Computational investigation of potential inhibitors of novel coronavirus 2019 through structure-based virtual screening, molecular dynamics and density functional theory studies.

Despite the intensive research efforts towards antiviral drug against COVID-19, no potential drug or vaccines has not yet discovered. Initially, the binding site of COVID-19 main protease was predicted which located between regions 2 and 3. Structure-based virtual screening was performed through a hierarchal mode of elimination technique after generating a grid box. This led to the identification of five top hit molecules that were selected on the basis of docking score and visualization of non-bonding interactions. The docking results revealed that the hydrogen bonding and hydrophobic interactions are the major contributing factors in the stabilization of complexes. The docking scores were found between -7.524 and -6.711 kcal/mol indicating strong ligand-protein interactions. Amino acid residues Phe140, Leu141, Gly143, Asn142, Thr26, Glu166 and Thr190 (hydrogen bonding interactions) and Phe140, Cys145, Cys44, Met49, Leu167, Pro168, Met165, Val42, Leu27 and Ala191 (hydrophobic interactions) formed the binding pocket of COVID-19 main protease. From identified hits, ZINC13144609 and ZINC01581128 were selected for atomistic MD simulation and density functional theory calculations. MD simulation results confirm that the protein interacting with both hit molecules is stabilized in the chosen POPC lipid bilayer membrane. The presence of lowest unoccupied molecular orbital (LUMO) and highest occupied molecular orbital (HOMO) in the hydrophobic region of the hit molecules leads to favorable ligand-protein contacts. The calculated pharmacokinetic descriptors were found to be in their acceptable range and therefore confirming their drug-like properties. Hence, the present investigation can serve as the basis for designing and developing COVID-19 inhibitors. Communicated by Ramaswamy H. Sarma.

Introduction

In the twenty-first century, the <span class="Species">novel coronavirus 2019 is a major cause of disaster due to a lack of vaccine, drugs to treatment. The pandemic has been born from the family of <mical">span class="Species">coronavirus and can cause illness such as elevated body temperature, common cold, pneumonia, bronchiolitis, rhinitis, pharyngitis, sinusitis, Middle East respiratory syndrome (MERS) and the severe acute respiratory syndrome (SARS) (Amanat & Krammer, 2020). In 2003, SARS had killed 774 people, whereas MERS killed 858 people between 2012 and 2019 (Boopathi et al., 2020). In 2019, a new virus identified in china namely novel coronavirus disease 2019 (COVID-19) (Boopathi et al., 2020).

<span class="Species">Coronavirus has a single-stranded RNA genome (Yang et al., 2006), pleomorphic or mical">spherical shape, and bears club-shaped projections of glycoproteins on its surface (80–120 nm diameter) (Guo et al., 2010; Belouzard et al., 2012). The RNA genome is made up of ∼30,000 nucleotides and covered by enveloped structure. It encodes four structural proteins, nucleocapsid (N) protein, membrane (M) protein, <mical">span class="Gene">spike (S) protein, and envelop (E) protein and several non-structural proteins (nsp) (Boopathi et al., 2020). COVID-19 packages its genome in a nucleocapsid (N) protein and forms a ribonucleoprotein (RNP) complex. The RNP is essential for viral replication, transcription, and assembly. Several studies suggested that the modulation of N protein oligomerization by small molecules is a possible antiviral drug development strategy (Chang et al., 2016; Zumla et al., 2016).

The membrane (M) protein found in the surface of the virus and supposed to be the central organizer for the <span class="Disease">COVID-19 assembly (Sarma et al., 2020). The <mical">span class="Gene">spike (S) protein integrated throughout the viral surface and involves the attachment with the host cell surface receptors and facilitates viral entry into the host cell by fusion between the viral and host cell membranes (Kirchdoerfer et al., 2016). The S protein is composed of three identical chains with 1273 amino acid residue each and characterized by two well-defined protein domain regions: S1 and S2 subunits. These S1 and S2 subunits are involved in cell recognition and the fusion of viral and cellular membranes respectively (Walls et al., 2020). The envelop (E) protein, a small component of the virus particle, is a small membrane protein made up of ∼76–109 amino acid residues and involves in host cell interaction and virus assembly (Boopathi et al., 2020; Gupta et al., 2020).

The structural <span class="Gene">spike (S) protein of <mical">span class="Species">Coronavirus binds with the angiotensin-converting enzyme 2 (ACE2) receptors that are present on the surface of many human cells, including lungs (Hasan et al., 2020). The S protein is associated with proteolytic cleavages by host proteases, in two domain regions located at the boundary between the S1 and S2 subunits. In a later stage happens the cleavage of the S2 subunit to release the fusion peptide (Boopathi et al., 2020; Lin et al., 2020). This process will trigger the activation of the membrane fusion mechanism (Tahir Khan et al., 2020). Therefore, inhibition of the activation of membrane fusion could be a possible target for antiviral drug development against COVID-19. Coronavirus entry, replication, transcription, and formation of RNP complex are depicted in Figure 1.

Figure 1.

A schematic representation of the possible mechanism of COVID-19 entry, replication, transcription and formation of RNP complex.

A schematic representation of the possible mechanism of <span class="Disease">COVID-19 entry, replication, transcription and formation of RNP complex.

Furthermore, endosome opens to release <span class="Disease">COVID-19 to the cytoplasm and translation of viral polymerase protein takes place (Li, 2016). The positive RNA genome is translated to generate replicase proteins that use the genome as a template to generated full-length negative-sense RNAs, which subsequently serve as templates in generating addition full-length genomes (Masters, 2006; Van Hemert et al., 2008). The nucleocapsids are formed from the encapsidation of replicated genomes. The structural proteins S, M, and E are synthesized in the cytoplasm and further translation results in the transfer to endoplasmic reticulum-Golgi intermediate compartment (ERGIC) (Masters, 2006). This ERGIC membrane coalesces with the nucleocapsids to self-assembly into new virions. Finally, new virions are exported from <mical">span class="Disease">infected cells by exocytosis, so that can affect other cells.

The ongoing <span class="Disease">COVID-19 threat that emerged in China has rapidly mical">spread to the whole world and has been declared as a global public health emergency by the World Health Organization (WHO) (Muralidharan et al., 2020). Many efforts have been directed to develop potential drugs or vaccine but no drug or vaccine has not launched. Therefore, all nations implementing appropriate preventive and control measures only. So, it is an urgent need to develop effective and potential antiviral-<mical">span class="Disease">COVID-19 candidates for reducing disease severity, and transmission to block the COVID-19 outbreaks.

To accomplish the objective of the study, we have carried out the structure modeling of the crystal structure of <span class="Disease">COVID-19. The prepared protein structure was used to perform high-throughput virtual screening (HTVS) of chemical libraries. The novel hit molecules identified from docking study were selected based on the docking score, binding energy calculations, and their other interactions with amino acid residues. Also, we performed molecular dynamics simulations and molecular orbital analysis for the best two hit molecules. Pharmacokinetic parameters and bioavailability score were also assessed.

Materials and methodology

The entire computational investigation was performed on Windows 7 (64-bit) operating systems with 4 GB RAM and 2.66 GHz IntelVR CoreTM 2 Quad Q8400 processor except for molecular dynamics simulations. Molecular dynamics simulations were performed on Ubuntu 14.04.5 version in the linux environment with 4 GB RAM by Desmond.

Target protein preparation

The three-dimensional crystal structure of the <span class="Disease">COVID-19 main protease (PDB-ID: 6Y84) was retrieved from the Protein Data Bank (http://www.rscb.org/pdb). The target protein has a resolution of 1.39 Å and this indicates its good quality as a resolution of 2.0 Å is the recommended maximum for a good structural protein for docking (Hajduk et al., 2005). The retrieved protein was pre-processed using the protein preparation wizard by adding missing loops and atoms, adding missing <mical">span class="Chemical">hydrogens and assigning bond orders. Further, we removed non-essential water molecules and hetero atoms, before restrained minimization using OPLS-2005.

Binding site identification

The objective is to bind the ligands from the molecular docking to the binding site of the <span class="Disease">COVID-19 main protease protein. SiteMap tool of Maestro was employed to recognize potential binding pocket by joining together ‘sitepoints’ that most probably subsidize to tie protein-protein or protein-ligand binding (Halgren, 2007, 2009). The overall site score describes the rank of computed binding pocket using a linear combination of various terms.

Receptor grid generation

The active site of <span class="Disease">COVID-19 main protease protein was located in region 3 site and the receptor grid files were generated using Grid Generation panel of Glide module. This establishes two cubical boxes having a common centroid to organize the calculations: a larger enclosing and a smaller binding box (Athar et al., 2016).

Ligand preparation and virtual screening

Structure-based virtual screening is of major importance for the computational drug discovery process to speed up drug development. This approach emerged as an important tool in identifying small drug-like molecules through various computational tools, by using knowledge about the target protein, its constitutional information, or by known active compounds for target protein (<span class="Gene">Kar & Roy, 2013). In this process of virtual screening, the selected dataset consists of all compound libraries of the ZINC database in SDF format (Rollinger et al., 2008). The complete virtual screening was performed against the above said databases by the Glide module of Maestro. These database molecules were docked into the binding site of the protein utilizing HTVS protocol to calculate ligand–protein binding affinities. Molecules filtered from HTVS were subjected to <mical">span class="Chemical">SP docking which can dock few tens to millions of ligands with high accuracy. A further precise amount of molecule was then subjected to glide XP docking where further elimination of false positives is carried out by more extensive sampling and advanced scoring resulting in higher enrichment. Based on their Glide Gscore, the top five hit molecules were selected for further investigation.

Binding energy calculation

Molecular mechanics-generalized born surface area (MM-GBSA) method was used for the calculation of binding free energy for the docked complexes (Su et al., 2015). This method used the following equation:

Where solv and SA represent the salvation energy and surface area associated energy and E represents the energy minimized states of ligand-protein used in the calculations. For this calculation, the top-scored ligand–protein complexes were selected with default parameters such as OPLS-2005 force field and VSGB solvent model.

Molecular dynamics simulation

To analyze the structural stability of the <span class="Disease">COVID-19 main protease protein–ligand complexes, molecular dynamics simulations were carried out by using Desmond in the presence of the POPC bilayer membrane (Shekhar et al., 2019). The protein–ligand complex was pre-processed using the protein preparation wizard panel to add missing atoms and refinement of side chains. The solvated model of a complex was prepared by selecting POPC (300 K) as a membrane model. The solvated system was in an orthorhombic box to provide <mical">span class="Gene">SPC solvent model and the system was neutralized using Na+ and Cl− ions. The salt concentration was set as 0.15 M to maintain physiological conditions. To minimize the short range bad contacts, energy minimization was carried out using the hybrid method steepest decent and the limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithms (Guo et al., 2010) for 2000 iterations. Finally, these systems were subjected to 50 ns MD simulation production runs at 300 K temperature and 1 bar as pressure. The model was relaxed before the production system run because it makes a series of predefined minimizations and MD executions. The MD simulations were analyzed in terms of RMSD and RMSF to estimate the variations in the position of the systems in terms of simulation time. Protein–ligand contacts and torsion profiles were also examined during the simulation.

Prediction of bioavailability and synthetic feasibility

In silico bioavailability and the synthetic feasibility of the top-scored molecules were evaluated through the Swiss ADME tool. Consideration of synthetic practicability produces a number, between 1 – for simply synthesized compounds, and 10 – for compounds that are challenging to synthesize.

Pharmacokinetic and bioavailability calculations

The assessment of pharmacokinetic parameters of top-scored molecules obtained from virtual screening protocol is believed to be an essential step of the computational drug discovery process. It is known that the major concern for the failure of drug candidates in clinical trials is poor pharmacokinetics (Venugopal et al., 2020). Hence, the evaluation of pharmacokinetic properties in previous stages of drug discovery reduces the chance of failure. With this objective, the pharmacokinetic properties of the top-scored molecules of <span class="Disease">COVID-19 obtained from the present investigation were predicted using the Qikprop module (Kumar et al., 2018).

Density functional theory calculations

Single point energy calculations using density functional theory were performed using <span class="Species">Jaguar (Bochevarov et al., 2013) to explain ligand-bound protein in electronic level. The structures were optimized using hybrid functional B3LYP parameters with 6-31 G** basis set. Various properties such as electrostatic potential, average local ionization energy, gas-phase energy, canonical orbital, etc. were calculated. The positions of lowest unoccupied molecular orbital (<mical">span class="Chemical">LUMO)–highest occupied molecular orbital (HOMO) orbitals of selected hit molecules were analyzed to evaluate the binding pattern at the quantum level.

Results and discussion

Present computational work aimed to search novel, potential inhibitors of <span class="Disease">COVID-19, and characterize the binding pattern to identify the structural features through structure-based screening and molecular dynamics simulation studies. Subsequent in silico bioavailability and synthetic feasibility was also evaluated to strengthen the scope of work.

Analysis of active site identification

The localization and identification of the binding site are necessary as the function and structure of the protein are related to specific interaction with binding site functionalities before structure-based drug design. For predicting the binding site, a comprehensive search was done by SiteMap. It indicates the locations of <span class="Chemical">hydrogen-bonded, van der Waals, and hydrophobic regions in the representative <mical">span class="Disease">COVID-19 proteins. The calculated structural features of binding sites have shown in Table 1. The druggability of binding sites was determined which is given in the terms of site score. In all possible binding sites, site_1 has 0.961 site score along with 274.743 as volume. Same as the site score of other site_2, site_3, site_4, and site_5 were found to be 0.946, 0.837, 0.591, and 0.571 respectively. Based on the results, site_1 has the potential to mechanistically justify the binding pattern and answer for the protein–ligand interactions. Therefore, site_1 was considered as an appropriate binding site to perform virtual screening

Table 1.

Summary of structural features of the binding site predicted by SiteMap.

Sitemap_site	Site_1	Site_2	Site_3	Site_4	Site_5
SiteScore	0.961	0.946	0.837	0.591	0.571
Dscore	0.986	0.993	0.720	0.547	0.407
Volume	274.743	299.782	160.181	50.764	55.909
Exposure	0.605	0.741	0.557	0.652	0.667
Enclosure	0.642	0.588	0.745	0.574	0.616
Contact	0.851	0.777	1.089	0.922	0.873
Don/acc	1.056	0.983	0.790	1.748	0.767

<span class="Disease">COVID-19 main protease protein composed of three regions of 306 amino acid residues. The first (blue), second (green), and third (orange) regions contain 8–101, 102–184, and 201–305 residues, ren class="Gene">mical">spectively. The potential binding site lies between <mical">span class="Gene">region 2 and 3 that is connected through a long loop (Figure 2).

Figure 2.

Predicted binding pocket where virtual screening was performed.

Virtual screening and interaction analysis of complex

Small molecules of the ZINC database have been used for structure-based screening of <span class="Disease">COVID-19 main protease protein. The active site with the best site score (top-ranked potential receptor binding cavity) was taken as a prerequisite for receptor grid generation. The executed approach of virtual screening was the hierarchal mode of elimination technique i.e. HTVS followed by <mical">span class="Chemical">SP and further the XP docking. Glide XP combines accurately energy-based scoring terms and thorough sampling that resulted in compounds with docking scores between −7.524 and −6.711 kcal/mol indicating strong ligand-protein interactions. Final shortlisting of hit molecules was based on visual observation of the major amino acid residues involved in the binding that included hydrogen bonding with Glu166, Phe140, Gly143, Leu141, Asn142, Thr26 and Thr190. From these observations, we selected the top five compounds, the docking score, binding free energies of hit molecules are provided in Table 2.

Table 2.

Docking score and binding free energies of selected five-hit molecules.

Ligand No.	Docking score	dG Bind	dG Bind Coulomb	dG Bind Lipo	dG Bind Hbond	dG Bind vdW
ZINC13144609	−7.524	−38.461	−1.285	−18.163	−1.478	−33.972
ZINC01581128	−7.429	−69.390	−70.280	−34.362	−2.880	−35.765
ZINC06062920	−6.994	−62.967	−54.608	−34.027	−2.650	−40.284
ZINC03022586	−6.947	−68.341	7.655	−24.258	−1.830	−37.597
ZINC00134422	−6.711	−61.889	−23.221	−22.414	−1.492	−33.074

The top hit molecule <span class="Chemical">ZINC13144609 (<mical">span class="Chemical">1,4-bis(1H-benzo[d]imidazol-2-yl)butane-1,2,3,4-tetraol) showed hydrogen bonding with amino acid residue Glu166, Leu141 and Asn142 with a docking score of −7.524 kcal/mol. The selected five potential hit molecules in the binding site of protease protein, interacting with amino acid residues Phe140, Gly143, Thr26, Thr190, Glu166, Pro168, Met165 and Leu141 with a docking score of −7.524 and −6.711 kcal/mol. The number of hydrogen bond along with residue and other interactions is tabulated in Table 3. All hit molecules showed hydrogen–bonding interactions with main amino acid residues Phe140, Leu141, Gly143, Asn142, Thr26, Glu166 and Thr190 with a well-fitted pose within the binding site in the hydrophobic pocket formed by Phe140, Cys145, Cys44, Met49, Leu167, Pro168, Met165, Val42, Leu27 and Ala191. The binding pattern within the binding site pocket of all top-scored hit molecules was quite same and additional π-π interactions contributed for the binding affinity of the molecules.

Table 3.

Top five hit molecules with their number of interacting hydrogen bonds, residues and other interactions.

Ligand No.	Number of hydrogen bond		H-bond interaction		Other interactions
	Backbone	Sidechain	Backbone	Sidechain
ZINC13144609	3	3	Glu166, Leu141	Glu166, Asn142	–
ZINC01581128	5	1	Gly143, Leu141, Phe140, Glu166	Glu166	Π–Π stacking
ZINC06062920	4	1	Gly143, Phe140, Glu166	Glu166	Π–Π stacking
ZINC03022586	5	1	Thr26, Gly143, Thr190, Glu166	Gln192	–
ZINC00134422	4	–	Thr26, Gly143, Glu166	–	–

Top five hit molecules with their number of interacting <span class="Chemical">hydrogen bonds, residues and other interactions.

The docking pattern of the docked hit molecule <span class="Chemical">ZINC13144609 and <mical">span class="Chemical">ZINC01581128 is represented in Figures 3 and 4. The docking orientation of the remaining hit molecules ZINC06062920, ZINC03022586 and ZINC00134422 are depicted in Figures S1–S3.

Figure 3.

COVID-19 main protease protein and ZINC13144609 binding interaction diagram.

Figure 4.

COVID-19 main protease protein and ZINC01581128 binding interaction diagram.

<span class="Disease">COVID-19 main protease protein and <mical">span class="Chemical">ZINC13144609 binding interaction diagram.

<span class="Disease">COVID-19 main protease protein and <mical">span class="Chemical">ZINC01581128 binding interaction diagram.

Molecular dynamics simulation analysis

A molecular dynamics simulation time step involves a computationally intensive force calculation for each atom of a chemical system, followed by a less expensive integration step that advances the positions of the atoms according to classical laws of motion. It enables the atomic-level characterization of numerous biomolecular processes such as investigation of the stability of protein–ligand interactions associated with activation and deactivation of various molecular pathways. The atomistic MD simulation was performed to obtain insights into the stability behavior of top hit molecules <span class="Chemical">ZINC13144609 and <mical">span class="Chemical">ZINC01581128 at the binding pocket of 6Y84. The complex was embedded within the POPC bilayer chosen with default parameters and simulated for 50 ns. Each complex was soaked in an orthorhombic box with SPC water molecules. To maintain a neutral system, Na+ and Cl− ions were added with 0.15 M salt concentration. The trajectories generated were analyzed by plotting the RMSD, RMSF of each frame as a function of simulation time.

For hit molecule <span class="Chemical">ZINC13144609, the RMSD value of the protein Cα was found to increase up to a value of 2.6 Å with ren class="Gene">mical">spect to its starting coordinate (t = 0) for first 10 ns and stabilize around an average value of 2.347 Å for rest of the MD trajectories which indicates no change in the protein backbone. Further, the <mical">span class="Gene">RMSF of the backbone at the binding site of 6Y84 is found to be in the range of 1.0 Å to 2.573 Å which suggests lower degree of flexibility in binding region. From the above observation, it is proved that the ligand movement was stable during the simulation. The ligand–protein contacts are illustrated in Figure 5. It is found that the hydrogen bonds with Glu166 and hydrophobic interactions with Pro168, Leu167, Met 49, His41are major contributing factor for stabilizing hit molecule ZINC13144609 at the binding site which is in accordance with our docking result. His41 found to exhibit π-π stacking with the benzene ring for 61% of the trajectory. Among the six-hydrogen bond predicted by docking, only four are found to be preserved during the simulation.

Figure 5.

RMSD, RMSF and residue interaction plots of 6Y84 protein complexed with ZINC13144609.

RMSD, <span class="Gene">RMSF and residue interaction plots of 6Y84 protein complexed with <mical">span class="Chemical">ZINC13144609.

For hit molecule <span class="Chemical">ZINC01581128, the RMSD and RNSF plots are given in Figure 6. The analysis of 6Y84 protein-<mical">span class="Chemical">ZINC01581128 complex showed low RMSD value, 2.586 Å indicating the stability of the atomistic molecular system. The plot showed that the system attained stability after 20 ns of simulations. The RMSF value is found to be in the range of 1.2–3.0 Å which indicates low fluctuations in the binding site. The hydrogen-bonding interactions with Gly143, Phe140 and Glu166 were retained throughout the simulations. It suggests the importance of these interactions in stabilizing the simulated complex.

Figure 6.

The protein RMSD, RMSF and protein–ligand contacts diagram of complex 6Y84 with ZINC01581128.

The protein RMSD, <span class="Gene">RMSF and protein–ligand contacts diagram of complex 6Y84 with <mical">span class="Chemical">ZINC01581128.

From the above results, it is clear that the protein interacting with both hit molecules is stabilized in the chosen POPC bilayer membrane. The top hit molecules are stabilized through, <span class="Chemical">hydrogen bonding, hydrophobic and π–π interactions with various residues indicate its better stabilization in 6Y84 protein.

Pharmacokinetic parameters and bioavailability prediction

In the drug discovery process, the molecules under consideration need to possess good pharmacokinetic profiles as well as potency to confirm their bioavailability and effectiveness. The various physicochemical and therapeutic significant descriptors were calculated and documented in Tables 4 and 5, respectively.

Table 4.

Summary of physicochemical properties of top five hit molecules.

Ligand no.	MW	Dipole	donorHB	accptHB	Volume	Rotor	PSA	Rule of five
ZINC13144609	354.365	9.895	6	9.8	1067.801	9	131.487	1
ZINC01581128	411.46	4.735	5	7.7	1241.312	13	148.557	1
ZINC06062920	411.46	7.793	5	7.95	1242.611	12	159.536	1
ZINC03022586	426.458	4.967	4	14.4	1215.403	10	178.68	0
ZINC00134422	304.259	3.251	2	9.4	913.267	6	167.007	0
Rangea	130.0–725.0	1.0–12.5	1.0–6.0	2.0–20.0	500.0–2000.0	0–15	7.0–200	Max 4

aRange: for 95% oral drugs.

Table 5.

Therapeutic significant properties of top five hit molecules.

Ligand No.	CNS	QPlogPo/w	QPlogS	QPlogBB	QPPMDCK	Metab	QPlogKhsa
ZINC13144609	−2	0.421	−2.727	−2.005	44.317	4	−0.693
ZINC01581128	−2	0.689	−1.188	−1.896	1.152	9	−0.232
ZINC06062920	−2	0.457	−1.421	−2.09	0.729	8	−0.23
ZINC03022586	−2	−1.301	−2.892	−3.745	1.987	2	−1.097
ZINC00134422	−2	−0.8	−2.191	−2.579	5.219	2	−0.799
Rangea	−2 to +2	−2 to 6.5	−6.5 to 0.5	−3.0 to 1.2	<25 p >500 g	1–8	−1.5 to 1.5

aRange: for 95% oral drugs.

All the calculated physicochemical and therapeutic significant properties were found to be in their permissible range and therefore confirming their drug-likeness.

The bioavailability of top scored hit molecules was determined through SwissADME tool. The bioavailability radar chart of <span class="Chemical">ZINC13144609 and <mical">span class="Chemical">ZINC01581128 is illustrated in Figure 7. The pink area shown in Figure 7 represents most favorable area for each property like INSATU (unsaturation), INSOLU (insolubility), FLEX (rotatable bonds), LIPO (lipophilicity), SIZE (molecular weight) and POLAR (polar surface area). The predicted bioavailability score is tabulated in Table 6.

Figure 7.

The bioavailability radar chart for top hit molecule (A) ZINC13144609 (B) ZINC01581128.

Table 6.

Synthetic accessibility and bioavailability score of top screened molecules.

Ligand No.	Hit structures	Bioavailability score	Synthetic accessibility score
ZINC131446091,4-bis(1H-benzo[d]imidazol-2-yl)butane-1,2,3,4-tetraol		0.55	3.79
ZINC015811283-((7,10-dihydroxy-2-(2-((2-hydroxyethyl)ammonio)ethyl)-6-oxo-2,6-dihydrodibenzo[cd,g]indazol-5-yl)amino)propan-1-aminium		0.55	3.26
ZINC060629203-((6-hydroxy-2-(2-((2-hydroxyethyl)ammonio)ethyl)-7,10-dioxo-1,2,6,7,10,10b-hexahydrodibenzo[cd,g]indazol-5-yl)amino)propan-1-aminium		0.55	3.57
ZINC03022586N2,N7-bis(2-hydroxyethyl)-9H-fluorene-2,7-disulfonamide		0.55	3.19
ZINC001344222-(2-hydroxyethyl)-6-propylpyrrolo[3,4-f]isoindole-1,3,5,7(2H,6H)-tetraone		0.55	2.15

The bioavailability radar chart for top hit molecule (A) <span class="Chemical">ZINC13144609 (B) <mical">span class="Chemical">ZINC01581128.

Estimation of synthetic feasibility

The synthetic feasibility of top scored molecules is shown in Table 6. The synthetic feasibility score is in the range of 1 for simply synthesizable and 10 for difficult to synthesize. All top hit molecules showed synthetic feasibility score around 3.0 that indicates all are easily synthesizable. Furthermore, we forward to plan future synthesis with the best plausible route.

Quantum mechanical calculations

The hit molecule <span class="Chemical">ZINC13144609 and <mical">span class="Chemical">ZINC01581128 were chosen for energy calculations. The HOMO–LUMO energy gap represents the chemical reactivity of molecules. The electronic properties of screened hit molecules were shown in Table 7.

Table 7.

Summary of calculated electronic properties of hit molecules.

Electronic properties	ZINC13144609	ZINC01581128
Number of canonical orbitals	478	581
Gas phase energy	−1216.649911	−1390.606133
HOMO	−0.21684	−0.34043
LUMO	−0.022889	−0.23834
Electrostatic potential mean (kcal/mol)	−0.17	116.53
Average local ionization energy mean (kcal/mol)	261.64	400.01

The position of HOMO-<span class="Chemical">LUMO orbitals of <mical">span class="Chemical">ZINC13144609 (Figure 8) suggests that the hydrophobic region of molecule alters the topology of HOMO-LUMO orbitals and also energies are localized. HOMO and LUMO covers mainly benzimidazole ring of hit molecule. The ZINC01581128 molecule found to have LUMO orbital over hydrophobic region and HOMO orbital mainly cover polar region (Figure 9). Therefore, the presence of HOMO and LUMO orbitals near the hydrophobic part of the hit molecules is important to from stable ligand–protein complex.

Figure 8.

Surface maps for DFT results for ZINC13144609: (A) HOMO map, (B) LUMO map, (C) Interaction strength map and (D) Electrostatic potential map.

Figure 9.

Surface maps for DFT results for ZINC01581128: (A) HOMO map, (B) LUMO map, (C) Interaction strength map and (D) Electrostatic potential map.

Surface maps for DFT results for <span class="Chemical">ZINC13144609: (A) HOMO map, (B) <mical">span class="Chemical">LUMO map, (C) Interaction strength map and (D) Electrostatic potential map.

Surface maps for DFT results for <span class="Chemical">ZINC01581128: (A) HOMO map, (B) <mical">span class="Chemical">LUMO map, (C) Interaction strength map and (D) Electrostatic potential map.

Conclusions

In the present investigation, structure-based virtual screening, DFT calculation, MM/GBSA, pharmacokinetic parameters calculation, and molecular dynamics simulation studies in search of the antiviral drug against <span class="Disease">COVID-19 were performed. The <mical">span class="Disease">COVID-19 main protease protein has made up of three regions of 306 amino acid residues. The predicted binding site lies between regions 2 and 3. Virtual screening was executed through a hierarchal mode of elimination technique in the prerequisite binding site. Further, the top five hit molecules were shortlisted based on docking score and non-bonding interactions. The reliability of docking analysis was confirmed by the low RMSD value of docked ligand and protein. The highest scoring hit molecule ZINC13144609 (−7.524 kcal/mol) exhibited six hydrogen bonds with amino acid residues Glu166, Leu141 and Asn142. The second hit molecule ZINC01581128 showed a docking score −7.429 kcal/mol with six hydrogen bonding (Gly143, Leu141, Phe140 and Glu166) and Π–Π stacking interactions. The docking study suggested that the polar part of the molecules forms hydrogen bonding network. It is evident from the MD simulation study that the ligand–protein complexes have equilibrated and changes were perfectly acceptable for small, globular proteins. The LUMO–HOMO orbital study described the interaction pattern of hits with the protein at the quantum level. The pharmacokinetic properties calculation of top scored hits obtained from present work ensured their safe administration in the human body. Lastly, all top-scored hits can be easily synthesizable based on the least synthetic accessibility score. Therefore, investigated hits could be potent and efficacious drugs targeting COVID-19 main protease protein for the COVID-19 treatment. Experimentally, the need to validate the molecular modeling results reported here with in vitro and/or in vivo inhibition evaluation is acknowledged but due to lack of funding, work is limited. Therefore, investigated inhibitors could be potent and efficacious drugs targeting COVID-19 main protease protein for the COVID-19 treatment.

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