key: cord-0860019-l9hj6xdw
authors: Rudrapal, Mithun; Gogoi, Neelutpal; Chetia, Dipak; Khan, Johra; Banwas, Saeed; Alshehri, Bader; Alaidarous, Mohammed A.; Laddha, Umesh D.; Khairnar, Shubham J.; Walode, Sanjay G.
title: Repurposing of Phytomedicine-Derived Bioactive Compounds with Promising Anti-SARS-CoV-2 Potential: Molecular Docking, MD Simulation and Drug-Likeness/ ADMET Studies
date: 2021-12-13
journal: Saudi J Biol Sci
DOI: 10.1016/j.sjbs.2021.12.018
sha: 76bf7f4ad8f98d5b3c1f6bf503fd6ac2bdceb9d5
doc_id: 860019
cord_uid: l9hj6xdw

In view of the potential of traditional plant-based remedies (or phytomedicines) in the management of COVID-19, the present investigation was aimed at finding novel anti-SARS-CoV-2 molecules by in silico screening of bioactive phytochemicals (database) using computational methods and drug repurposing approach. A total of 160 compounds belonging to various phytochemical classes (flavonoids, limonoids, saponins, triterpenoids, steroids etc.) were selected (as initial hits) and screened against three specific therapeutic targets (Mpro/ 3CLpro, PLpro and RdRp) of SARS-CoV-2 by docking, molecular dynamics simulation and drug-likeness/ADMET studies. From our studies, six phytochemicals were identified as notable ant-SARS-CoV-2 agents (best hit molecules) with promising inhibitory effects effective against protease (Mpro and PLpro) and polymerase (RdRp) enzymes. These compounds are namely, ginsenoside Rg2, saikosaponin A, somniferine, betulinic acid, soyasapogenol C and azadirachtin A. On the basis of binding modes and dynamics studies of protein-ligand intercations, ginsenoside Rg2, saikosaponin A, somniferine were found to be the most potent (in silico) inhibitors potentially active against Mpro, PLpro and RdRp, respectively. The present investigation can be directed towards further experimental studies in order to confirm the anti-SARS-CoV-2 efficacy along with toxicities of identified phytomolecules.

In December 2019 at the city of Wuhan, China, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has begun the outbreak of coronavirus disease-2019 (COVID-19) (Ciotti et al., 2019; Bhat, 2021) . The disease has led to a catastrophic situation with unprecedented health and economic burden of current times around the world. Infection with SARS-CoV-2 causes mild illness (fever, cough, sore throat) to severe respiratory disease Kumar et al., 2021b; Rasmi et al., 2021; . This devastating infectious disease may even cause fatal systemic complications and eventually deaths. SARS-CoV-2 has affected lives of millions of people till date. Apart from certain prophylactic measures (antiviral therapy, use of antibacterial antibiotics, corticosteroids or immunosuppressants, and convalescent plasma therapy etc.) (Guo et al., 2020; Singhal, 2020) , there are no currently available FDA approved drugs and/or drug therapy that can cure the disease. The discovery of novel antiviral agents against COVID-19 is, therefore, an urgent need of the hour.

There is an extensive research going on across the globe to discover and develop therapeutic candidates that can effectively treat SARS-CoV-2 infection. Many existing drugs/ drug therapy/ clinical candidates (for example, hydroxychloroquine/chloroquine, hydroxychloroquine/azithromycin, favipiravir, oseltamivir (tamiflu), lopinavir/ritonavir, remdesivir/baricitinib/tocilizumab, etc. (Hassan et al., 2020; Singhal, 2020) have been repurposed, but the clinical development of these drugs/ drug regimens in confirmation of their efficacy and toxicity are still under trial. Drug repositioning (DR) utilizes computational and experimental approaches to explore new clinical indications of existing drugs on a rational basis. DR increases the efficiency of drug discovery and it reduces the time as well as cost of discovery as well (Adeoye et al., 2020; Singhal, 2020) . Repurposing has investigated the clinical usefulness of many existing drugs as depicted above including some of the natural products such as ivermectin, colchicine etc. (Elmezayen et al., 2020; Harapan et al., 2020) as prophylactic agents. Researchers have also been investigation the repurposing of available antiviral molecules as possible therapeutic alternatives against coronavirus infection (Caly et al., 2020; Harapan et al., 2020; . Apart from repurposing FDA approved and clinical candidates, phytomedicine-derived bioactive compounds (or simply called phytochemicals such as curcumin, quercetin, epigallocatechin gallate (EGCG) and many others) (Khan and Al-Balushi, 2020; Shah et al., 2020) have also been extensively investigated in search for potential lead molecules/ drug candidates against COVID-19.

Phytomedicine have been an immense repository of bioactive molecules of medicinal importance with biological/ pharmacological potential in diverse therapeutic areas. For instance, natural products like quinine, taxol, artemisinin have been derived from traditionally useful plants or phytomedicine. Phytomedicine is still, therefore, believed to be a potential resource for new drug molecules/ drug candidates (Cherian et al., 2020; Kouznetsova et al., 2020) . Many medicinal plants and their isolated compounds have also been claimed to exhibit antiviral activities (Swargiary et al.; . Plant-derived compounds (phytochemicals) belonging to several classes of phytochemicals (such as polyphenols, flavonoids, limonoids, terpenoids, saponins, steroids etc.) obtained from several phytochemical database (PubChem, Zinc, Drug Bank, IMPPAT, COCONUT, NPBS etc.) have been investigated by computational methods using in silico approaches to identify possible leads that can inhibit biological (protein/ enzymatic) molecules as targets in SARS-CoV-2 (Wang et al., 2015; Swargiary et al., 2020) . In recent days, in sillico screening of phytochemical database has gained tremendous interest in drug discovery research for the identification of new drugs leads. In DR approach, the combined DR strategy of in silico approach i.e., high-throughput virtual screening/structure-based drug design (SBDD) and experimental assays is successfully implemented in the discovery of drug molecules (Tiwari et al., 2010; Rajasekaran et al., 2013) . The combined DR strategy of in silico approach and experimental assays offers an effective alternative platform for the development of bioactive phytochemicals as possible drug candidates. Virtual screening approaches including molecular dynamics simulation studies are usually being adopted to screen out potential drug candidates from natural products/ phytochemicals libraries using various phytochemical databases (mentioned above) available in the public domain. Recent studies also report virtual screening/ docking/ molecular dynamics of medicinal plants/ phytochemicals having traditional medicinal importance with SARS-CoV-2 protease/ polymerase inhibitory potential (Gupta et al., 2020; Vardhan and Sahoo, 2020) .

Inhibition of protease (chymotrypsin-like protease, 3CLpro or main protease, Mpro) (Joshi et al., 2020; Umadevi et al., 2020; Sepay et al., 2020; Chowdhury, 2020; Gowrishankar et al., 2021) , and RNA-dependent RNA polymerase (RdRp) polymerase (Motiwale et al., 2020; Bhat, 2021; Naik et al., 2021; Ishola et al., 2021; Shree et al., 2020; Schoeman and Fielding, 2019) enzymes has been attributed to be crucial for blocking maturation of viral particles, viral replication and further infection. Several research related to the development of natural products as inhibitors of protease and polymerase enzymes have already been reported using computational modeling and approaches (Prajapat et al., 2020; Motiwale et al., 2020; Schoeman and Fieldin, 2019) . In view of above considerations, our investigation was carried out to evaluate the anti-SARS-CoV-2 potential of biologically active phytochemicals particularly found in traditional medicines (Ayurvedic medicines, Chinese medicines etc.) with possible inhibitory activities against Mpro, PLpro and RdRp enzymes. In this paper, a total of 160 phytochemicals belonging to various phytochemical classes were selected and virtually screened against three aforementioned therapeutic targets of SARS-CoV-2 by DR approach with an aim to find out anti-SARS-CoV-2 molecules effective against SARS-CoV-2 infection with protease and polymerase inhibitory activities.

A total of 160 biologically active plant-derived compounds (phytochemicals) with a wide range of structural diversity belonging to different phytochemical classes (polyphenols, flavonoids, limonoids, triterpenoids, saponins and steroids) were selected based on their potential medicinal/ biological interests (such as antiviral, antimicrobial, antibacterial activities etc.) as reported in traditional (Ayurvedic or Chinese medicines) as well as modern phytomedicines (Hussain et al., 2021; Isyaku et al., 2020; Rolta et al., 2020) . The 3D structures of compounds were downloaded from the PubChem database and saved in sdf files. Ligands were energetically minimized (2000 steps, RMSD value = 0.01 kcal/mol) using the CHARMm-based minimizer on Biovia Discovery Studio (DS 2020).

The structures of SARS-CoV-2 proteins viz., Mpro (3CLpro) with resolution 2.16 Å (6LU7) (Prajapat et al., 2020) , PLpro with resolution 1.66 Å (6WX4) (Shree et al., 2020) , and RdRp with resolution 2.50 Å (7BV2) (Ibrahim et al., 2020) were retrieved from the RCSB-PDB database. DS 2020 program was used for the preparation of proteins. The co-crystallized inhibitor, water molecules and hetero atoms were removed. The protein molecules were energetically minimized (200 steps, RMSD value = 0.1 kcal/mol) using the CHARMm minimizer.

Docking was performed using Biovia DS 2020 (Biovia, USA) software. The binding site residues were selected and thereby binding spheres were generated and parameters for the receptor grid were set (Kumar et al., 2021a; Ibrahim et al., 2020; (Kalita et al., 2020) . The radius for the sphere of binding site was set at 12.738 Å, 10.323 Å and 8.500 Å, with x, y, z dimensions of -10. 897304, 13.066857, 68.557888; 8.904486, -27.443594, -37.926085; and 95.588588, 92.112297, 105 .258816 for 6LU7, 6WX4 and 7BV2, respectively.

The docking was performed using CDocker of the DS 2020 (Larini et al., 2007; Holzinger et al., 2014) . The calculations of CDocker energy and CDocker energy were done. Based upon docking score, top 25 phytochemicals were selected for further study. The binding modes of docked complexes were studied using the 3D receptor-ligand poses. 2D diagrams of receptor-ligand complexes were used to study various interactions like hydrogen bonding, hydrophobic etc.

MD simulation of docked complexes was executed on DS software 2020. Compounds which exhibited best binding affinity were further investigated for MD simulation. This study validates results of docking and evaluates the conformation of protein-ligand complexes. The original structures of the target proteins with co-crystal inhibitors (6LU7, 6WX4 and 7BV2) were treated as native compounds for the MD simulation. The best pose obtained in docking for each selected ligand having the highest dock score against each target protein was used for the MD simulation (Rolta et al., 2020; Singh et al., 2020; Larini et al., 2007) . Basic parameters of trajectory analyses such as root mean square deviations (RMSD), root mean square fluctuation (RMSF), radius of gyration (ROG), number of hydrogen bonds (H-bonds) were analyzed for each protein-ligand complex (Noha et al., 2017) .

After cleaning, the solvation of complexes was done in a cubic water box with 10 Å × 10 Å × 10 Å in size. During solvation 0.15 M NaCl was used for the neutralization of the system. The energy minimization was then carried out for 5000 steps with a RMSD gradient of 0.01 kcal/mol (Peele et al., 2020) . Once the simulation is complete, RMSD, RMSF and ROG were computed. During simulation period, analysis of hydrogen bonding was also carried out (Wang et al., 2006; Zakaryan et al., 2017) .

To calculate the binding free energy (ΔG), the MM-PBSA is applied (Rubenstein et al., 2018; (Lai et al., 2011) . The binding free energies were calculated using DS software. All generated conformations were analyzed and the individual binding free energy was calculated. The average binding free energy (ΔG) was finally calculated (Genheden and Ryde, 2015; Zhu et al., 2020) .

Top 15 phytochemicals which showed higher docking score or better binding affinities were selected for in silico drug-likeness and ADME-Toxicity (ADMET) calculations using DS 2020.

Parameters such as log of n-octanol/water partition coefficient (LogP), molecular weight (MW), hydrogen bond acceptors (nHBAs), hydrogen bond donors (nHBDs), molecular polar surface area (PSA) and number of rotable bonds (nRotBs) incorporated in Lipinski's rule of five (Rastelli et al., 2010) and Veber rule (Lipinski et al., 1997; Pillaiyar et al., 2020) were calculated. In ADMET prediction, parameters such as aqueous solubility, blood-brain barrier penetration, cytochrome P450 (CYP) 2D6 inhibition, hepatotoxicity, intestinal absorption and plasma protein binding) were determined (Jorgensen and Duffy, 2002; Veber et al., 2002) .

Prior to protein-ligand docking, the receptor grid models [ Figure 1 (a)-(c)] of Mpro, PLpro and RdRp were generated/ optimized in terms of predicting binding site spheres in order to achieve optimal molecular interactions between receptor molecules and test compounds. The validation of the protein models (6LU7/N3, 6WX4/VIR251 and 7BV2/Remdesivir) used for the docking study was done by re-docking procedure (Kousar et al., 2020) . After docking, top 10 docked posed generated were superimposed within the binding site containing the original co-crystal ligand and RMSD values of docked poses were calculated using the co-crystal ligand as the reference ligand (Naik et al., 2021) . Results showed superimposition of the complexes to the original structures of the protein molecules. Figure 2 shows superimposition of co-crystal ligands (N3, VIR251 and Remdesivir) in the predicted binding site of the original structure of Mpro (6LU7), PLpro (6WX4) and RdRp (7BV2), respectively. The figure also demonstrates that the docked pose possesses binding mode similar to the co-crystal ligand.

Results of docking clearly reveal that almost all of the 160 phytochemicals were successfully docked into the protein molecules. Depending on the scores of CDocker and CDocker interaction energies, top 25 docked molecules were screened out and selected for the protein-ligand interaction analyses. Results of top 25 dock scored phytochemicals against the three target proteins are summarized in Table 1 . A majority of phytochemicals exhibited binding affinities against all the three target proteins. These 25 phytochemicals were identified as active inhibitors (best hit molecules) of both protease (Mpro and PLpro) and polymerase (RdRp) enzymes. Out of 25 compounds, top three dock scored phytochemicals were selected as the best inhibitors against their respective protein molecule. Detailed explanation about the selected candidates protease or polymerase inhibitors are as follows:

The Mpro (3CLpro) is a cysteine protease viral enzyme. The catalytic dyad i.e., His41-Cys148 is responsible for the proteolytic activity of Mpro (Chowdhury, 2020 Hydrogen bonds were formed with amino acid residues like Thr26, Asn142, Glu166, Gln189 and

Ala191. The extent of interaction of co-crystal ligand with the Mpro enzyme was comparatively less than that of phytochemical inhibitors. Docking interaction diagrams (2D) of ginsenoside Rg2 along with their co-crystal inhibitor, N9 showing the participation of active site amino acids of Mpro are presented in Figure 7 (a, b) . Details of hydrogen bonding interactions are given in Table   2 (a).

The PLpro is another important cysteine protease enzyme responsible for the maturation of viral particles (Shree et al., 2020) . From the docking study, it was revealed that betulinic acid, Gln269. The extent of interaction of co-crystal ligand, VIR251 with the PLpro enzyme was comparatively more than that of phytochemical inhibitors. Docking interaction diagrams (2D) of saikosaponin A along with its co-crystal inhibitor, VIR251 are displayed in Figure 7 (c, d) . Details of hydrogen bonding interactions are given in Table 2 .

The RdRp is believd to be an indispensable enzyme for the replication of viral genomes (Chowdhury, 2020) . Docking study against PLpro revealed that azadirachtin, betulinic acid and somniferine exhibited betters binding affinity compared to rest of the compounds. Azadirachtin Figure 7 (e, f). Details of hydrogen bonding interactions are given in Table 2 .

The RMSD value of Mpro-N3 complex was found stable without significant deviations during the 50 ns simulation period which was maintained below 4 Å. In the case of Mpro-ginsenoside Rg2, the deviation was more and maintained below 8 Å. In both the cases, the plateu state of proteinligand complexes reached within 3 ns and thereafter no significant deviations were observed. 

Results of drug-likeness parameters for top 15 dock scored compounds are displayed in Table 4 .

All drug-likeness data were found to be within the considerable range indicating the good druglikeness behaviour of the screened phytochemicals. LogP, MW, and molecular PSA indicate good membrane permeability, intestinal absorption and oral bioavailability, whereas, other parameters such as nHBAs, nHBDs, and nRotb bonds facilitate drug metabolism and pharmacokinetics (DMPK). Some compounds showed relatively higher lipophilicity, which could be accounted for the better biological activities due to the increased permeation/ absorption of biological membranes (Rudrapal and Chetia, 2017) .

The predicted ADMET data of top 15 compounds are presented in Table 5 . All the compounds exhibited good aqueous solubility and gastrointestinal absorption, which could facilitate compounds to attain increased concentration in the blood for optimal biological action. These compounds also exhibited poor blood-brain barrier (BBB) penetration indicating less probability of producing CNS toxicity. Most of the compounds were found to be not inhibitors of cytochrome P450 2D6 (CYP 2D6). Compounds showed no predictive hepatotoxicity, which means that compounds are not expected to possess significant liver toxicity. Plasma protein binding (PPB) data revealed that some of the compounds were strongly bound, while some were less strongly bound. However, overall predicted ADMET data proved satisfactory ADMET parameters of phytochemicals confirming their drug-like or lead-like properties.

A more detailed explanation about the phytochemicals including their phytochemical type/ nature of structural skeleton, plant source and inhibitory activity against specific enzymes are summarized in Table 6 . Figure 9 depicts chemical structures of individual compounds identified as protease/ polymerase inhibitors.

Medicinal plants (turmeric, neem, ashwagandha, tea, holy basil, licorice, dioscorea, sesame etc.) that are reported in traditional remedies and herbal medicine (Ayurvedic or Chinese medicine) having antiviral, anti-HIV, antibacterial, antifungal, antiprotozoal, antimutagenic and immunomodulatory activities were extensively reviewed for the selection of phytochemicals (flavonoids, limonoids, saponins, triterpenoids, steroids etc.) with plausible protease and/or polymerase inhibitory effects. A total of 16 phytochemicals (initial hits) were selected and virtually screened by molecular docking, MD and drug-likeness/ADMET studies in order to find out potential protease and polymerase inhibitors, particularly against 3CLpro, PLpro and RdRp enzymes.

In validation study, the RMSD values were obtained as 1.57 Å, 1.47 Å, 2.45 Å for Mpro, PLpro and RdRp, respectively, which indicated the robustness of the docking procedure. Superimposed docked complexes showed lower RMSD values as compared to the original co-crystal structures.

It further demonstrated the stability of the complexes. The validation study further substantiates the strong binding affinity of ligands for their corresponding protein molecules. Results of validation study also confirmed the experimental binding modes of co-crystal inhibitors (N3, VIR251 and Remdesivir) in the active site residues of respective protein molecule (6LU7, 6WX4

and 7BV2) with sufficient reproducibility of the predicted protein-ligand interactions.

Molecular docking is a structure-based virtual screening approach which identifies active inhibitors based upon the prediction of binding affinity as well as molecular interactions of ligand molecules (or inhibitors) with their corresponding target proteins or enzymes (Cheng et al., 2007; Ford et al., 2020) . Flexible docking simulation protocol is usually performed to assess the binding affinity of active inhibitors. The most favourable binding mode of docked poses is investigated for the protein-ligand complex formed with low energy conformation. Docking finds out the best binding orientation of ligands for their corresponding target molecules. With the prediction of binding affinity against the target protein, docking helps to assess the biological efficacy of small molecules (Zhang et al., 2020) . In docking study, CDocker program docked all the phytocompounds into the predicted active site of three different protein molecules. CDocker, a simulation based algorithm which uses a CHARMm-based molecular dynamics (Swargiary et al., 2020; Ferrin, 2004) . Compounds showed predictable binding affinity against target protein molecules (Mpro, PLpro and RdRp) with well defined molecular interactions with several active site residues. Higher binding affinities were observed for docked compounds as compared to the co-crystal inhibitor. CDcoker energy represents the minimum binding energy of a protein-ligand complex, whereas CDocker interaction energy is the minimum energy of interactions taken place between the protein and ligand molecule. The more negative the interaction energy, the stronger this interaction will be. Affinity therefore depends on the energy of interaction. Binding energies thus depicts the strength of interactions as well as the affinity of a ligand molecule for its receptor molecule. Formation of stable complexes with well-defined interaction details predicts the significance of molecular docking and further molecular modeling studies.

From MD simulation, the stability and flexibility of complexes can be assessed (Salmaso and Moro, 2018) . From results of MD simulation and MM-PBSA analyses, it is claimed that ginsenoside Rg2 possesses superior binding affinity against Mpro than that of of saikosaponin A and somniferine against PLpro and RdRp, respectively. Medicinal plants containing polyphenolic substances, saponins and steroids have been reported to possess antiviral activities (Buonaguro et al., 2020) .

Results of drug-likeness revealed that all these fifteen compounds exhibited acceptable drug-like properties. Lipinski's rule of five and Veber rule (Zhang and Lazim, 2017) were obeyed. Compounds with LogPo/w ≤ 5, Mol. wt. ≤ 500, nHBAs ≤ 10 and nHBDs ≤ 5 (Zhang and Lazim, 2017) are satisfactory. Further, compounds having ≤ 10 RotB and molecular PSA of ≤ 140 A2 behave to be drug-like to exhibit optimal permeability across membrane with acceptable oral bioavailability. In ADMET prediction, good intestinal absorption is probably owing to their satisfactory LogP property. Unsatisfactory ADMET profile accounts for the failure of most drug candidates in the late phase of drug discovery (Ibrahim et al., 2020) . Poor solubility and inadequate intestinal absorption are attributed to lower the extent of drug absorption and oral bioavailability (Kousar et al., 2020; Othman et al., 2021; Ghosh et al., 2021; Khan et al., 2021) . CYP 2D6 is the key metabolic enzyme in drug metabolism (Ibrahim et al., 2020) . Non-inhibition of CYP 2D6

suggests their ease of metabolism by this enzyme. The extent of PPB signifies the therapeutic action of drugs. Fraction of drug in a protein bound state does not confer any action, whereas the unbound fraction exerts the therapeutic action (Isyaku et al., 2020; Junejo et al., 2021) .

From docking and MD simulation studies, six phytochemicals were found to exhibit remarkable SARS-CoV-2 inhibitory activities (best hit compounds), particularly against Mpro, PLpro and

RdRp. These compounds are namely, ginsenoside Rg2, saikosaponin A, somniferine, betulinic acid, soyasapogenol C and azadirachtin A. These phytochemicals are found in traditional Indian i.e., Ayurvedic and Chinese medicines such as neem, ashwagandha, ginseng including food plants like soybean. All the identified compounds are basically tri-/tetra-terpenoids, saponins or steroids with their wide natural abundance in traditional Ayurvedic and/or Chinese medicines. However, upon critical analysis of MD results, it is apparent that the interaction occurred between ginsenoside Rg2 and the Mpro protein is comparatively more significant over the interactions involved between saikosaponin and PLpro and somniferine and RdRp. Conventional hydrogen bonding predominates over other non-bonding interactions like carbon hydrogen bonds and hydrophobic interaction. Amino acid residues involved in various interactions come from predicted catalytic/ active sites of protein molecules. It is evident that the interaction of ginsenoside Rg2 with Mpro protein is much more promising than the interaction between N3 and Mpro. In case of saikosaponin and somniferine, the interactions with their corresponding protein targets, PLpro and RdRp, respectively occurs to a lesser extent comapred to VIR251 and Remdesivir, respectively.

Our study identifies six bioactive phytomolecules with promising anti-SARS-CoV-2 potential, particularly effective against SARS-CoV-2 Mpro, PLpro (protease) and RdRp ( AS (aqueous solubility level): 3-good, 2-low, 1-very low, 0-extremely low; BBB (blood brain barrier) penetration level: 4-very low, 3-low, 2-medium, 1-high, 0-very high; CYP2D6 (cytochrome P450 2D6) inhibition level: F (false)-noninhibitor, T (true)-inhibitor; Hepatox (hepatotoxicity): T (true)-toxic, F (false)-non-toxic; IA (intestinal absorption level): 0-good, 1-moderate, 2-poor, 3-very poor; PPB (plasma protein binding): T (true)-highly bound, F (false)-poorly bound 

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The authors thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project number (xxxx). Authors would like to thank Dr. Sagarika Chandra for her needful help towards editing the images incorporated in the manuscript.

The authors hereby declare that there are no conflicting interests concerning the present study. There are no financial and personal relationships with other people or organizations that could inappropriately influence (bias) the outcomes of the present study.