDB format using ChemAxon MarvinView 20.9.0 [35] .

The target enzyme 6LU7 and the ligand molecule library were converted into PDBQT format using Open Babel [36] . AutoDock Vina 1.1.2 [37] in MGLTools PyRx Virtual Screening software [38] was used to screen the ligand library against the target enzyme. UCSF Chimera 1.14 [39] was used for analysis and rendering of the docking results. Since Lopinavir [40] and Nelfinavir [41] were shown to be effective in COVID-19 patients and also protease inhibitors, they were included as positive controls.

Absorption, Distribution, Metabolism, Excretion and Toxicity predictions were carried out for all the top 10 lead compounds identified from docking results along with positive controls using the SwissADME server [42] . AMES toxicity, carcinogenicity and acute oral toxicity of lead compounds were predicted by the AdmetSAR 2.0 [43] .

The GROMACS 5.1.2 software [44] was used to carry out MD simulations using the GROMOS 96 54a7 force field. The topology file was generated from the PDB file through the pdb2gmx program of GROMACS. The PRODRG2.5 server [45] was used to build the topology parameters of lead1, lead2, lead3, Lopinavir and Nelfinavir. MD simulation of 20 ns with a time step of 2 fs at a 300 K temperature was carried out. A total of 6 systems; one SARS-CoV-2 M pro apoenzyme (6LU7) and 5 SARS-CoV-2 M pro complexes viz., 6LU7-Lead1, 6LU7-Lead2, 6LU7-Lead3, 6LU7-Lopinavir and 6LU7-Nelfinavir were prepared. The apo-protein and protein-ligand complexes were submerged in a solvent box, surrounded by 4 Na ions to maintain electro-neutrality. Energy minimization was done using the steepest descent algorithm in order to alleviate the bad van der Waals interactions strain. After the convergence of the system, equilibration was carried out with NVT and NPT ensembles to attain the system temperature and pressure of 300 K and 1 bar, respectively. The electrostatic interaction in the systems was measured with the particle mesh Ewald. The GROMACS molecular dynamics simulation engine -mdrun‖ program was used to carry out equilibration MD simulations. The temperature and pressure of the system were kept constant using the velocity-rescale algorithm and the Parrinello-Rahman algorithm. The LINEAR Constraint Solver algorithm was utilized to restrain all the bonding lengths [46] . The simulation trajectories were examined with the Visualization Molecular Dynamics (VMD) software package [47] . Root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg) and the number of hydrogen bonds were calculated by the gmx rms, gmx rmsf, gmx gyrate and gmx hbond tools of GROMACS, respectively. The Protein-Ligand Interaction Profiler (PLIP) web server [48] was used to analyze the docked complexes for types and distances of non-covalent bonds.

Binding energy of each protein-ligand complex was calculated by the MM-PBSA method [49] using the g_mmpbsa tool [50] . The binding energy of each complex was computed from van der Waal energy, electrostatic energy, polar solvation energy and non-polar solvation energy based on the solvent accessible surface area (SASA) model using the script MmPbSaStat.py. The final contribution energy of each residue from individual energetic terms obtained from the g_mmpbsa was calculated using MmPbSaDecomp.py script. Sequence alinement showed some residues that are unique for SAR-CoV-2 in comparison to SARS-CoV, which further showed a profound effect on docking studies resulting in new lead compounds, which were not reported for SARS-CoV M Pro . The evolutionary replacement of amino acids from SARS-CoV to SAR-CoV-2 were found to be Leu3Phe, Gln8Phe, Phe12Lys, Lys15Gly, Val17Met, Arg19Gln, Cys21Thr, Tyr22Cys, Asn24Thr, Val26Thr, Gly33Asp, Ile/Thr35Val, Ala44Cys, Ser/Pro45Thr, Thr47Glu, Thr48Asp, Ser/Val49Met, Ile52Pro, Asp53Asn, Asp55Glu, Ile141Leu, Ala144Ser, Gln164His, Ile165Met, Gly168Pro, Ser169Thr, Gln188Arg, and Arg189Gln.

The X-ray crystallographic structure of SARS-CoV-2 M pro ( Fig.2A) The top 1000 viral protease inhibitor-like molecules identified by Ton et al. [24] were obtained in SDF format, cleaned three-dimensionally, hydrogenized and used as smallmolecule library for screening. SARS-CoV-2 M pro structure PDB 6LU7 was prepared by removing water and ligand molecules. Two lead compounds Lead1-ZINC000621278586 and Lead2-ZINC000621285995 showed maximum binding affinities of -9.3 kCal/mol and -9.1 kCal/mol towards the SARS-CoV-2 M pro active site, respectively, which are far better than the positive controls Lopinavir (-6.8 kCal/mol) and Nelfinavir (-7.9 kCal/mol) ( Table 1) . Binding of three drugs viz. lopinavir, oseltamivir, and ritonavir simultaneously with the protein resulted in a binding affinity of −8.32 kCal/mol [15] . Three molecules of natural origin from Moroccan medicinal plants Crocin, Digitoxigenin and β-Eudesmol were docked with SARS-CoV-2 M pro and showed an interaction energy equal to -8.2 kCal/mol, -7.2 kCal/mol and -7.1 kCal/mol, respectively. Both lead1 and lead2 were found to surpass these previously reported binding energies.

Molecular docking of SARS-CoV-2 M pro with lead1 and lead2 are depicted in Fig.3 . Lead1 binds to the active site formed by Domain-I and Domain-II chymotrypsin-like β barrels, where the active site dyad His41and Cys145 is located. Khan et al. proposed 5 inhibitors, all of which exhibited significant interactions with the same active site dyads [52] . The binding of lead1 was found to be stabilized by various hydrogen bonds and alkyl bonds. His41, Met49 and Met165 showed a stronger tendency to form alkyl bonds; on the other hand, Phe140, Leu141, Asn142, Gly143, Ser144, Cys145, His164, Glu166 and Gln189 formed hydrogen bonds, giving rise to a stronger binding affinity of -9.3 kCal/mol. Lead2 bound to the same active site with the His41 and Cys145 dyad. His41, Met49 and Met165 formed alkyl bonds with the lead2, while Phe140, Ser144 and Cys145 were involved in conventional hydrogen bonds with a total binding affinity of -9.1 kCal/mol. It is interesting to note that some amino acids that were unique to SARS-CoV-2 M pro as identified by the sequence alignment studies were involved in critical bond formation. Some of these amino acids were Met49, Lew141, Ser144, His164, and Met165 (Table-1 ). Islam et al. reported that analysis of the non-covalent interactions of a best five phytochemicals with the main protease revealed that the selected compounds interacted with either both (Cys145 and His41) or at least one catalytic residue [53] . Zhang et al. demonstrated that dimerization of SARS-CoV-2 M pro is crucial for catalytic activity because the N-finger of each of the two protomers interacts with Glu166 of the other protomer. This interaction helps shape the S1 pocket of the substratebinding site [16] . It is interesting to note that almost all 10 lead compounds showed binding preference to the same active site amino acids His41, Cys145 and Glu166. Lead10 showed a binding affinity of -8.7 kCal/mol. Lead1, lead2 and lead4 formed a hydrogen bond with Glu166. Even though the maximum number of interactions of lead4 is the same as lead1, it did not bind to the active site dyad His41and Cys145. This makes lead1 and lead2 as the favourable choices. Both lead1 and lead2 are Pyrimidin-7-one compounds. Pyrimidinones are implicated in a wide range of biological activities, including viral infections. The pyrimidone scaffold is the backbone of many of the approved anti-retrovirals e.g. Zidovudine, Didanosine and Zalcitabine [54] .

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

Absorption, distribution, metabolism, excretion and toxicity predictions were carried out for all 10 lead compounds and positive controls. The physiochemical properties like molecular weight, number of hydrogen bond acceptors/donors, topological polar surface area, lipophilicity and solubility were calculated.

The pharmacokinetics predictions (Table 2) showed that lead1 and lead 2 were nonpermeators of the blood brain barrier and skin with high gastrointestinal absorption. Lead1 was predicted to inhibit none of the Cytochrome P450 viz., CYP1A2, CYP2C19, CYP2C9, CYP2D6 and CYP3A4. Lead2 was inhibitory to CYP1A2 alone. On the other hand, both the positive controls Lopinavir and Nelfinavir were predicted to inhibit CYP2C19 and CYP3A4. Druglikeness screening showed that all lead compounds satisfied all the druglikeness rules viz., Lipinski [55] , Ghose [56] , Veber [57] , Egan [58] and Muegge [59] , except lead10, which showed violations in the Veber and Egan rules owing to its high TPSA values. On the other hand, the positive controls showed at least one violation in all the rules except the Egan rule. Medicinal chemistry analysis showed that all the leads were passed by filters for removal of pan assay interference compounds (PAINS) [60] and a list of 105 fragments identified by Brenk et al. [61] , except lead4, which showed hydantoin alert. The synthetic accessibility scores of Lopinavir and Nelfinavir were 5.67 and 5.58, respectively, while those of lead1 and lead2 were 3.58 and 3.46, which shows that these leads could be easily synthesized compared to positive controls.

Drug-induced liver injury probability values of lead1 and lead2 were found to be lower than that of Lopinavir; and the acute oral toxicity LD50 values of lead1 and lead2 were predicted to be 2.034 mol/kg and 2.371 mol/kg, respectively. Both the leads exhibited negative Ames mutagenesis probability scores and were found to be non-carcinogenic. Since, both lead1 and lead2 show exceptional druglike properties with good medicinal chemistry properties, they can further be assessed for their in vitro SARS-CoV-2 M pro inhibitory activities. However, lead1 (ZINC000621278586) could be better than lead2 (ZINC000621285995) because of its non-inhibitory nature of cytochrome P450, while lead2 inhibits CYP1A2. Apart from this minor negative characteristic, lead2 was also found to be a good SARS-CoV-2 M pro inhibitor. Non-Carcinogenic #Cytochrome P450 Inhibitors include inhibitors of CYP1A2, CYP2C19, CYP2C9, CYP2D6 and CYP3A4; all the molecules showed a bioavailability score of 0.55; b Pan assay interference compounds alert; c 105 fragments identified by Brenk database; d Synthetic accessibility score on a scale of 1-10 (1 easy to 10 difficult to synthesize).

Based on the docking scores and absorption, distribution, metabolism, excretion and toxicity predictions Lead1-ZINC00062127858, Lead2-ZINC000621285995 and Lead3-ZINC000566550443 were selected and their complexes with SARS-CoV-2 M pro were subjected to MD simulations along with complexes of SARS-CoV-2 M pro -Lopinavir and Nelfinavir positive controls. The best docking conformation of each of the complexes was chosen and used as the starting point for a 20 ns simulation. The trajectories were analyzed for stability and the complete details of conformations of proteins were observed to reconfirm the results of docking. Furthermore, the time-dependent RMSD values of atoms in the unliganded SARS-CoV-2-M pro , SARS-CoV-2-M pro -Lead1 complex, SARS-CoV-2-M pro -Lead2 complex, SARS-CoV-2-M pro -Lead3 complex, SARS-CoV-2 M pro -Lopinavir and SARS-CoV-2 M pro -Nelfinavir complexes were plotted (Fig-4A) . The complexes of all lead compounds and Lopinavir were well correlated with unliganded protein with only a few atomic fluctuations in the magnitude. Overall mean RMSD values for SARS-CoV-2-M pro apo protein and SARS-CoV-2-M pro complexes with Lead1, Lead2, Lead3, Lopinavir and J o u r n a l P r e -p r o o f Nelfinavir were found to be 0.28±0.034 nm 0.20±0.025 nm, 0.23±0.039 nm, 0.20±0.024 nm 0.21±0.025 nm and 0.26±0.060 nm, respectively. The RMSD of lead1, lead2 and lead3 complexes were less than 0.23 nm, while overall RMSD of all the complexes showed consistency within 0.3 nm over the entire trajectory, which is well within the range of previous reports [54] .

Similarly, the backbone radiation of gyration (Rg) values (Fig-4B) for SARS-CoV-2-M pro apo protein was found to be 2.195±0.016 nm and that of SARS-CoV-2-M pro complexes with Lead1, Lead2, Lead3, Lopinavir and Nelfinavir were found to be 2.218±0.014 nm, 2.203±0.016 nm, 2.222±0.017 nm 2.212±0.018 nm and 2.186±0.025 nm, respectively. Rg value of 2.2 nm for all lead compounds showed that the binding of these ligands does not cause considerable stress on the backbone of SARS-CoV-2-M pro . The data revealed that all the systems were compact throughout the simulation, which indicates that the systems are well converged. 

The binding energies for all protein ligand complexes were calculated for the last 10 ns of MD trajectories. All 5 complexes showed negative binding energies (Table 3) indicating that J o u r n a l P r e -p r o o f all the complexes were energetically stable. Lead1 showed the lowest binding energy (-129.266 ± 2.428 kJ/mol) of the lead molecules. The binding energy of lead 1 is lower than Lopinavir (-29.410 ± 9.493) and higher than Nelfinavir (-140.785 ± 3.989) and was considered as the most stable lead molecule. Lead2 and lead3 showed binding energies of -116.478 ± 3.502 and -96.864 ± 3.820, respectively, which is better than that of Lopinavir. The Lopinavir complex showed a less favorable energy value of -29.410 ± 9.493 kJ/mol. The rigorous bootstrapping used in this study resulted in a reduced standard deviation. A previous study calculated ΔG binding energy for Remdesivir, Saquinavir, Darunavir, Nat-1 and Syn-16 with target protein Chymotrypsin-like protease (3CL pro ) as -45.5240, -36.3026, -48.1041, -41.2565 and -31.5581 kJ/mol, respectively, and proposed Darunavir as the best protease inhibitor [52] . Another study reported -4.62 kCal/mol or -19.33 kJ/mol for the Mpro-ZINC000015988935 complex [62] . The lead compounds of this study, particularly lead1 and lead2, exhibited far better binding energies and hence can be expected to outperform these previously reported drugs and lead compounds. Energy decomposition plot was calculated as the energy contribution of each residue (Fig.7) . All the lead compounds showed stabilization of the complex around residue number 40 and between residues 140 to 170, indicating that they bind to the active site of SARS-CoV-2-M pro . Lopinavir showed binding in the same region with lower affinity. Nelfinavir has been shown to bind in a different region between residues 250 and 300, which makes its usability as an inhibitor questionable. The thermodynamic calculations showed that the binding of both lead1 and lead2 to the active site of SARS-CoV-2-M pro is energetically favored followed by lead3. Hence, they can act as good inhibitors of SARS-CoV-2-M pro .

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

Structure-based sequence alignment of SARS-CoV-2 and SARS-CoV main proteases showed extensive similarities in their secondary and tertiary structures. Hence, it can be construed that in silico molecular approaches used for screening SARS-CoV M pro inhibitors can also be used for finding potent SARS-CoV-2 M pro inhibitors. This study screened 1000 proteaseinhibitor-like molecules against SARS-CoV-2-M pro and proposes lead compounds viz., Lead1 -2-amino-5-{[(5R)-5-methyl-2,3,4,5-tetrahydro-1H-1-benzazepin-1-yl]methyl}-1H,7H- [1, 2, 4] triazolo[1,5-a]pyrimidin-7-one (ZINC000621278586) and Lead2 -2-amino-5-({1',2'-dihydrospiro[cyclobutane-1,3'-indol]-1'-yl}methyl)-1H,7H-[1,2,4]triazolo[ 1,5-a]pyrimidin-7-one (ZINC000621285995) as potent SARS-CoV-2 M pro inhibitors with better binding properties and less toxicity than existing protease inhibitors like Lopinavir and Nelfinavir. Both these molecules are [1, 2, 4] triazolo[1,5-a]pyrimidin-7-one compounds and their antiviral properties have not been reported previously. MD simulation studies showed J o u r n a l P r e -p r o o f that both lead compounds had high binding affinity towards SARS-CoV-2-M pro . Binding free energy calculations by MM/PBSA showed energetically stable negative values of -129.266 ± 2.428 kJ/mol and -116.478 ± 3.502 kJ/mol for lead1 and lead2, respectively. Taken all together, according to docking studies, physicochemical characterizations, ADME/Tox predictions and molecular dynamics studies, it is safer to conclude that these pyrimidin-7-one lead compounds could be considered as possible SARS-CoV-2 M pro inhibitors. However, the inhibitory activity of these lead compounds should be further tested in vitro and animal studies. Future perspective of this study could be designing a covalent inhibitor based on these pyrimidin-7-one compounds that could form favourable covalent bond with the reactive thiol group of active site Cys145.

 Two lead compounds viz. lead1 (ZINC000621278586) and lead2 (ZINC000621285995) were found to be potent SARS-CoV-2 M pro inhibitors based on molecular docking of 1000 protease-inhibitor like molecules derived from ZINC database.  Both these molecules are [1, 2, 4] J o u r n a l P r e -p r o o f

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The authors express immense gratitude to the Google Cloud Platform for supporting this work with COVID HCLS Research Credit 46225154. We are grateful to Dr.K.R. Venkatesan, Principal and the management of Sri Sankara Arts and Science College, for providing the facilities and knowledge resources to conduct this research. We are thankful to Mr.B.Rajganesh, Enstoa India Private Limited, Bangalore, India, for providing computational resources for docking studies.

The authors declare that they have no conflicts of interest.