In-silico drug discovery approaches have been utilized to identify potential natural products (NPs) as Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) inhibitors. The MolPort database that contains over 100,000 NPs was screened and filtered using molecular docking techniques. Based on calculated docking scores, the top 5,000 NPs/natural-like products (NLPs) were selected and subjected to molecular dynamics (MD) simulations followed by molecular mechanics–generalized Born surface area (MM-GBSA) binding energy calculations. Combined 50 ns MD simulations and MM-GBSA calculations revealed nine potent NLPs with binding affinities (ΔGbinding) > −48.0 kcal/mol. Interestingly, among the identified NLPs, four bis([1,3]dioxolo)pyran-5-carboxamide derivatives showed ΔGbinding> −56.0 kcal/mol, forming essential short hydrogen bonds with HIS163 and GLY143 amino acids via dioxolane oxygen atoms. Structural and energetic analyses over 50 ns MD simulation demonstrated NLP-Mpro complex stability. Drug-likeness predictions revealed the prospects of the identified NLPs as potential drug candidates. The findings are expected to provide a novel contribution to the field of COVID-19 drug discovery.
Results and discussion
Unavailability of therapies as yet indicates an urgent need for drug exploration against SARS-CoV-2. The main protease (Mpro) is a likely targeted for enzyme inhibition due to the polypeptides' essential role in viral replication. In this study, the expansive MolPort database for natural products (NPs) and natural-like products (NLPs) was screened and filtered for the identification of potential SARS-CoV-2 Mpro inhibitors.
Two levels of molecular docking calculations were carried out to minimize computational-costs and time. Initially, all NPs/NLPs in the MolPort database were screened against Mpro with standard docking parameters of GA = 25 and eval = 2,500,000. The NPs/NLPs were then sorted based on their docking scores. In terms of the calculated docking scores, almost three-fourths of the screened NPs and NLPs (≈78%) showed binding energies less than −8.7 kcal/mol with SARS-CoV-2 Mpro. Therefore, only the top 25,000 NPs/NLPs were selected for further docking calculations. Using expensive docking parameters of GA = 100 and eval = 10,000,000, the selected 25,000 NPs/NLPs were re-docked against Mpro. Docking scores and binding features, as well as 2 D chemical structures, of nine potent NLPs with SARS-CoV-2 Mpro are listed . The 2 D and 3 D representations of interactions of the top nine potent NLPs with important amino acid residues of SARS-CoV-2 Mpro are depicted
The most promising NLPs (shown in Table 1 and Supporting Information Figure S1) share the same binding mode inside the active site of SARS-CoV-2 Mpro, forming essential hydrogen bonds with key amino acid residues including HIS164, HIS163, and GLU166. Further interactions including van der Waals and hydrophobic interactions were identified, giving a docking score higher than ≥ −9.6 kcal/mol (Supporting Information Figure S1). For instance, the potency of MolPort-000-708-794 with a binding energy of −11.0 kcal/mol may be attributed to its ability to form four hydrogen bonds with GLU166, GLY143, HIS163, and HIS164 with bond lengths of 1.80, 1.72, 2.35 and 1.83 Å, respectively (Figure 1). Two out of the latter four hydrogen bonds were formed via dioxolane oxygen atoms, revealing a significant contribution of dioxolane rings in NLPs-Mpro binding mode and affinity. Based on calculated expensive docking scores, the top 5,000 potent NPs/NLPs were closely investigated using molecular dynamics (MD) calculations.
Figure 1. 2 D and 3 D representation of predicted binding mode of MolPort-000-708-794 as potent natural-like products (NLPs) inside the active site of SARS-CoV-2 main protease (Mpro).
Molecular dynamics simulations
Conformational flexibilities of drug-receptor complexes, solvent effects, and dynamics must be considered to achieve reliable drug-receptor binding affinities (De Vivo et al., 2016; Kerrigan, 2013). Therefore, molecular dynamics (MD) simulations combined with binding energy calculations over reasonable simulation time were performed for the top 5,000 potent NPs/NLPs in complex with SARS-CoV-2 Mpro. Considering computational-costs and time, MD simulations for the 5,000 docked NPs/NLPs-Mpro complexes were conducted in implicit solvent for 100 ps and the corresponding binding energies were estimated using MM-GBSA approach (see Computational Methodology section for details). The calculated MM-GBSA binding energies of the top 1,000 compounds are listed
Interestingly, of the 5,000 investigated NPs/NLPs, only 86 NPs/NLPs showed significant MM-GBSA binding energies with values in range −45.0 to −56.3 kcal/mol. To achieve a greater degree of accuracy, molecular dynamics of 86 NPs/NLPs in complex with Mpro were further investigated over a longer implicit MD simulation with a time of 1 ns. Estimated MD//MM-GBSA binding energies are presented in Supporting Information Table S2. The corresponding MM-GBSA binding energies for nine potent NLPs are presented in Figure 2.
Figure 2. Calculated MM-GBSA binding energies for the top nine potent natural-like products (NLPs) as SARS-CoV-2 main protease (Mpro) inhibitors over 100 ps and 1 ns implicit MD and 10 ns and 50 ns explicit MD simulations
On the basis of the calculated MM-GBSA binding energies over 1 ns (Supporting Information Table S2), 46 NPs/NLPs exhibited instability in molecular dynamics and their corresponding MM-GBSA binding energies with Mpro were decreased to be less than −45.0 kcal/mol, while the other 40 NPs/NLPs showed significant binding affinities with MM-GBSA energies ≥ −45.0 kcal/mol. Therefore, we focused on these potent 40 NPs/NLPs over a reasonable MD simulation time in explicit solvent. Consequently, 10 ns MD simulations for the potent 40 NPs/NLPs-Mpro complexes were conducted in explicit solvent and followed by MM-GBSA binding energies calculation. The estimated 10 ns MD//MM-GBSA binding energies are noted in Supporting Information Table S3, and the corresponding binding energies for nine potent NLPs are presented in Figure 2.
Interestingly, only nine of the investigated NPs/NLPs showed promising binding affinities > −50.0 kcal/mol towards SARS-CoV-2 Mpro over 10 ns MD simulation time (Supporting Information Table S3). To increase the reliability of the observed results, each one of these nine NLPs in complex with Mpro was further simulated for 50 ns MD, and the corresponding binding energies were calculated (Figure 2). As can be seen from Figure 2, there was no significant difference between the estimated MM-GBSA binding energies over 10 ns MD and those over 50 ns MD, reflecting the tight binding of the identified NLPs with SARS-CoV-2 Mpro. Surprisingly, four of the nine NLPs—namely, MolPort-004-849-765, MolPort-000-708-794, MolPort-002-513-915 and MolPort-000-702-646—are bis([1,3]dioxolo)pyran-5-carboxamide derivatives and showed outperformance affinity towards SARS-CoV-2 Mpro with binding energies > −56.0 kcal/mol. Therefore, considerable interest was given in the following sections to investigate the identified dioxolo-derivatives as potential SARS-CoV-2 Mpro inhibitors.
Post-dynamics analyses The purpose of the post-dynamics analyses was to evaluate the interaction nature and stability of the identified dioxolo-derivatives inside the SARS-CoV-2 Mpro active site. Structural and energetic analyses for the four promising dioxolo-derivatives were conducted over 50 ns explicit MD simulations.
Binding energy decomposition Decomposition of average MM-GBSA binding energy over 50 ns MD simulation was performed to reveal the nature of dominant interactions in NLPs-Mpro complexes (Table 2). Energy decomposition results showed that E vdw was the dominant force in NP/NLP-Mpro binding affinities with a contribution value of ≈−63.0 kcal/mol for the four dioxolo-derivatives. E ele was favorable with values of −35.1, −47.1, −34.5 and −43.6 kcal/mol for MolPort-004-849-765, MolPort-000-708-794, MolPort-002-513-915 and MolPort-000-702-646, respectively.
Binding energy per frame The stability of dioxolo-derivatives inside the Mpro active site was investigated using the correlation between the binding-energy and time. Therefore, MM-GBSA binding energy was estimated per-frame for each of MolPort-004-849-765, MolPort-000-708-794, MolPort-002-513-915, and MolPort-000-702-646 with Mpro and presented in Figure 3. According to data in Figure 3, there was overall stability for the four dioxolo-derivatives till the end of the simulations with average values of −58.4, −57.3, −56.6, and −56.2 (kcal/mol), respectively. These findings indicated the promising stability of NLP-Mpro complexes over the simulated MD time of 50 ns.
Figure 3. Variations in the MM-GBSA binding energies for MolPort-004-849-765 (in cyan), MolPort-000-708-794 (in red), MolPort-002-513-915 (in blue) and MolPort-000-702-646, (in black) with SARS-CoV-2 main protease (Mpro) during the 50 ns MD simulation.
Hydrogen bond length and center-of-mass distance
Inspecting the hydrogen bond length and center-of-mass (CoM) distance between the dioxolo-derivatives and the key HIS164 amino acid residue over the 50 ns MD simulation would reflect an indication of NLPs-Mpro stability. Therefore, the desired hydrogen bond lengths and CoM distances were measured over the 50 ns MD simulations and depicted in Figure 4.
Figure 4. Hydrogen bond lengths and center-of-mass (CoM) distances between MolPort-004-849-765 (in cyan), MolPort-000-708-794 (in red), MolPort-002-513-915 (in blue) and MolPort-000-702-646 (in black) with the HIS164 amino acid residue inside the active site of SARS-CoV-2 main protease (Mpro) over the 50 ns MD simulation.
The most obvious finding to emerge from data plotted in Figure 4 was that MolPort-004-849-765, MolPort-000-708-794, MolPort-002-513-915 and MolPort-000-702-646 showed high stability inside the active site with average hydrogen bond lengths of 1.93, 1.98, 1.92, and 1.99 Å, respectively. In terms of the measured CoM distances, the average CoM distance between NLP and HIS164 was nearly constant around 8 Å during 50 ns MD simulations for the four investigated dioxolo-derivatives.
Overall, these post-dynamics results provided evidence for the stability of the identified dioxolo-derivatives in complex with SARS-CoV-2 Mpro, forming hydrogen bond interactions with the key amino acids.
Root-mean-square deviation (RMSD) was used to investigate the structural changes in the NLP-Mpro complexes. RMSD for the backbone atoms of the MolPort-004-849-765, MolPort-000-708-794, MolPort-002-513-915 and MolPort-000-702-646 in complex with Mpro relative to the starting structures throughout the 50 ns MD simulations were evaluated and presented in Figure 5.
Figure 5. Root-mean-square-deviation (RMSD) of the backbone from the initial structure for MolPort-004-849-765 (in cyan), MolPort-000-708-794 (in red), MolPort-002-513-915 (in blue) and MolPort-000-702-646 (in black) with SARS-CoV-2 main protease (Mpro) through 50 ns MD simulation.
From the data in Figure 5, it is apparent that the backbone of NLP-Mpro complexes exhibited stability over 50 ns MD simulation, giving RMSD with less than 0.35 nm. These results emphasize that four investigated dioxolo-derivatives are tightly bonded in the active site and do not impact the overall topology of Mpro. Finally, these energetic and structural analyses demonstrated the high stability of the four investigated dioxolo-derivatives-Mpro complexes through 50 ns MD simulations.
Drug likeness Lipinski’s rule of five is commonly used in drug discovery and development to evaluate the oral bioavailability of active drug in humans. In this study, physicochemical parameters of the promising NLPs as potential SARS-CoV-2 Mpro inhibitors were predicted using Molinspiration cheminformatics, (http://www.molinspiration.com) online software calculation toolkit. The predicted parameters included Lipinski’s parameters, topological polar surface area (TPSA), and percentage of absorption (%ABS). The predicted parameters are listed in Table 3.
From data given in Table 3, the milog P values of the four dioxolo-derivatives were found to be below five (calc. in range 1.43 to 2.38), suggesting that these NLPs have good permeability across the cell membrane. Molecular weight was found to be more or less than 500 (calc. in range 492.52 to 568.63), predicting the compounds to be easily transported, diffused and absorbed. Besides, the number of hydrogen bond donors (nOHNH) was less than 5 in accordance with Lipinski’s rules, and the number of hydrogen bond acceptors (nON) was in range 9 to 13. It is worth mentioning that this slight increase in molecular weight and hydrogen bond acceptors will not have a significant impact on compound transportation and diffusion, where it has been shown that several FDA-approved drugs moved beyond the traditional low molecular weight of 500 and hydrogen bond acceptors of 10 (Mullard, 2018). Besides, TPSA of all promising dioxolo-derivatives was observed in range 104.37 to 163.43 Å which was a very good indicator of the bioavailability of the investigated NLPs. In addition, the calculated %ABS was ranged between 52.62% and 72.99%, indicating that the investigated NLPs may have good cell membrane permeability and oral bioavailability.
Molecular target prediction and network analysis Severe Acute Respiratory Syndrome diseases (C1175175) displayed 117 genes based on DisGeNET online software. Additionally, targeted genes for the identified dioxolo-derivatives as potent Mpro inhibitors were collected using online SwissTargetPrediction tools, giving 100 targets. Classification of the predicted targets for each examined dioxolo-derivative is depicted in Supporting Information Figure S2. The Venn diagram comparison analysis between Severe Acute Respiratory Syndrome diseases and predicted targeted genes was demonstrated and plotted in Figure 6.
Figure 6. Venn diagram analysis for the four identified dioxolo-derivatives as potent SARS-CoV-2 main protease (Mpro) inhibitors and Severe Acute Respiratory Syndrome disease genes.
According to data presented in Venn diagram (Figure 6), Acute Respiratory Syndrome diseases and predicted targeted genes displayed commonly shared MAPK14 and EGFR (Figure 6). The host protein angiotensin-converting enzyme 2 (ACE2), serves as an entry receptor for SARS-CoV-2. Targeting MAPK14 would result in blocking of ACE2 production pathways, and in turn, reducing the probability of SARS-CoV-2 virus to be received and internalized by human cells (Kindrachuk et al., 2015). In addition, SARS-CoV-2 inhibition might be achieved by inhibition of MAPKs which are activated also by GFRs, such as in case of chloroquine (Hondermarck et al., 2020).
The possible targets predicted by SwissTargetPrediction for all examined dioxolo-derivatives were further analyzed using STRING PPI network and visualized by Cytoscape 3.8.0. The network topological analysis by Cytoscape demonstrated that the targets within the top 10 scores of degree were MAPK14 and EGFR for the four examined dioxolo-derivatives (Supporting Information Table S4). The STRING PPI network for the identified top 10 targets is depicted in Figure 7.
Figure 7. The STRING PPI network for the top 10 targets identified by network analyzer for the identified dioxolo-derivatives as potent SARS-CoV-2 main protease (Mpro) inhibitors.
SARS-CoV-2 main protease (Mpro) is characterized to be an essential and highly potent target for the inhibition of the novel coronavirus. In this study, a total of 113,756 natural and natural like products were screened against Mpro to discover potential SARS-CoV-2 Mpro inhibitors. Filtration of MolPort database was carried out using combined molecular docking and molecular dynamics (MD) followed by molecular mechanics–generalized Born surface area (MM-GBSA) binding energy calculations. Based on docking scores and MM-GBSA binding energies, nine NLPs showed promising binding affinities > −50.0 kcal/mol with SARS-CoV-2 Mpro. Interestingly, four of the nine NLPs—namely, MolPort-004-849-765, MolPort-000-708-794, MolPort-002-513-915 and MolPort-000-702-646—are bis([1,3]dioxolo)pyran-5-carboxamide derivatives showing high affinity towards Mpro with binding energies > −56.0 kcal/mol. Post-dynamics analyses demonstrated the stability and affinity of the identified dioxolo-derivatives with Mpro. Predicted physicochemical parameters of the promising dioxolo-derivatives fit drug-likeness properties, indicating the probability of these NLPs as prospective SARS-CoV-2 drug candidates. Protein-protein interaction (PPI) showed the linked top targets genes that could have an effect on viral infection, as well as, the host. The current results establish that bis([1,3]dioxolo)pyran-5-carboxamide derivatives hold promise as inhibitors against SARS-CoV-2 Mpro and are ready for in vitro inhibition against SARS-CoV-2.
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