key: cord-0780416-burdsju5
authors: Yadav, Rohitash; Imran, Mohammed; Dhamija, Puneet; Chaurasia, Dheeraj Kumar; Handu, Shailendra
title: Virtual screening, ADMET prediction and dynamics simulation of potential compounds targeting the main protease of SARS-CoV-2
date: 2020-07-25
journal: Journal of biomolecular structure & dynamics
DOI: 10.1080/07391102.2020.1796812
sha: 379bb5d9be70150e03c0d6cf7ba206f232bcaef5
doc_id: 780416
cord_uid: burdsju5

The coronavirus disease-2019 caused by a novel SARS CoV-2 virus has emerged as a global threat. Still, no drugs are available for its treatment. The main protease is the most conserved structure responsible for the posttranslational processing of non-structural polyproteins of this virus. Therefore, it can be the potential target for drug discovery against SARS CoV-2. Twenty-one thousand two hundred and seven chemical compounds used for sequential virtual screening studies including coronavirus screening compounds (Life Chemical database) and antiviral compounds (Asinex database). The Schrodinger suite 2019 employed for high throughput screening, molecular docking and MM-GBSA through the Glide module. Subsequently, 23 compounds were selected in the phase first selection criteria for re-docking with AutoDock and iDock followed by ADMET prediction. The drug-likeness predicted through Lipinski’s rule of five, Veber’s rule and Muegge’s rule. Finally, three ligands were selected for molecular dynamics simulation studies over 150 ns against the main protease of the SARS CoV-2. They showed promising docking scores on Glide, iDock and AutoDock Vina algorithms (ligand F2679-0163: −10.75, −10.29 and −9.2; ligand F6355-0442: −9.38, −8.61 and −7.6; ligand 8250: −9.795, −7.94 and −7.5), respectively. The RMSD parameter remained stable at 2.5 Å for all the three ligands for 150 ns. The high RMSF fluctuations, RoG of around 22 Å and the binding free energy were favorable in each case. The hydrogen bond interactions of 8250, F6355-0442 and F2679-0163 were six, five and three, respectively. These compounds can be further explored for in vitro experimental validation against SARS-CoV-2. Communicated by Ramaswamy H. Sarma

The COVID-19 has been an extremely contagious and pathogenic viral infection first observed in the Wuhan city of China (Singhal, 2020) . The causative agent was found to be a novel virus belonging to the coronavirus family, particularly from the beta subfamily (Cascella et al., 2020) . The virus has been designated as SARS CoV-2, as it causes severe acute respiratory syndrome and pneumonia (Pal et al., 2020) . The earlier viruses from the same family such as SARS and MERS have shown 10% to 36% mortality (da Costa et al., 2020; Paules et al., 2020) which was much higher than 2-4% by SARS CoV-2 (Roussel et al., 2020) , but attack rate of this virus is said to be very high, and therefore, it has been declared as a global pandemic by World Health Organization (Zarocostas, 2020) .

The coronaviruses are positive-stranded spherical enveloped RNA viruses that infect a variety of animals and human posttranslational processing of various viral proteins (Boopathi et al., 2020; Chen et al., 2020; Stobart et al., 2013) . However, papain-like protease (PL pro ) also cleaves the large polyproteins into NSPs (B aez- Santos et al., 2015) . These NSP play an essential role in viral genomic expression and replication (Snijder et al., 2016) . Both of them have been used as targets for potential drug therapy for SARS CoV-2 infections (Lindner et al., 2005; Wu et al., 2020) .

Some compounds have been found as a potential treatment against SARS CoV-2 in many studies (Caly et al., 2020; Elmezayen et al., 2020; Hendaus, 2020; Zhou et al., 2020) . There are studies in which antiviral drugs such as Anti-HIV, Anti-HCV, Anti-HBV and drugs for other infectious diseases have been suggested as therapies (Barlow et al., 2020; Bhatnagar et al., 2020; Cherian et al., 2020; Fan et al., 2020; Sayad et al., 2020) . Some of them have suggested that combinations of drugs may provide rapid viral clearance than the single agent alone Sheahan et al., 2020) . Additionally, many studies have ventured into finding the treatment in the phytochemicals as well (Elfiky, 2020a (Elfiky, , 2020b Islam et al., 2020) . Several studies have shown the drugs that can be suitably explored as inhibitors of M pro as well (Aanouz et al., 2020; Bhardwaj et al., 2020; Das et al., 2020; Enmozhi et al., 2020; Gupta et al., 2020; Havranek & Islam, 2020; Kumar et al., 2020; Umesh et al., 2020) . Even after so many studies, there is no approved drug yet against this virus. Therefore, there is an urgent need to conduct basic research to identify the drugs against the SARS CoV-2. Therefore, this study was conducted with the newly categorized coronavirus screening compounds by the Life Chemicals database and other antiviral drug-like chemical compounds to find the potential treatment through molecular docking, molecular dynamics simulations (MDS) and ADME/T (absorption, distribution, metabolism, elimination and toxicity [ADME/T]) studies. Figure 1 shows the virtual screening flow of the study, wherein three separate databases were used for the computational screening of 21,207 ligands against the target protein of SARS CoV-2. One was a very new group placed in the Life Chemical database (https://lifechemicals.com/) as coronavirus screening (Anti-nCoV2) compound library (Lib-A), and second, from the same database was taken as Antiviral drugs (Lib-B). The third library of chemical compounds with antiviral properties was taken from the Asinex database (Lib-C; http://www.asinex.com/antiviral/). Out of the total 21,207 compounds, the Lib-A had 2327 Anti-nCoV2 screening ligands, Lib-B had 10,158 compounds with antiviral activity and Lib-C had 8722 chemical moieties with antiviral properties. The main protease target protein of SARS CoV-2 (PBD IDs: 7BRP) was used in this study as it is the recently released crystal structure and is highly conserved in nature. The three-dimensional structure of the target protein was retrieved from the protein data bank (https://www.rcsb.org/ structure/7BRP). This protein structure was selected because of its high resolution (1.8 Å) and recognized by the X-ray diffraction technique. The three-dimensional structure of M pro and its interactive co-crystallized ligand site on its structure is represented in Figure 2 (A-C).

The target protein was prepared before starting the docking processes. It was done with the help of Schrodinger's protein preparation wizard tool (Madhavi Sastry et al., 2013) . The three-dimensional structure of the target protein (PDB IDs: 7BRP) was prepared. This was done by correcting bonds, removing unrelated chemical complexes, eliminating water molecules from het groups, creating zero-order bonds to metal atoms, the addition of hydrogen bonds, conversion of selenomethionine to methionine, filling in missing side chains and generating het states utilizing EpikPh 7 to þ2. The OPLS3e was used for the optimization of protein HBs through overlying and minimizing hydrogen utilization. The molecular modeling package of the ligand preparation module in OPLS3e was used to prepare ligands with appropriate parameters such as optimization, determination of promoters, tautomers, ionization state at pH 7.0, ring confirmation, two dimensional to three-dimensional conversion and correction of partial atomic charges (Madhavi Sastry et al., 2013) .

One of the most crucial aspect in designing a drug via computational docking or digital screening was recognizing the suitable active site for binding the ligand molecules. The grid generation was the next step with the selection of co-crystal ligand of the target protein ( Figure 2 ). The OPLS3e force field was used to generate partial cut off charge of 0.25 Å and mounting of the protein atoms through default parameters of 1 Å radii of Van der Waal's scaling factor. The dimensions of the grid box and receptor setup were x ¼ 20 Å, y ¼ 20 Å, z ¼ 20 Å and x ¼ 10 Å, y ¼ 10 Å, z ¼ 10 Å during docking study, respectively, with a grid space of 1 Å (Halgren et al., 2004) .

The sequential molecular docking study of the selected ligands (Lib-A: 2327 Anti-nCoV2 screening compounds from Life Chemicals, Lib-B: 10,158 antiviral drugs like compounds from Life Chemicals, and Lib-C: 8722 antiviral compounds from Asinex database) was done against the main protease of SARS CoV-2. High throughput virtual screening of the selected chemical compounds from Lib-A, B and C was performed against target protein using Schrodinger's Ligand docking module with a flexible docking parameter. The flow of the docking study has been described in Figure 1 wherein 10% of the ligands (300 Ligands from Lib-A, 1000 from Lib-B and 870 Ligands from Lib-C) were selected for the standard precision (SP) docking. After that, 10% of the compounds (30 from Lib-A, 100 from Lib-B and 87 from Lib-C) from SP docking underwent extra precision (XP) docking (Kesharwani et al., 2016) . Subsequently, to get more insights into these docking results, the iDock and AutoDock Vina docking program were also employed. Thus, compounds with docking scores more negative than À8 were selected for docking with iDock and AutoDock Vina (see 2.7 Lead compounds selection criteria). The molecular mechanics generalized born surface area (MM-GBSA) methods were used for binding free energy (DG bind) calculation of these selected ligand molecules.

The AutoDock Vina (Trott & Olson, 2009 ) and iDock (Li et al., 2012) programs were used for further validating the findings of Glide docking analysis of selected 23 ligands (8 from Lib- A, six from Lib-B and nine from Lib-C). All the ligands were converted into 'pdbqt' format using the AutoDock tools. The dimensions of the grid box and receptor setup were similar to the Glide docking grid box edges.

The OPLS 3e Force field of Schrodinger suite module was used for prime MM/GBSA calculation with DG bind between protein and ligand complexes (Genheden & Ryde, 2015) . The prime MM-GBSA strategy was used to calculate the DG bind of every selected ligand. Thus, the top three compounds extracted after the docking process underwent the DG scores. Therefore, the final prioritization of the optimized lead compound was based on docking scores, ADME/T studies and DG bind, which were further followed up by the molecular dynamic studies.

The drug likeliness properties of the final 23 compounds from Lib-A, B and C, were calculated by using SwissADME (Daina et al., 2017) and admetSAR-2.0 online tools (http:// lmmd.ecust.edu.cn/admetsar2/). The predicted result consists of physiochemical properties, lipophilicity, water-solubility, pharmacokinetics, drug-likeness and toxicity studies.

The physicochemical properties include molecular weight (MW), number of the rotatable bonds (NRB), hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), molar refractivity (MR) and topological surface area (TSA). The other two significant determinant are lipophilicity and solubility that are monitored for favorable drug development. The preceding parameter is predicted by using an average of five logarithmic predictions of the octanol-water coefficient (Po/w). They include consensus of values based on atomistic and knowledge Log Po/w (XLOGP3), physics Log Po/w (iLOGP), atomistic Log Po/w (WLOGP), topological Log Po/w (MLOGP) and hybrid fragmental/topological Log Po/w (SILICOS-IT). The latter property is also based on logarithmic solubility (Log S) predicted values scale. The solubility characteristic of the compounds as per this scale is defined as insoluble if more negative than À10. The solubility ranges from poorly soluble to highly soluble corresponding to the value of À10 to greater than zero, respectively. The values of the poorly soluble compounds lie in between À10 and À 6. The higher than À6 and less than À4 is classified as moderately soluble. The soluble compounds are in between À4 and À2. The values between À2 and 0 are very soluble, while higher than zero are highly soluble.

The estimation of different pharmacokinetic properties such as ADME/T along with drug likeliness was done for these compounds by applying three established methods such as Lipinski's rule of 5 (Lipinski, 2004) , Veber's rule and Muegge's rule (Muegge, 2003) .

The Lipinski's rule defines an orally active drug, which confirms to the number of hydrogen bonds acceptor 10, HBD 5, MW < 500 Da and LogP (The logarithm of Octanolwater partition coefficient) 5. However, Veber's rule takes two parameters to determine the good oral bioavailability in the majority of the compounds, such as < 10 rotatable bonds (ROTB) and polar surface area (PSA) less than 140 Å. Moreover, according to the Muegge's rule, for a drug like chemical compound to become a successful drug molecule, it has to pass a pharmacophore point filter such as MW between 200 and 600 Da, XLogP À2 to 5, TPSA less than 150, number of rings less than 7, number of carbon less than 4, number of heteroatoms less than 1, number of rotatable bonds less than 15, HBA less than 10, HBD less than 5.

The prediction of toxicity was made by a new online admetSAR-2.0 webserver. The parameters such as human ether-a-go-go-related gene inhibition, AMES toxicity, acute oral toxicity (c) and carcinogenicity (Class-3) were determined. The US EPA based criteria were used to categorize predicted LD50 values into four classes. The Class I comprises compounds with LD50 lower than or equal to 50 mg/kg. Class II includes the compounds with LD50 values higher than 50 mg/kg but lower than 500 mg/kg. The Class III compounds have values more than 500 mg/kg but lower than 5000 mg/kg. The compounds with values higher than 5000 mg/kg are classified as Class IV. However, the rat carcinogenicity is classified as per the Carcinogenicity Potency Database of TD 50 values as 'Danger', Warning' and 'Nonrequired'. The 'Danger' compounds have TD50 value of 10 mg/kg body wt./day. The 'Warning' compounds have TD50 > 10 mg/kg body wt./day. However, non-carcinogenic compounds are assigned as 'Non-required'.

The selection of the final lead compounds was made with two sets of selection criteria extends over two phases of the process based on criteria such as the protein-ligand binding affinity, binding free energy, drug likeliness properties, physiochemical interactions between ligand and the target protein. The Glide docking score less than À8 was taken for the selection of ligands in the phase one-selection criteria. The further selection of the potential compounds in the second phase involves consideration of four parameters. These were docking score lower than or equal to the co-crystal ligand docking scores with respective docking tools, binding fee energy less than À60 (kcal/mol), ability to interact with a minimum of four amino acids implicated in positioning the acceptor substrate and drug likeliness properties through Lipinski's rule of five, Veber's rules criteria, Muegge's rules and toxicities study. These filtering criteria would lead us to conclude the potential compounds for the MDSs and drug designing against SARS CoV-2.

The MDS studies were performed to analyze the stability of ligand-protein interactions with respect to the physical transition of the structural aspect of macromolecules to the functional relevance of the complex. Additionally, it demonstrates strength, pattern, dynamic conformational changes and intermolecular properties of the interactions. The selected complex then underwent MDS for 150 ns using AMBER 18 software used for the MDSs of ligand-protein complexes. It evaluates the root mean square deviation (RMSD), root mean square fluctuation (RMSF), intermolecular hydrogen bonds interactions, binding free energy and radius of gyration (RoG) for the elucidation of conformational, structural and compactness of the protein-ligand (Nandekar et al., 2013) .

The Leap module of the software created the molecular receptor topology. The ligand-protein complex obtained from molecular docking was the starting structure of each MD simulations. Subsequently, followed established protocol such as neutralization and submerging complex into the water molecule rectangular box (TIP3P) where the protein atoms were 10 Å away from the nearest edge of the box. The minimization of the solvent system was achieved by freezing the protein and removing the bad contact with restraint on the heavy atoms, first through 2500 steepest descent method and then conjugation gradient for 2000 further steps. The system was gradually heated from 0 to 300 K temperature at 1 atm pressure (NPT conditions). It was achieved for 50 ps followed by 50ps of density equilibrium and 1 ns of constant equilibrium to exchange potential and kinetic energies. Afterward, the temperature was kept constant by using the Berendsen algorithm.

The MDS was run for 150 ns to evaluate the stability of the docked ligand-receptor complexes preceded by the system equilibration for 500ps on the canonical NPT ensemble. The covalently bound hydrogen atoms were constrained by the SHAKE algorithm with 2 fs time and temperature control by Langevin dynamics. Finally, the recording was made for every 5ps using the particle-mesh Ewald summation method for treating electrostatic interaction. The CPPTRAJ module of the Amber18 software was used to analyze the MD trajectories for every 20 ns. The MM-GBSA binding free energy was calculated by inbuilt Amber Tools. The RMSD, RMSF and RoG values were recorded for 150 ns.

The process of drug designing necessitates the accuracy of the quality and reliability of the three-dimensional structure of the target protein. That can be judged by using the PROCHECK server to develop Ramachandran plot, which displays allowed, and the disallowed regions regarding backbone dihedrals of protein residues. The essential condition of being a model of good quality having more than 90% of residues in favored regions. This determines a good worth of stereo-chemical quality of three-dimensional protein structure with minimum steric interaction concerning the forbidden psi and phi angles. Figure 3 and Table 1 show that 91.2% residues lie in the most favored region, and only 0.6% lie in the disallowed region of the three-dimensional structure of target protein (PDB ID: 7BRP). The active site was predicted after the selection of the co-crystal ligand of the target protein. The receptor grid of the protein was done similarly to the co-crystal ligands of the protein. Their identification helps in designing potent protein inhibitors through their binding sites.

Identification of active site residues and location of the target structures help in designing potent drug molecules via ligand-protein docking. The active site was predicted after the selection of the three-dimensional structure of the target protein. Twenty-one thousand two hundred seven compounds were screened and docked against the target protein (Figure 4(A) ). After virtual screening and molecular docking, 23 compounds were finalized (eight from Lib-A, six from Lib-B and nine from Lib-C) in first phase selection criteria based on their Glide docking scores (less than À8 docking score)

with the target proteins. These 23 ligand molecules were selected for ADMET prediction, DG bind calculation and docking with AutoDock Vina and iDock. Three ligand molecules (two compounds from Lib-B and one compound from Lib-C) were found to be potential hit candidates for further exploration after the second phase (see lead compounds selection criteria in the Material and Methods section). The comparative docking score of these three compounds shown in Figure 4 (B) with respect to the reference ligands.

3.2.1. Docking score (glide, iDock and AutoDock vina) and binding free energy (DG bind) calculation by prime MM-GBSA of top eight compounds from Library-A Table 2 (A-1 to A-8) shows the two-dimensional structures, free binding energy, docking scores, number of hydrogen bonds and interactive residues of best eight ligand molecules from Library-A. These eight ligand molecules had less than À8 docking scores with glide docking in phase one. Their free binding energies and docking scores, such as Glide, iDock and AutoDock Vina scores were recorded. The range of DG bind lies between À60.87 and À67.88 of the most notable top eight compounds. The docking scores of these eight compounds are between À8.0 and À8.81 with Glide; À7.68 to À10.46 with iDock and À6.5 to À8.5 with AutoDock Vina methodology. The F0015-0201 compound had good docking scores of À8.817, À10.46 and À8.5 with Glide, iDock and AutoDock Vina, respectively, but it was not proceeded for further MDS study because of unfavorable AMES toxicity results. The number of hydrogen bonds made and interactions with amino acid residues are shown in Table 2 . Out of the total eight compounds, one interacted with five hydrogen bonds, five of them formed four hydrogen bonds, and the rest two compounds interaction with three hydrogen bonds. All hydrogen bond interactive amino acids are HIS164, LEU141, GLY143, SER144, CYS145, GLU166, ARG188, but interestingly, all compounds interaction was shown mainly with two common amino acids GLY143, CYS145.

binding free energy (DG bind) calculation by prime MM-GBSA of top-six compounds from Library-B One compound forms five hydrogen bond interactions while the other five compounds have shown four hydrogen bond interactions with the target protein. All hydrogen bond interactive amino acids are LEU141, GLU166, THR190, GLN192, GLY143, SER144 and CYS145. All six compounds have shown interaction with two common residues GLY143, CYS145, similar to Lib-A. Finally, two compounds were selected for further MDS from Lib-B as they pass all the selection criteria (see lead compounds selection criteria in the Material and Methods section).

The first compound (F2679-0163) has a good docking score of À10.75, À10.29 and 9.2 with Glide, iDock and AutoDock Vina, respectively. Its binding free energy is À61.37 and forms four hydrogen bond interactions with LEU141, GLY143, CYS145 and GLU166 ( Figure 5 ). Docking scores of second compounds with Glide, iDock, AutoDock Vina are À9.38, À8.61 and 7.6, respectively, and four hydrogen bond interactions with GLU166(x2), CYS145, GLY143. The DG bind of the second compounds is À59.75 (% À60.00). Both compounds have shown good docking scores with all docking methods and more negative than crystal ligand Glide docking scores.

binding free energy (DG bind) calculation by prime MM-GBSA of top nine compounds from Library-C scores, number of hydrogen bonds and hydrogen bond associated amino acids. The DG bind lies between À48.74 and À65.92 of the most notable top nine compounds from Lib-C. The docking score of these nine compounds is between À8.212 and À9.795 with Glide, À7.94 to À9.67 with iDock and À7.2 to À8.3 with Auto Dock Vina. Among all nine compounds, one compound formed seven hydrogen bond interactions, oneformed six, the other three formed five, two of them formed four and rest two compounds formed three hydrogen bond interactions with the target protein. All hydrogen bond interactive amino acids are HIS164, LEU141, GLY143, SER144, CYS145, GLU166 and ARG188. Among these residues, most of the compounds interact with three amino acid residues, including CYS145, SER144 and GLY143. One compound was passed all the selection criteria (see lead compounds selection criteria in the Material and Methods section) and selected for further MDS from Lib-C among all the best nine compounds.

One compound stands out with its parameter in this database, which can be worthy of further development of drugs against the M pro of SARS CoV-2. The DG bind of this compound (Asinex ID: 8250) is À63.70 and Glide, iDock and AutoDock Vina docking scores are of À9.795, 7.94 and 7.5, respectively. Four hydrogen bond interactions formed with THR190, GLY43, SER144 and CYS145 ( Figure 5 ).

In silico tools such as SwissADME and admetSAR À2.0 webserver can lead to early predictions of Physiochemical properties, ADME/T and drug-likeness properties. The oral bioavailability of the possible active compounds was Table 2 . Two-dimensional structure, free binding energy, docking scores, number of hydrogen bond and interactive residues of best 23 ligands (from Lib-A: A-1 to A-8; Lib-B: B-9 to B-14 and Lib-C: C-15 to C-23) with target protein structure of SARS CoV-2. Table 3 . Therefore, final compounds were predicted to have good bioavailability and satisfied the drug likeliness parameters according to these rules. Moreover, 20 compounds show high human intestinal absorption except three compounds (F0648-0053, F0648-0756 and 5804) which have low absorption. Log S scale predicted the solubility level of 23 compounds, out of which, one compound is very soluble, four compounds are moderately soluble, and remaining18 compounds are soluble. Out of 23 compounds, three compounds (F0015-0201, F1011-1885 and F1057-0056), have AMES toxic nature, and one (F0452-4293) has carcinogenic nature based on the toxicity screening results of ligands. The rest 19 compounds were relatively safe (Table 3) .

The compounds that qualify two-phase lead optimization screening selection criteria were taken for the molecular dynamics studies. Out of the 23 compounds, three compounds (F2679-0163, F6355-0442 and 8250) fulfilled all the parameters further needed for molecular dynamic simulations. The parameters explored for analyses such as RMSD, RMSF, inter-molecular hydrogen bonding (H bonding), RoG and binding free energy through MDS.

The RMSD assess the structural stability of the protein-ligand complexes within a particular period. The behavior of protein-ligand complexes was calculated as a function of time performed over 150 ns by MDS. The RMSD values of proteinligand are depicted in Figure 6 (A). The ligand F6355-0442 is gradually reached the stabilized value of 2.5 Å in 50 ns and remained at the same value for 150 ns. However, it showed slightly non-significant fluctuation around 90 and 130 ns. Moreover, the RMSD graph for ligand F2679-0163 showed a similar pattern to maintain the stabilization. However, the ligand 8250 reached the average value of 2.5 Å only in 10 ns and remained there for 140 ns. Additionally, it showed little higher values at the end of the plot for the last 10 ns. Thus, all three compounds were stabilized for the majority of the MDS time duration.

The RMSF determines the deviation of the particle from their original position in the macromolecule three-dimensional structure. It details the conformational flexibility of the protein structure by identifying the flexible and rigid structures of the proteins. We find the high fluctuations in the loop of three protein-ligand complexes (M pro -F2679-0163, M pro -8250 and M pro -F6355-0442) depicting their accommodating nature of the binding site of the proteins (Figure 6(B) ). It measures the deviation of the particle from their original positions in the rigid, flexible regions.

The number of hydrogen bonds formed between ligand and protein measures the binding affinity of the ligand. Therefore, more number of a hydrogen bonds between the protein and ligands depict stronger binding affinity. Therefore, MDS analysis takes into consideration the formation and deformation of hydrogen bonds. Our study observed a maximum of six hydrogen bonds at the end of 150 ns simulation in the case of ligand F6355-0442. The M pro -8250 and M pro -F2679-0163 show five and three HB, respectively (Figure 7(A) ).

The folding and compactness of the proteins-ligand complexes can be judged with the help of RoG. It is an important method of revealing the influence of ligand molecules on the three-dimensional conformational structural changes after the interaction of the ligand with it. The high value of RoG depicts the sustenance of loose packing and folding behavior of the protein after the interaction with the ligands. All three ligand-protein complexes show an average high value of around 22 Å throughout the 150 ns (Figure 7(B) ). They are in its native structure as there was not much variation observed throughout the 150 ns in the RoG graph except in case of ligand 8250 where values went even higher gradually starting from 110 to 150 ns. However, there were few minor variations in RoG due to conformational changes in the secondary structure of protein during the MDS process. The RoG graph of all three complexes (Figure 7 (B)) shows that ligand remains tightly bound to the active site of the protein. Table 3 . The physiochemical, lipophilicity, water solubility, pharmacokinetics, drug likeliness and toxicity predictions of the best 23 antiviral small molecules selected from Lib-A (A-1 to A-8), and Lib-C (C-15 to C-23 

The average binding free energy (DG) of these three compounds was calculated for every 20 ns by the MM-GBSA approach up to 150 ns. Table 4 shows the change of DG values at each data point along with their standard deviations. The minimum values of DG for ligands F2679-0163, F6355-0442 and 8250 were À22.90 ± 2.97 Kcal/mol (90 ns), À24.78 ± 3.38 Kcal/mol and (150 ns) À35.94 ± 3.05 (10 ns) and maximum values were À38.99 ± 2.73 Kcal/mol (30 ns), À30.61 ± 2.47 Kcal/mol (90 ns) and À42.44 ± 3.35 Kcal/mol (110 ns), respectively. Therefore, the order of binding free was in a favorable range that can be appreciated graphically in Figure 8 .

The study has used the main protease of SARS CoV-2 as it is the recently released crystal structure (PDB ID: 7brp) and is highly conserved, predominantly responsible for posttranslational processing of various viral proteins. This study was conducted with 21,207 compounds from Life Chemicals and Asinex database to find the potential drug molecules through computational drug designing techniques. The molecular docking results showed a good binding affinity with the target protein of SARS CoV-2. The docking and MDSs studies have delineated three chemical compounds, which can be the potential drug molecules against the main protease of SARS-CoV-2. Two final compounds (Compound id: F2679-0163, F6355-0442) are from the Life Chemicals library, and the third chemical compounds library having an antiviral property is from the Asinex database (Compound ID: 8250). All three ligand-protein complexes have favorable parameter values in RMSD, RMSF, RoG, intermolecular hydrogen bonding and binding free energy for 150 ns. These compounds (F2679-0163, F6355-0442 and 8250) can be further explored for in vitro experimental validation against SARS-CoV-2. 

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Chimeric exchange of coronavirus nsp5 proteases (3CLpro) identifies common and divergent regulatory determinants of protease activity

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

Identification of new anti-nCoV drug chemical compounds from Indian spices exploiting SARS-CoV-2 main protease as target

Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods

Insight into 2019 novel coronavirus -An updated interim review and lessons from SARS-CoV and MERS-CoV

Coronavirus infection induces DNA replication stress partly through interaction of its nonstructural protein 13 with the p125 subunit of DNA polymerase d

Virtual screening and dynamics of potential inhibitors targeting RNA binding domain of nucleocapsid phosphoprotein from SARS-CoV-2

Zoonotic origins of human coronaviruses

What next for the coronavirus response? The Lancet

Network-based drug repurposing for novel coronavirus 2019-nCoV/ SARS-CoV

Thanks to the Department of Pharmacology, AIIMS, Rishikesh for providing the facility. Special thanks to Dr. Prajwal Nandekar, Dr. Vinod and Schr€ odinger's team for their corporation.

No potential conflict of interest was reported by the authors.