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Research Article
Natural compounds as potential inhibitors of novel coronavirus (COVID-19) main protease: An in silico study
Vishwas Tripathi
Gautam Buddha University, Greater Noida, India
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
COVID-19 pandemic has now expanded over 213 nations across the world. To date, there is no specific medication available for SARS CoV-2 infection. The main protease (Mpro) of SARS CoV-2 plays a crucial role in viral replication and transcription and thereby considered as an attractive drug target for the inhibition of COVID-19,. Natural compounds are widely recognised as valuabe source of antiviral drugs due to their structural diversity and safety. In the current study, we have screened twenty natural compounds having antiviral properties to discover the potential inhibitor molecules against Mpro of COVID-19. Systematic molecular docking analysis was conducted using AuroDock 4.2 to determine the binding affinities and interactions between natural compounds and the Mpro. Out of twenty molecules, four natural metabolites namely, amentoflavone, guggulsterone, puerarin, and piperine were found to have strong interaction with Mpro of COVID-19 based on the docking analysis. These selected natural compounds were further validated using combination of molecular dynamic simulations and molecular mechanic/generalized/Born/Poisson-Boltzmann surface area (MM/G/P/BSA) free energy calculations. During MD simulations, all four natural compounds bound to Mpro on 50ns and MM/G/P/BSA free energy calculations showed that all four shortlisted ligands have stable and favourable energies causing strong binding with binding site of Mpro protein. These four natural compounds have passed the Absorption, Distribution, Metabolism, and Excretion (ADME) property as well as Lipinski’s rule of five. Our promising findings based on in-silico studies warrant further clinical trials in order to use these natural compounds as potential inhibitors of Mpro protein of COVID.
Keywords
SARS-CoV-2, COVID19, Anti-viral, Natural compounds, Mpro, novel coronavirus
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
|
S.N. |
Compound Name |
Mol. Weight (g/mol) |
Concensus Log Po/w |
Num. H-bond acceptors |
Num. H-bond donors |
Molar Refractivity |
Lipinski |
Veber |
Bioavailability score |
Synthetic accessibility (SA) |
TPSA (Ų) |
No of rotatable bonds |
Solubility (mg/ml) |
|
1 |
Ritonavir |
720.94 |
5.04 |
7 |
4 |
197.82 |
No |
No |
0.17 |
6.45 |
202.26 |
22 |
6.87e-08 |
|
2 |
Lopinavir |
628.8 |
4.37 |
5 |
4 |
187.92 |
Yes |
No |
0.55 |
5.67 |
120.00 |
17 |
5.57e-08 |
|
3 |
Herbacetin |
302.24 |
1.33 |
7 |
5 |
78.03 |
Yes |
Yes |
0.55 |
3.2 |
131.36 |
1 |
1.73e-01 |
|
4 |
Rhoifolin |
578.52 |
-0.66 |
14 |
8 |
137.33 |
No |
No |
0.17 |
6.33 |
228.97 |
6 |
1.92e+01 |
|
5 |
Guggulsterone |
312.45 |
4.03 |
2 |
0 |
93.54 |
Yes |
Yes |
0.55 |
4.79 |
34.14 |
0 |
8.14e-03 |
|
6 |
Cyanidin-3-o-galactoside |
449.38 |
-1.16 |
11 |
8 |
108.29 |
No |
No |
0.17 |
5.27 |
193.44 |
4 |
5.23e+01 |
|
7 |
Xanthohumol |
354.40 |
3.76 |
5 |
3 |
102.53 |
Yes |
Yes |
0.55 |
3.16 |
86.99 |
6 |
9.26e-03 |
|
8 |
Phloretin |
274.27 |
1.93 |
5 |
4 |
74.02 |
Yes |
Yes |
0.55 |
1.88 |
97.99 |
4 |
1.16e-01 |
|
9 |
Crocetin |
328.40 |
4.21 |
4 |
2 |
98.48 |
Yes |
Yes |
0.56 |
3.99 |
74.60 |
8 |
5.44e+01 |
|
10 |
Pectolinarin |
622.57 |
-0.43 |
15 |
7 |
148.29 |
No |
No |
0.17 |
6.63 |
227.20 |
8 |
3.48e+00 |
|
11 |
Apigenin |
270.24 |
2.11 |
5 |
3 |
73.99 |
Yes |
Yes |
0.55 |
2.96 |
90.90 |
1 |
1.07e-02 |
|
12 |
Luteolin |
286.24 |
1.73 |
6 |
4 |
76.01 |
Yes |
Yes |
0.55 |
3.02 |
111.13 |
1 |
4.29e-02 |
|
13 |
Amentoflavone |
538.46 |
3.62 |
10 |
6 |
146.97 |
No |
No |
0.17 |
4.27 |
181.80 |
3 |
1.07e-06 |
|
14 |
Daidzein |
254.24 |
2.24 |
4 |
2 |
71.97 |
Yes |
Yes |
0.55 |
2.79 |
70.67 |
1 |
2.64e-03 |
|
15 |
Puerarin |
416.38 |
0.27 |
9 |
6 |
104.59 |
Yes |
No |
4.98 |
0.55 |
160.82 |
3 |
4.49e-01 |
|
16 |
Epigallocatechin |
306.27 |
0.42 |
7 |
6 |
76.36 |
Yes |
Yes |
0.55 |
3.53 |
130.61 |
1 |
8.42e+00 |
|
17 |
Resveratrol |
228.24 |
2.48 |
3 |
3 |
67.88 |
Yes |
Yes |
0.55 |
2.02 |
60.69 |
2 |
1.18e-01 |
|
18 |
Maslinic acid |
472.70 |
5.24 |
4 |
3 |
137.82 |
Yes |
Yes |
0.56 |
6.22 |
77.76 |
1 |
2.37e-03 |
|
19 |
Piperine |
285.34 |
3.04 |
3 |
0 |
85.47 |
Yes |
Yes |
0.55 |
2.92 |
38.77 |
4 |
2.87e-01 |
|
20 |
Ganomycin B |
344.44 |
4.44 |
4 |
3 |
103.1 |
Yes |
Yes |
0.56 |
3.13 |
77.76 |
9 |
3.13e-02 |
Table 1. List of compounds with suitable ADME properties
- 3.3 Target prediction
00The target prediction analysis was displayed for our two best compounds, Amentoflavone and Guggulsterone on the web page with the following observations the top 15 of the results were given as a pie-chart (Figure 2). The pie chart for Amentoflavone predicts 20% of Family AG protein-coupled receptor, 13.3% Kinase, 13.3% of Enzymes, 13.3% of unclassified protein, 6.7% of Phosphatase, 6.7% of protease, 6.7% of Oxidoreductase, 6.7% of primary active transporter, 6.7% of Secreted protein, 6.7% of Ligand-gated ion channel. The pie chart for Guggulsterone predicts 40% of Nuclear Receptors, 13.3% of CytochromE P450, 13.3% of Secreted protein, 13.3% of Oxidoreductase, 6.7% of Membrane receptors, 6.7% of Fatty acid-binding protein family, 6.7% of Enzymes. The output table consisting of Target, Common Name, Uniprot ID, ChEMBL-ID, Target Class, Probability, and Known actives in 2D/3D are given in the Supplementary material. The possible sites of the target which the compound may bind to are mostly the targets which are predicted by the software and the probability score for Amentoflavone and Guggulsterone are obtained from 1.0 to 0.0868 & 1.0 to 0.101672 respectively. This makes an inference that the small compound may have high target attraction towards the specific binding site it is directed to.
- 3.4 Molecular docking
Molecular docking is an extensively used in-silico way to predict protein-ligand interaction. To perform the docking analysis, the structures and amino acids found in the active site pockets of 6LU7. 6LU7 is the main protease (Mpro) found in COVID–19, which has been structured and repositioned in PDB databank. Thereafter, Ligand-protein docking was performed, and the interactions were determined based on the binding affinity of our compounds. Each individual analysis gave positive results, suggesting that the selected natural compounds may directly inhibit COVID–19 main protease Mpro. The 14 selected natural compounds were docked with COVID–19 main protease Mpro along with the standard ritonavir and lopinavir to compare the results. Further, like previous other findings, our results also indicated a good binding affinity of ritonavir and lopinavir to the COVID–19 main protease Mpro.The results obtained are as follows:
Due to technical limitations, Table 2 cannot be displayed in the text. Please find Table 2 in the supplemental file section.
Table 2. Shows the molecular docking analysis results for selected natural compounds against COVID–19 main protease Mpro (PDB–6LU7).
Figure 3 and Figure 4 can be found in the figures section.
Due to technical limitations, Table 3 cannot be displayed in the text. Please find Table 3 in the supplemental file section.
Table 3. Docking analysis visualization of COVID–19 main protease Mpro (PDB–6LU7) binding with Lopinavir*, Ritonavir*, Amentoflavone, Guggulsterone, Puerarin, Piperine, Maslinic acid, Apigenin, Epigallocatechin, Daidzein, Xanthohumol, Resveratrol, Luteolin, Cyanidin–3-o-galactoside, Pectolinarin, Herbacetin, Rhoifolin, Ganomycin B, Phloretin, and Crocetin. The 3D structures of protein-ligand interactions were visualized by discovery studio programs. The binding residues and their chains were identified from the protein-ligand complex as shown in the above images.
- 3.5 Molecular dynamics (MD) simulations
To further investigate the molecular docking results, the top four natural compound complexes namely, amentoflavone, guggulsterone, puerarin, and piperine were subjected to 50ns MD simulations. The conformational stability and flexibility of the complexes have been analyzed by using various parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent accessible surface area (SASA), radius of gyration (Rg), and binding affinity of phytomolecule complexes by using mmpbsa and hydrogen bond formation ability. The RMSD is a commonly used similarity tool to measure the conformational perturbation during the simulation of macromolecule structures. RMSD of the Cα Atoms related to the stability of the complexes. The time dependent RMSD from the initial stage of the simulation to 50 ns simulation. The RMSD of the backbone of these 4 complexes lies between 0.231–0.50 nm, which stabilizes at the 35 ns whereas the RMSD of ligands ranged from 0.35–0.96 nm (Figure 5a). RMSD of the protein backbone of all system was small and comparable, which may conclude that the binding of ligands does not lead to the confirmation perturbation during the simulation (Figure 5b). During MD simulations, RMSF define the residual flexibility from the average position. The RMSF of the protein ranged from 0.2–0.4 nm of all systems (Figure 5c). Some amino acid shows the high-intensity pick, which may represent a loop region. The presence of low-intensity pick revealed that binding of the phytomolecules does not affect the stability of the structural region of the enzyme.
In MD simulations, Rg determines the compactness of protein, induced by the movement of a ligand. The lower the flexibility of the Rg during the simulation associated with the structural stability of the protein. The Rg values of all phytochemical’s complexes were lies 2.20–2.05 nm (Figure 5d). The Rg values of all four phytochemicals complexes support their consensus architecture as well as size. The SASA associated with the exposures of the hydrophobic residue during the simulation. SASA plays a principal role in the van der interaction. The SASA values of all systems were lies between 125–150 nm2. SASA confirmations showed that the binding of ligand molecules does not affect the overall folding of the protein (Figure 6a).
In a complex protein and ligand, hydrogen bonding plays a critical role to determine the strength of interaction. During the simulation time, several hydrogen bonds formed between the donor and the acceptor group (Figure 6c). Two hydrogen bonds consistently formed during the time of simulation (Figure 6b). Over all observations indicated that all four complexes are stable during simulation.
- 3.6 Binding free energy calculation: In recent reports, it has been demonstrated that 100–200 snapshots are enough to calculate the binding free energies (Khan et al., 2020)(Kumar et al., 2020)(Sarma et al., 2020). The free binding energy results showed that as compare to other molecules Amentoflavens has maximum binding energy –240.434±17.602–180.787 kJ/mol whereas Guggulsterone has lower binding energy –121.708±12.423 kJ/mol (Table 5). In each molecule, the polar salvation and SASA energy showed moderate effects on binding energy component.
|
Name of molecules |
Van der Waal energy (K.J./mol) |
Electrostatic energy (K.J./mol) |
Polar solvation energy (K.J./mol) |
SASA energy (K.J./mol) |
Binding energy (kJ/mol) |
|
Amentoflavone |
-350.08217.177 |
-104.69619.160 |
239.90621.251 |
-25.5621.010 |
-240.43417.602-180.787 |
|
Guggulsterone |
-140.06815.668 |
-7.4089.330 |
37.62111.572 |
-11.8531.070 |
-121.70812.423 |
|
Piperine |
-173.54511.759 |
-9.3734.129 |
50.5737.610 |
-13.9431.293 |
-146.287 11.205 |
|
Puerarin |
-180.78720.912 |
-82.40516.508 |
148.20017.298 |
-16.6391.417 |
-131.63120.483 |
Table 4. Binding free energy calculation of four stable complexes during simulation
- 3.7 Toxicity analysis
In-silico toxicities of selected natural compounds were predicted by using ProTox-II. As shown in Table 4, ProTox-II toxicity prediction was done to check the safety of the compounds based on two major toxicity end points, hepatotoxicity & cytotoxicity. According to the toxicity analysis, none of the selected natural compounds showed potential hepatotoxicity or cytotoxicity except Pectolinarin which showed potential cytotoxicity.
Due to technical limitations, Table 5 cannot be displayed in the text. Please find Table 5 in the supplemental file section.
Table 5: Toxicity predictions for selected natural compounds
- 3.8 Similarity checks with FDA approved drugs using SWISS similarity
We further checked the similarity of our two top hit natural compounds Amentoflavone and Guggulsterone if any, with the FDA approved drugs using SWISS similarity check. Swiss Similarity web tool is used for rapid ligand-based virtual screening. For Amentoflavone, we did not find any reported similar FDA approved drug in Swiss Similarity database. Whereas for Guggulsterone, we found 117 FDA proved drugs. The output table consisting of Drug ID, Drug name, Similarity score and molecule structure are given in the Supplementary material. The FDA approved drug structure obtained with the similar structure of Guggulsterone which are predicted by the software and the probability score for obtained from 0.995 to 0.009. This makes an inference that that these compounds could be very important and unique with pharmaceutical perspectives and need to be explored at in vitro and subsequent pre-clinical and clinical trials.
Due to technical limitations, Table 6 cannot be displayed in the text. Please find Table 6 in the supplemental file section.
Table 6: Swiss similarity prediction of Amentoflavone and Guggulsterone compounds
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Research Article
Natural compounds as potential inhibitors of novel coronavirus (COVID-19) main protease: An in silico study
Vishwas Tripathi
Gautam Buddha University, Greater Noida, India
Corresponding Author
Badges
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This manuscript passed our Prescreen assessment
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License:
This work is licensed under a CC BY 4.0 License. Read Full License
COVID-19 pandemic has now expanded over 213 nations across the world. To date, there is no specific medication available for SARS CoV-2 infection. The main protease (Mpro) of SARS CoV-2 plays a crucial role in viral replication and transcription and thereby considered as an attractive drug target for the inhibition of COVID-19,. Natural compounds are widely recognised as valuabe source of antiviral drugs due to their structural diversity and safety. In the current study, we have screened twenty natural compounds having antiviral properties to discover the potential inhibitor molecules against Mpro of COVID-19. Systematic molecular docking analysis was conducted using AuroDock 4.2 to determine the binding affinities and interactions between natural compounds and the Mpro. Out of twenty molecules, four natural metabolites namely, amentoflavone, guggulsterone, puerarin, and piperine were found to have strong interaction with Mpro of COVID-19 based on the docking analysis. These selected natural compounds were further validated using combination of molecular dynamic simulations and molecular mechanic/generalized/Born/Poisson-Boltzmann surface area (MM/G/P/BSA) free energy calculations. During MD simulations, all four natural compounds bound to Mpro on 50ns and MM/G/P/BSA free energy calculations showed that all four shortlisted ligands have stable and favourable energies causing strong binding with binding site of Mpro protein. These four natural compounds have passed the Absorption, Distribution, Metabolism, and Excretion (ADME) property as well as Lipinski’s rule of five. Our promising findings based on in-silico studies warrant further clinical trials in order to use these natural compounds as potential inhibitors of Mpro protein of COVID.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
|
S.N. |
Compound Name |
Mol. Weight (g/mol) |
Concensus Log Po/w |
Num. H-bond acceptors |
Num. H-bond donors |
Molar Refractivity |
Lipinski |
Veber |
Bioavailability score |
Synthetic accessibility (SA) |
TPSA (Ų) |
No of rotatable bonds |
Solubility (mg/ml) |
|
1 |
Ritonavir |
720.94 |
5.04 |
7 |
4 |
197.82 |
No |
No |
0.17 |
6.45 |
202.26 |
22 |
6.87e-08 |
|
2 |
Lopinavir |
628.8 |
4.37 |
5 |
4 |
187.92 |
Yes |
No |
0.55 |
5.67 |
120.00 |
17 |
5.57e-08 |
|
3 |
Herbacetin |
302.24 |
1.33 |
7 |
5 |
78.03 |
Yes |
Yes |
0.55 |
3.2 |
131.36 |
1 |
1.73e-01 |
|
4 |
Rhoifolin |
578.52 |
-0.66 |
14 |
8 |
137.33 |
No |
No |
0.17 |
6.33 |
228.97 |
6 |
1.92e+01 |
|
5 |
Guggulsterone |
312.45 |
4.03 |
2 |
0 |
93.54 |
Yes |
Yes |
0.55 |
4.79 |
34.14 |
0 |
8.14e-03 |
|
6 |
Cyanidin-3-o-galactoside |
449.38 |
-1.16 |
11 |
8 |
108.29 |
No |
No |
0.17 |
5.27 |
193.44 |
4 |
5.23e+01 |
|
7 |
Xanthohumol |
354.40 |
3.76 |
5 |
3 |
102.53 |
Yes |
Yes |
0.55 |
3.16 |
86.99 |
6 |
9.26e-03 |
|
8 |
Phloretin |
274.27 |
1.93 |
5 |
4 |
74.02 |
Yes |
Yes |
0.55 |
1.88 |
97.99 |
4 |
1.16e-01 |
|
9 |
Crocetin |
328.40 |
4.21 |
4 |
2 |
98.48 |
Yes |
Yes |
0.56 |
3.99 |
74.60 |
8 |
5.44e+01 |
|
10 |
Pectolinarin |
622.57 |
-0.43 |
15 |
7 |
148.29 |
No |
No |
0.17 |
6.63 |
227.20 |
8 |
3.48e+00 |
|
11 |
Apigenin |
270.24 |
2.11 |
5 |
3 |
73.99 |
Yes |
Yes |
0.55 |
2.96 |
90.90 |
1 |
1.07e-02 |
|
12 |
Luteolin |
286.24 |
1.73 |
6 |
4 |
76.01 |
Yes |
Yes |
0.55 |
3.02 |
111.13 |
1 |
4.29e-02 |
|
13 |
Amentoflavone |
538.46 |
3.62 |
10 |
6 |
146.97 |
No |
No |
0.17 |
4.27 |
181.80 |
3 |
1.07e-06 |
|
14 |
Daidzein |
254.24 |
2.24 |
4 |
2 |
71.97 |
Yes |
Yes |
0.55 |
2.79 |
70.67 |
1 |
2.64e-03 |
|
15 |
Puerarin |
416.38 |
0.27 |
9 |
6 |
104.59 |
Yes |
No |
4.98 |
0.55 |
160.82 |
3 |
4.49e-01 |
|
16 |
Epigallocatechin |
306.27 |
0.42 |
7 |
6 |
76.36 |
Yes |
Yes |
0.55 |
3.53 |
130.61 |
1 |
8.42e+00 |
|
17 |
Resveratrol |
228.24 |
2.48 |
3 |
3 |
67.88 |
Yes |
Yes |
0.55 |
2.02 |
60.69 |
2 |
1.18e-01 |
|
18 |
Maslinic acid |
472.70 |
5.24 |
4 |
3 |
137.82 |
Yes |
Yes |
0.56 |
6.22 |
77.76 |
1 |
2.37e-03 |
|
19 |
Piperine |
285.34 |
3.04 |
3 |
0 |
85.47 |
Yes |
Yes |
0.55 |
2.92 |
38.77 |
4 |
2.87e-01 |
|
20 |
Ganomycin B |
344.44 |
4.44 |
4 |
3 |
103.1 |
Yes |
Yes |
0.56 |
3.13 |
77.76 |
9 |
3.13e-02 |
Table 1. List of compounds with suitable ADME properties
- 3.3 Target prediction
00The target prediction analysis was displayed for our two best compounds, Amentoflavone and Guggulsterone on the web page with the following observations the top 15 of the results were given as a pie-chart (Figure 2). The pie chart for Amentoflavone predicts 20% of Family AG protein-coupled receptor, 13.3% Kinase, 13.3% of Enzymes, 13.3% of unclassified protein, 6.7% of Phosphatase, 6.7% of protease, 6.7% of Oxidoreductase, 6.7% of primary active transporter, 6.7% of Secreted protein, 6.7% of Ligand-gated ion channel. The pie chart for Guggulsterone predicts 40% of Nuclear Receptors, 13.3% of CytochromE P450, 13.3% of Secreted protein, 13.3% of Oxidoreductase, 6.7% of Membrane receptors, 6.7% of Fatty acid-binding protein family, 6.7% of Enzymes. The output table consisting of Target, Common Name, Uniprot ID, ChEMBL-ID, Target Class, Probability, and Known actives in 2D/3D are given in the Supplementary material. The possible sites of the target which the compound may bind to are mostly the targets which are predicted by the software and the probability score for Amentoflavone and Guggulsterone are obtained from 1.0 to 0.0868 & 1.0 to 0.101672 respectively. This makes an inference that the small compound may have high target attraction towards the specific binding site it is directed to.
- 3.4 Molecular docking
Molecular docking is an extensively used in-silico way to predict protein-ligand interaction. To perform the docking analysis, the structures and amino acids found in the active site pockets of 6LU7. 6LU7 is the main protease (Mpro) found in COVID–19, which has been structured and repositioned in PDB databank. Thereafter, Ligand-protein docking was performed, and the interactions were determined based on the binding affinity of our compounds. Each individual analysis gave positive results, suggesting that the selected natural compounds may directly inhibit COVID–19 main protease Mpro. The 14 selected natural compounds were docked with COVID–19 main protease Mpro along with the standard ritonavir and lopinavir to compare the results. Further, like previous other findings, our results also indicated a good binding affinity of ritonavir and lopinavir to the COVID–19 main protease Mpro.The results obtained are as follows:
Due to technical limitations, Table 2 cannot be displayed in the text. Please find Table 2 in the supplemental file section.
Table 2. Shows the molecular docking analysis results for selected natural compounds against COVID–19 main protease Mpro (PDB–6LU7).
Figure 3 and Figure 4 can be found in the figures section.
Due to technical limitations, Table 3 cannot be displayed in the text. Please find Table 3 in the supplemental file section.
Table 3. Docking analysis visualization of COVID–19 main protease Mpro (PDB–6LU7) binding with Lopinavir*, Ritonavir*, Amentoflavone, Guggulsterone, Puerarin, Piperine, Maslinic acid, Apigenin, Epigallocatechin, Daidzein, Xanthohumol, Resveratrol, Luteolin, Cyanidin–3-o-galactoside, Pectolinarin, Herbacetin, Rhoifolin, Ganomycin B, Phloretin, and Crocetin. The 3D structures of protein-ligand interactions were visualized by discovery studio programs. The binding residues and their chains were identified from the protein-ligand complex as shown in the above images.
- 3.5 Molecular dynamics (MD) simulations
To further investigate the molecular docking results, the top four natural compound complexes namely, amentoflavone, guggulsterone, puerarin, and piperine were subjected to 50ns MD simulations. The conformational stability and flexibility of the complexes have been analyzed by using various parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent accessible surface area (SASA), radius of gyration (Rg), and binding affinity of phytomolecule complexes by using mmpbsa and hydrogen bond formation ability. The RMSD is a commonly used similarity tool to measure the conformational perturbation during the simulation of macromolecule structures. RMSD of the Cα Atoms related to the stability of the complexes. The time dependent RMSD from the initial stage of the simulation to 50 ns simulation. The RMSD of the backbone of these 4 complexes lies between 0.231–0.50 nm, which stabilizes at the 35 ns whereas the RMSD of ligands ranged from 0.35–0.96 nm (Figure 5a). RMSD of the protein backbone of all system was small and comparable, which may conclude that the binding of ligands does not lead to the confirmation perturbation during the simulation (Figure 5b). During MD simulations, RMSF define the residual flexibility from the average position. The RMSF of the protein ranged from 0.2–0.4 nm of all systems (Figure 5c). Some amino acid shows the high-intensity pick, which may represent a loop region. The presence of low-intensity pick revealed that binding of the phytomolecules does not affect the stability of the structural region of the enzyme.
In MD simulations, Rg determines the compactness of protein, induced by the movement of a ligand. The lower the flexibility of the Rg during the simulation associated with the structural stability of the protein. The Rg values of all phytochemical’s complexes were lies 2.20–2.05 nm (Figure 5d). The Rg values of all four phytochemicals complexes support their consensus architecture as well as size. The SASA associated with the exposures of the hydrophobic residue during the simulation. SASA plays a principal role in the van der interaction. The SASA values of all systems were lies between 125–150 nm2. SASA confirmations showed that the binding of ligand molecules does not affect the overall folding of the protein (Figure 6a).
In a complex protein and ligand, hydrogen bonding plays a critical role to determine the strength of interaction. During the simulation time, several hydrogen bonds formed between the donor and the acceptor group (Figure 6c). Two hydrogen bonds consistently formed during the time of simulation (Figure 6b). Over all observations indicated that all four complexes are stable during simulation.
- 3.6 Binding free energy calculation: In recent reports, it has been demonstrated that 100–200 snapshots are enough to calculate the binding free energies (Khan et al., 2020)(Kumar et al., 2020)(Sarma et al., 2020). The free binding energy results showed that as compare to other molecules Amentoflavens has maximum binding energy –240.434±17.602–180.787 kJ/mol whereas Guggulsterone has lower binding energy –121.708±12.423 kJ/mol (Table 5). In each molecule, the polar salvation and SASA energy showed moderate effects on binding energy component.
|
Name of molecules |
Van der Waal energy (K.J./mol) |
Electrostatic energy (K.J./mol) |
Polar solvation energy (K.J./mol) |
SASA energy (K.J./mol) |
Binding energy (kJ/mol) |
|
Amentoflavone |
-350.08217.177 |
-104.69619.160 |
239.90621.251 |
-25.5621.010 |
-240.43417.602-180.787 |
|
Guggulsterone |
-140.06815.668 |
-7.4089.330 |
37.62111.572 |
-11.8531.070 |
-121.70812.423 |
|
Piperine |
-173.54511.759 |
-9.3734.129 |
50.5737.610 |
-13.9431.293 |
-146.287 11.205 |
|
Puerarin |
-180.78720.912 |
-82.40516.508 |
148.20017.298 |
-16.6391.417 |
-131.63120.483 |
Table 4. Binding free energy calculation of four stable complexes during simulation
- 3.7 Toxicity analysis
In-silico toxicities of selected natural compounds were predicted by using ProTox-II. As shown in Table 4, ProTox-II toxicity prediction was done to check the safety of the compounds based on two major toxicity end points, hepatotoxicity & cytotoxicity. According to the toxicity analysis, none of the selected natural compounds showed potential hepatotoxicity or cytotoxicity except Pectolinarin which showed potential cytotoxicity.
Due to technical limitations, Table 5 cannot be displayed in the text. Please find Table 5 in the supplemental file section.
Table 5: Toxicity predictions for selected natural compounds
- 3.8 Similarity checks with FDA approved drugs using SWISS similarity
We further checked the similarity of our two top hit natural compounds Amentoflavone and Guggulsterone if any, with the FDA approved drugs using SWISS similarity check. Swiss Similarity web tool is used for rapid ligand-based virtual screening. For Amentoflavone, we did not find any reported similar FDA approved drug in Swiss Similarity database. Whereas for Guggulsterone, we found 117 FDA proved drugs. The output table consisting of Drug ID, Drug name, Similarity score and molecule structure are given in the Supplementary material. The FDA approved drug structure obtained with the similar structure of Guggulsterone which are predicted by the software and the probability score for obtained from 0.995 to 0.009. This makes an inference that that these compounds could be very important and unique with pharmaceutical perspectives and need to be explored at in vitro and subsequent pre-clinical and clinical trials.
Due to technical limitations, Table 6 cannot be displayed in the text. Please find Table 6 in the supplemental file section.
Table 6: Swiss similarity prediction of Amentoflavone and Guggulsterone compounds