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. 2020 Jun 9;20:100367. doi: 10.1016/j.imu.2020.100367

Screening and druggability analysis of some plant metabolites against SARS-CoV-2: An integrative computational approach

Kazi Faizul Azim a,b,, Sheikh Rashel Ahmed a,c, Anik Banik a, Md Mostafigur Rahman Khan a, Anamika Deb a, Saneya Risa Somana a
PMCID: PMC7280834  PMID: 32537482

Abstract

The sudden outbreak of novel coronavirus has caused a global concern due to its infection rate and mortality. Despite extensive research, there are still no specific drugs or vaccines to combat SARS-CoV-2 infection. Hence, this study was designed to evaluate some plant-based active compounds for drug candidacy against SARS-CoV-2 by using virtual screening methods and various computational analyses. A total of 27 plant metabolites were screened against SARS-CoV-2 main protease proteins (MPP), Nsp9 RNA binding protein, spike receptor binding domain, spike ecto-domain and HR2 domain using a molecular docking approach. Four metabolites, i.e., asiatic acid, avicularin, guajaverin, and withaferin showed maximum binding affinity with all key proteins in terms of lowest global binding energy. The crucial binding sites and drug surface hotspots were unravelled for each viral protein. The top candidates were further employed for ADME (absorption, distribution, metabolism, and excretion) analysis to investigate their drug profiles. Results suggest that none of the compounds render any undesirable consequences that could reduce their drug likeness properties. The analysis of toxicity pattern revealed no significant tumorigenic, mutagenic, irritating, or reproductive effects by the compounds. However, withaferin was comparatively toxic among the top four candidates with considerable cytotoxicity and immunotoxicity. Most of the target class by top drug candidates belonged to enzyme groups (e.g. oxidoreductases hydrolases, phosphatases). Moreover, results of drug similarity prediction revealed two approved structural analogs of Asiatic acid i.e. Hydrocortisone (DB00741) (previously used for SARS-CoV-1 and MERS) and Dinoprost-tromethamine (DB01160) from DrugBank. In addition, two other biologically active compounds, Mupirocin (DB00410) and Simvastatin (DB00641) could be an option for the treatment of viral infections. The study may pave the way to develop effective medications and preventive measure against SARS-CoV-2. Due to the encouraging results, we highly recommend further in vivo trials for the experimental validation of our findings.

Keywords: SARS-CoV-2, Plant metabolites, Main protease proteins, Molecular docking, ADME analysis, Drug target

Graphical abstract

Image 1

Highlights

  • Asiatic acid, Avicularin, Guajaverin & Withaferin showed maximum binding affinity.

  • The crucial binding sites & drug surface hotspots were unravelled for key viral protein.

  • ADME analysis revealed that top candidates do not show any undesirable consequences.

  • Witheferin was comparatively toxic with considerable cytotoxicity and immunotoxicity.

  • Hydrocortisone and Simvastatin could be an option to treat SARS-CoV-2 infections.

1. Introduction

The sudden outbreak of novel coronavirus (SARS-CoV-2) infection emanated from Wuhan, China and spread throughout the world excepting a few countries to date [1]. The virus is responsible for causing novel disease, which WHO officially called COVID-19. As of April 23, 2020, World Health Organization (WHO) estimated that new coronavirus touched 213 countries, areas or territories [2,3]. The infection rate is increasing. However, the fatality rate of SARS-CoV-2 (3.4%) estimated by WHO is lower than previous fatal diseases SARS and MERS, which had 9.6% and 35% death rates, respectively [4,5].

Coronaviruses are enveloped, positive single-stranded RNA viruses with large genome size ranging from 26 kb to 32 kb. These viruses are representative of four subfamilies, which include alpha-, beta-, gamma- and delta-coronaviruses. SARS-CoV-2 showed more sequence similarity with SARS-CoV than MERS-CoV when genome sequences of these mentioned viruses were compared [6]. But they also exhibit dissimilarities that can influence their process of pathogenesis [7,8]. SARS-CoV-2 infects humans through the same entry point of the ACE receptor which is expressed in the respiratory tract [9,10]. However, among various proteins, four proteins are commonly found in the structure of all coronaviruses representing spike (S), envelope (E), membrane (M), and nucleocapsid (N) [8]. The initial and important stage of viral entry into host cell is receptor recognition [11]. The assembly of viral particle involves M protein and E protein, while virus binding and entrance into host cell take place by S protein with the assistance of SARS-CoV-2 angiotensin-converting enzyme [10,12].

The coronavirus (SARS-CoV-2) belongs to the family of Beta-coronaviruses, which are responsible for causing severe human respiratory syndrome [3,13]. The virus is spread mainly through community transmission, while SARS and MERS affect people via nosocomial spread [14]. It can transmit from one individual to other by respiratory droplets. SARS-CoV-2 infected patients have general signs and symptoms, suffering initially from common flu-like fever, sputum production, dyspnoea, headache, sore throat/pharyngalgia, and diarrhoea, which may further lead to express life-threatening symptoms including fatal pneumonia [15]. COVID-19 affected patients, either symptomatic or asymptomatic, were detected with the nose area containing a higher viral load than in throat [16]. A critically ill patient has a series of complexities with progression of disease.

The efficacy and safety of antivirals require evaluation by clinical trial [3]. There is no efficient, safe, and specific potential therapeutic to be approved for rapid remedy of this new respiratory syndrome to date [17,18]. Clinical trials of some drugs have been started, yet till now, only a few candidates have shown some efficacy in in vitro studies [19]. Not many have progressed to randomized animal or human trials; hence they may have limited use to counter infection [19]. Many countries and some pharmaceutical companies announced their headway and programs to develop vaccines (e.g. subunit, mRNA, DNA, live-vector vaccine) against the virus. But the developmental process of making human vaccine from concept to licensure may require years [20]. As the epidemic is still spreading, medicinal plants may be alternatively used in making drugs as early as possible. Several scientific researchers reported the helpfulness of plants due to their medicinal value and therapeutic uses as drugs from the ancient times [21]. Plant-derived active compounds of different plant parts are useful for treating diseases including diarrhoea, headache, and inflammation, and bacterial and fungal infections. From prehistoric times, traditional people utilized these plants for the remedial purposes of health deteriorating diseases because of the existence of numerous phytochemicals [22]. Various limitations are associated with modern treatment options including drug-resistance, severe side effects, adverse toxicity profiles, complicated medication etc. Natural products have the potential to form the basis of holistic health care [23]. The properties of antioxidants render medicinal plants to be effective in treating life-threatening diseases such as cancer, Alzheimer's disease, diabetes, malaria, and cardiac diseases (Table 1 ) while minimizing drug toxicity [24].

Table 1.

List of plant metabolites used in the study with respective source and activities.

Metabolites PubChem CID Class Source Activities References
Allicin 65036 S-containing compound Allium sativum Antimicrorial, antiviral Antioxidant, anti-cancer activity [25]
Andrographolide 5318517 Diterpenoid labdane Andrographis paniculata antioxidant, anti-inflammatory, and anti-cancer [26]
Apigenin 5280443 Flavonoid Vegetable and fruit Effective in cancer, depression, diabetes & Alzheimer's disease, [27]
Asiatic acid 119034 Aglycone type pentacyclic triterpenoids Centella asiatica Antioxidant, cardioprotective, anti-inflammatory, antitumor, neuroprotective, antimicrobial [28]
Avicularin 5490064 quercetin-3-a-L arabinofuranoside (flavonoid) Psidium guyava, Lespedeza cuneata anti-inflammatory, anti-oxidant, hepatoprotective activity [29]
Capsaicin 1548943 Alkaloid Capsicum genus Pruritis, pain relief, non-steroidal anti-inflammatory drug induced gastritis [30]
Chavibetol 596375 Phenylpropanoid Piper betle immunomodulatory, radical scavenging [31]
Cinnamic acid 444539 Aromatic carboxylic acids Cinnamomum species Antibacterial, antifungal, antimalarial, antitubercular [32]
Curcumin 969516 Polyphenolic compound Curcuma longa antibacterial, anti-inflammatory antiviral, antioxidant, anti-arthritis & anti-cancer activity [33]
Eugenol 3314 Phenylpropanoid Ocimum tenuiflorum, Eugenia caryophyllata antimicrobial, anti-inflammatory, analgesic and antioxidant [34]
Arjunone 14034821 Flavonoids Terminalia arjuna Arjunone and other compounds have role in antioxidant, antiatherogenic, anti-inflammatory, anti-carcinogenic activity [35]
Galangin 5281616 Flavonol Honey, Alpinia officinarum, propolis Anti-cancer, anti-mutagenic, anti-oxidative, radical scavenging etc. [36]
Gentisic acid 3469 Phenolic acid Gentiana, Citrus, H. rosa-sinensis,
O. europaea, S. indicum
Antioxidant, neuroprotective, antiinflammatory, hepatoprotective, antimicrobial activities [37]
Guajaverin 5481224 Flavonoid Psidium guyava Anti-plaque activity [38]
Kaempferol 5280863 Flavonoid aglycone Vegetable and fruit Anti-inflammatory, antioxidant, antimicrobial, antitumor, cardioprotective, and antidiabetic activities [39]
Luteolin 5280445 Flavonoid Carrots, celery peppers, olive peppermint Anticancer, antioxidant, antimicrobial, anti-inflammatory, and activities [40]
m-Coumaric acid 637541 Phenolic acid Solanum nigrum Role in pharmacological activities [41]
Piperic acid 5370536 Alkaloid Piper nigrum No known function [42]
Piperine 638024 Alkaloid Piper spp. Anticancer, antimicrobial, antimalarial [42]
Quercetine 5280343 Flavonoid Diverse plant species Antioxidant, cardiovascular, antiviral, anti-inflammatory, anticancer, antimicrobial [43]
Swertiamarin 442435 Secoiridoid glycoside Swertia chirata Anti-arthritic, anti-diabetic Cardio-protective, Anticancer, Anti-hepatitis, Antibacterial, anti-atherosclerotic [44]
Swertinin 5491517 Secoiridoid glycoside Swertia chirata Role in pharmacological activities [45]
Thymoquinone 10281 Monoterpene Nigella sativa Anti-oxidant and anti-inflammatory properties, Anti-microbial, Anti-arthritic, anti-cancer efficacy [46]
Vincamine 15376 Alkaloid Catharanthus roseus, Vinca minor Cerebral disorders, antiulcer activity, cerebrovascular insufficiencies [47]
Vitexin 5280441 Apigenin flavone glucoside Crataegus species Anti-inflammatory effects, anti-oxidant effects, anti-carcinogenic effects, anti-viral effects [48]
Withaferin 265237 Steroidal lactone Withania somnifera Anti-cancer, adaptogenic, anti-stress, immunomodulatory, anti-inflammatory, anti-tumor, cardioprotective, and neuroprotective activities. [49]
Zingiberene 92776 Isoprenoids Zingiber Officinale Anti-ulcer, antibacterial, cytoxic effect [50]

The expansion of natural product as new medicine or drug to resist the emerging virus SARS-CoV-2 could be done to bypass the side effects of synthetic drugs. Therefore, the study aimed at evaluating some plant-based active compound for drug candidacy against SARS-CoV-2 through virtual screening methods and various computational investigations.

2. Material and methods

2.1. Retrieval of SARS-CoV-2 proteins/protein-domains and plant metabolites

The 3D structures of SARS-CoV-2 main proteases (6W63, 6LU7), Nsp9 (Non-structural protein-9) RNA binding protein (6W4B), spike receptor binding domain (6M0J), spike ecto-domain (6VYB), and HR2 Domain (6LVN) were retrieved from the RCSB Protein Data Bank [51]. A total 27 plant metabolites belonging to different classes were extracted from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) in SDS (3D) format (Table 1) [52]. The structures were further converted into the PDB format by Open Babel v2.3 [53].

2.2. Screening of plant metabolites against SARS-CoV-2 proteins/protein-domains

Molecular docking is an effective approach for screening suitable therapeutics against specific drug target of deadly pathogens [54]. This powerful tool is used to model the interaction between small ligands and macromolecules, thereby paving the way for drug discovery [55]. The binding affinity of 27 plant metabolites with different SARS-CoV-2 proteins/protein domains (drug targets/macromolecules) were determined by using the PatchDock server [56]. Recently, alpha-ketoamide (CID 6482451) has been suggested as a SARS-CoV-2 MPP inhibitor by experimental study [57]. The ligand was used as a positive control for the present study, and employed for docking analysis against all six macromolecules. The docked complexes were further refined via the FireDock refinement tool [58]. The ligand bond complexes were visualized by Discovery Studio v3.1 and PyMOL v2.0 [59,60].

2.3. Analysis of drug surface hotspot and ligand binding pocket prediction

The drug surface hotspot of SARS-CoV-2 proteins was analysed by investigating the docked complexes with the top metabolites using LigPlot+, Discovery Studio and PyMOL v.2.0 software [59,60]. Binding patterns of asiatic acid, avicularin, guajaverin, and withaferin with six macromolecules were allowed for comparative structural analysis. Moreover, interaction of Alpha-ketoamide with the studied proteins were also investigated.

2.4. Drug profile analysis of top metabolites

Absorption, distribution, metabolism, and excretion (ADME) are four major criteria that influence the drug levels and kinetics of drug exposure to the tissues within an organism. The pharmacological activity and performance of a drug is largely controlled by these parameters [61]. The SwissADME server was used to assess the ADME properties of top four metabolites [62]. The BOILED-Egg model was employed to calculate the blood-brain barrier (BBB) in the studied compounds [63]. The relative toxicity of top drug candidates were analysed via the ProToxII server [64]. This popular webserver efficiently predicts various toxicity endpoints by incorporating molecular similarity, fragment tendency and fragment similarity methods. The server also predicted the oral toxicity based on the analysis of two-dimensional (2D) similarity to compounds with a known median lethal doses (LD50). The set used for the prediction consists of approximately 38,000 unique compounds with known oral LD50 values measured in rodents [65]. Additionally, OSIRIS Property Explorer were employed to investigate the undesired effects of these compounds [66].

2.5. Prediction of drug targets and available drug molecules from DrugBank

SwissTargetPrediction was utilized to estimate the possible macromolecular targets of predicted drug candidates [67]. The server predicts based on a combination of 2D and 3D similarity with a library of 370000 known bioactive compounds on approximately 3000 proteins. Moreover, the SwissSimilarity web tools were used to identify potential drug molecules against SARS-CoV-2 based on homology screening of predicted top drug candidates. The server allowed ligand-based virtual screening of several libraries of small molecules to find approved, experimental, or commercially available drugs from DrugBank using different approaches including FP2 fingerprints, electroshape, spectrophores, and align-IT [68].

3. Results

3.1. Screening of plant metabolites against SARS-CoV-2

All of the retrieved structures of SARS-CoV-2 proteins/protein-domains (macromolecules) and plant metabolites (ligands) were optimized and employed for molecular docking to predict the affinity between the above-mentioned ligands and the macromolecules. The metabolites were ranked based on global binding energy and the results depict that the top four scorers (metabolites) were the same for each of the macromolecules in terms of minimum binding energy (Table 2 and Supplementary File 1). In each case, asiatic acid, avicularin, guajaverin, and withaferin showed the best binding interactions with six studied macromolecules (Fig. 1 and Table 2). Moreover, asiatic acid exhibited the highest binding affinity with SARS-CoV-2 main protease (−53.05 kcal/mol), Nsp9 RNA binding protein (−50.04 kcal/mol), and spike ecto-domain (60.68 kcal/mol) (Fig. 2 and Table 2), while guajaverin bound with the spike receptor binding domain and HR2 Domain with a binding energy of −47.34 kcal/mol and −28.73 kcal/mol, respectively (Fig. 3 and Table 2).

Table 2.

Analysis of global binding energy and interaction sites of the screened top 4 metabolites.

Macromolecules Ligands Global Energy ACE Score Area Ligand binding residues
6W63 α-ketoamide (Control) −56.92 −16.84 4560 526.40 Asp197, Leu272, Gly275, Leu286, Leu287, Asp289
Asiatic acid −53.05 −15.26 4916 577.10 His41, Met49, Tyr54, Asn142, Met165
Avicularin −48.62 −18.50 4694 532.10 Thr25, Thr26, His41, Cys44, Ser46, Met49, Gly143, Cys145
Guajaverin −48.48 −15.12 4450 497.50 Thr25, His41, Cys44, Met49, Asn142, Cys145, Met165, Asp187, Arg188
Withaferin −48.46 −14.08 4984 597.40 His41, Met49, Met165, Pro168, Ala191
6W4B α-ketoamide (Control) −48.60 −16.39 4458 504.60 Phe41, Trp54, Ile66, Thr68, Glu69
Asiatic acid −50.04 −16.37 4998 564.20 Met13, Gly39, Arg40, Phe41, Val42, Phe57, Pro58, Ile66
Withaferin −47.95 −13.30 4896 570.40 Arg40, Val42, Phe57, Pro58,Lys59, Ser60, Ile66
Guajaverin −42.72 −10.63 4548 641.40 Asn1, Asn2, Glu3, Gln50, Pro72, Pro73
Avicularin −39.83 −23.80 4556 514.50 Met49, Met165, Glu166, Thr190
6VYB α-ketoamide (Control) −63.94 −17.32 5728 705.10 Thr547, Gly548, Thr549,Asp745, Val976
Asiatic acid −60.68 −22.33 6276 771.50 Phe338, Ala363, Tyr365, Leu368, Cys379, Pro384, Leu387, Leu390, Phe392, Val395, Cys432, Ile434, Leu513, Val524
Withaferin −60.19 −20.49 5760 793.10 Ile410, Pro412, Leu425, Pro426, Cys432, Val433, Phe464, Val512, Leu513
Guajaverin −55.24 −17.51 5208 659.20 Ile410, Pro412, Lys424, Gly431, Cys432, Val433, Val512
Avicularin −52.93 −17.15 5474 683.30 Ala411, Pro412, Leu425, Cys432, Val433, Val512
6LVN α-ketoamide (Control) −25.52 −2.71 4318 564.20 Ile16, Asn20, Lys24, Asn27, Glu28
Guajaverin −28.73 −2.13 3696 443.50 Asp17, Arg18, Glu21, Lys24
Withaferin −28.11 −1.24 4376 507.70 Lys14, Lys24, Arg18
Asiatic acid −27.58 −1.12 4366 500.30 Lys14, Lys24, Asp17, Arg18, Glu21
Avicularin −26.48 −1.22 3986 465.10 Asp17, Arg18, Glu21, Asn20
6M0J α-ketoamide (Control) −60.50 −9.34 5374 655.40 Lys94, Tyr196, Asp206, Glu208, Val209, Asn210
Guajaverin −47.34 −11.22 4554 575.60 Leu95, Gln98, Ala99, Glu208, Asn210, Ala396, Lys562, Trp566
Withaferin −46.84 −11.13 5598 640.50 Leu95, Gln98, His195, Tyr196, Lys562
Asiatic acid −45.69 −13.09 5978 691.70 Leu95, Gln98, Ala99, Tyr202, Asp206, Glu208, Val209, Ala396, Lys562, Pro565, Trp566
Avicularin −43.13 −11.09 5232 604.20 Lys94, Leu95, Tyr196, Val209, Asn210, Ala396, Lys562
6LU7 α-ketoamide (Control) −56.13 −15.07 4578 492.00 Asp197, Lys236, Tyr237, Leu272
Avicularin −54.04 −14.77 4584 520.60 His41, Met49, His164, Met165, Glu166, Pro168, Thr190, Asp187, Arg188, Gln189
Guajaverin −51.69 −12.92 4182 515.50 Met49, Phe140, His163, His164, Met165, Glu166, Arg188, Gln192
Withaferin −47.08 −14.06 4708 560.60 Thr24, Met49, His41, Cys145, Met165, Arg188
Asiatic acid −43.52 −13.90 5050 562.20 Met49, Leu141, Asn142, Ser144, Cys145, His163, Glu166

Fig. 1.

Fig. 1

Chemical structures of asiatic acid (A), guajaverin (B), avicularin (C) and withaferin (D).

Fig. 2.

Fig. 2

Molecular interaction of asiatic acid with SARS-CoV-2 main protease (A), Nsp9 RNA binding protein (B), and spike ecto-domain (C).

Fig. 3.

Fig. 3

Molecular interaction of SARS-CoV-2 main protease with avicularin (A), HR2 domain with guajaverin (B), and spike receptor-binding domain with guajaverin (C).

3.2. Analysis of drug surface hotspot and ligand binding pocket prediction

The structural conformation of the docked complex was analysed to unravel the drug surface hotspot of studied SARS-CoV-2 proteins. The ligand binding pattern and interacting residues with their respective positions were investigated (Table 2). Results revealed that the amino acids from 41 to 54 and 142–190 positions were crucial for the binding interactions of SARS-CoV-2 main protease protein (6W63). Moreover, His41, Cys44, Met49, Asn142, Cys145, Met165 were involved in maximum cases to form the docked complexes. The ligands showed highest binding affinity for 39–73 and 142–166 regions of Nsp9 RNA binding protein (6W4B). Again, the residues from 94 to 99 and 563–566 regions were identified as top surface hotspots for spike receptor binding domain (6M0J) where the position Lys94, Leu95, Tyr196, Lys562, Trp566 were most dominant (Table 2).

3.3. ADME analysis of top drug candidates

Different ADME properties, i.e., physicochemical parameters, pharmacokinetics, lipophilicity, water solubility, medicinal chemistry of top drug candidates were estimated to evaluate their drug profiles (Fig. 4 and Table 3 ). Analysis of inhibition effects with different CYP isoforms (CYP1A2, CYP2D6, CYP2C9, CYP2C19, CYP3A4) revealed that none of the candidates had such an interaction possibility with any cytochromes P450 isoforms. GI absorption was found higher for asiatic acid and withaferin, while lower for guajaverin and avicularin. Moreover, blood-brain barrier (BBB) permeation was calculated by the BOILED-Egg model, which revealed no BBB permeant among the studied top drug candidates. Each candidate was water soluble from a moderate to high level, while guajaverin and avicularin showed maximum solubility (Table 3).

Fig. 4.

Fig. 4

ADME analysis of top four metabolites; A: Asiatic acid, B: Guajaverin, C: Avicularin, and D: Withaferin.

Table 3.

Drug profile and ADME analysis of the top four metabolites.

Parameter Top Main Protease Protein Inhibitors of SARS-CoV-2
Asiatic acid Guajaverin Avicularin Withaferin
Physicochemical parameters Formula C30H48O5 C20H18O11 C20H18O11 C28H38O6
Molecular weight 488.70 g/mol 434.35 g/mol 434.35 g/mol 470.60 g/mol
No. H-bond acceptor 5 11 11 6
No. H-bond donors 4 7 7 2
Molar Refractivity 139.24 104.19 104.19 127.49
TPSA 97.99 Å2 190.28 Å2 190.28 Å2 96.36 Å2
Lipophilicity Log Po/w (iLOGP) 2.95 1.77 1.86 3.24
Log Po/w (XLOGP3) 5.70 0.43 0.98 3.83
Log Po/w (WLOGP) 5.03 0.10 0.10 3.35
Log Po/w (MLOGP) 4.14 −2.06 −2.06 2.75
Log Po/w (SILICOS-IT) 3.96 −0.10 0.06 3.93
Consensus Log Po/w 4.36 0.03 0.19 3.42
Pharmacokinetics GI absorption High Low Low High
BBB permeant No No No No
P-gp substrate Yes No No Yes
CYP1A2 inhibitor No No No No
CYP2C19 inhibitor No No No No
CYP2C9 inhibitor No No No No
CYP2D6 inhibitor No No No No
CYP3A4 inhibitor No No No No
Log Kp (skin permeation) - 5.23 cm/s −8.64 cm/s −8.25 cm/s −6.45 cm/s
Water Solubility Log S (ESOL) −6.33 −2.99 −3.27 −4.97
Solubility 2.29e-4 mg/ml;
4.69e-7 mol/l
4.47e-01 mg/ml; 1.03e-03 mol/l 2.34e-01 mg/ml; 5.39e-04 mol/l 5.01e-03 mg/ml; 1.07e-05 mol/l
Class Poorly soluble Soluble Soluble Moderately soluble
Log S (SILICOS-IT) −4.28 −1.94 −2.07 −3.79
Solubility 2.59e-2 mg/ml; 5.31e-05 mol/l 4.96e+00 mg/ml; 1.14e-02 mol/l 3.71e+0 mg/ml; 8.55e-3 mol/l 7.54e-02 mg/ml; 1.60e-04 mol/l
Class Moderately soluble Soluble Soluble Soluble
Medicinal Chemistry Leadlikeness No; 2 violations: MW > 350, XLOGP3>3.5 No; 1 violation: MW > 350 No; 1 violation: MW > 350 No; 2 violations: MW > 350, XLOGP3>3.5
Bioavailability Score 0.56 0.17 0.17 0.55
PAINS 0 alert 1 alert: catechol_A 1 alert: catechol_A 0 alert
Synthetic accessibility 6.56 5.05 5.04 6.83

3.4. Toxicity pattern analysis of top drug candidates

Prediction of various toxicity endpoints such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes (Tox21) pathways and toxicity targets were analysed (Table 4 ). Results revealed that guajaverin and avicularin fell in the category of toxicity class 5, while the predicted toxicity group for Asiatic acid and withaferin were 4 and 2 respectively (the lower the class the higher the toxicity). Estimated LD50 for asiatic acid, avicularin, guajaverin and withaferin were 2000, 5000, 5000 and 7 mg/kg respectively. The toxicity radar in Fig. 5 illustrates the confidence of positive toxicity results compared to the average of its class. None of the compounds showed any undesired effects such as tumorigenicity, mutagenicity, irritating, or reproductive effects. Withaferin, however, was found to be relatively toxic among the four candidates, with considerable cytotoxicity and immunotoxicity (Fig. 5).

Table 4.

Toxicity model reports of the top four drug candidates.

Classification Target Prediction and Probability
Asiatic Acid Aviculerin Guajaverin Withaferin
Organ toxicity Hepatotoxicity Inactive (0.91) Inactive (0.80) Inactive (0.80) Inactive (0.93)
Toxicity end points Carcinogenicity Inactive (0.70) Inactive (0.79) Inactive (0.79) Inactive (0.55)
Toxicity end points Immunotoxicity Active (0.77) Active (0.68) Active (0.93) Active (0.99)
Toxicity end points Mutagenicity Inactive (0.81) Inactive (0.73) Inactive (0.79) Inactive (0.79)
Toxicity end points Cytotoxicity Inactive (0.73) Inactive (0.72) Inactive (0.69) Active (0.87)
Tox21-Nuclear receptor signalling pathways Aryl hydrocarbon Receptor (AhR) Inactive (0.99) Inactive (0.85) Inactive (0.90) Inactive (0.98)
Tox21-Nuclear receptor signalling pathways Androgen Receptor (AR) Inactive (0.59) Inactive (0.92) Inactive (0.96) Inactive (0.63)
Tox21-Nuclear receptor signalling pathways Androgen Receptor Ligand Binding Domain (AR-LBD) Inactive (0.51) Inactive (0.98) Inactive (0.97) Inactive (0.54)
Tox21-Nuclear receptor signalling pathways Aromatase Inactive (0.91) Inactive (0.98) Inactive (0.97) Inactive (0.80)
Tox21-Nuclear receptor signalling pathways Estrogen Receptor Alpha (ER) Inactive (0.73) Inactive (0.85) Inactive (0.92) Inactive (0.60)
Tox21-Nuclear receptor signalling pathways Estrogen Receptor Ligand Binding Domain (ER-LBD) Inactive (0.97) Inactive (0.99) Inactive (0.99) Inactive (0.98)
Tox21-Nuclear receptor signalling pathways Peroxisome Proliferator Activated Receptor Gamma (PPAR-γ) Inactive (0.97) Inactive (0.93) Inactive (0.94) Inactive (0.91)
Tox21-Stress response pathways Nuclear factor (erythroid-derived 2)-like 2/antioxidant responsive element Inactive (0.89) Inactive (0.91) Inactive (0.94) Inactive (0.86)
Tox21-Stress response pathways Heat shock factor response element (HSE) Inactive (0.89) Inactive (0.91) Inactive (0.94) Inactive (0.86)
Tox21-Stress response pathways Mitochondrial Membrane Potential (MMP) Inactive (0.85) Inactive (0.89) Inactive (0.89) Inactive (0.80)
Tox21-Stress response pathways Phosphoprotein (Tumor Supressor) p53 Inactive (0.93) Active (0.55) Inactive (0.72) Inactive (0.75)
Tox21-Stress response pathways ATPase family AAA domain-containing protein 5 (ATAD5) Inactive (0.96) Inactive (0.96) Inactive (0.96) Inactive (0.94)

Fig. 5.

Fig. 5

Toxicity patterns of the top four drug candidates; A: Asiatic acid, B: Guajaverin, C: Avicularin, and D: Withaferin.

3.5. Prediction of drug targets and available drug molecules from DrugBank

Most of the target class belonged to enzymes, kinase proteins, oxidoreductases (i.e. aldose reductase, aldo-keto reductase), phosphatases and lyases (i.e. carbonic anhydrase) (Fig. 6 and Table 5 ). Ligand-based virtual screening was performed to predict biologically active small compounds against SARS-CoV-2 from DrugBank. Two approved drugs, Hydrocortisone (DB00741) and Dinoprost-tromethamine (DB01160) were found analogous to asiatic acid with prediction scores of 50.52 and 50.53, respectively. Moreover, results revealed the similarity of Mupirocin (DB00410) and Simvastatin (DB00641) with withaferin, with a high prediction score (Table 6 ). The findings suggest that these could be potential drug candidates against SARS-CoV-2, thus requiring further experimental trials.

Fig. 6.

Fig. 6

Prediction of drug targets for asiatic acid (A), guajaverin (B), avicularin (C), and withaferin (D).

Table 5.

Predicted drug targets for asiatic acid, guajaverin, aviculerin, and withaferin.

Metab-olites Drug Targets Common Name Uniprot ID ChEMBL ID Target Class Probability
Asiatic Acid Aldo-keto reductase family 1 member B10 AKR1B10 O60218 CHEMBL5983 Enzyme Image 2
Protein-tyrosine phosphatase 1B PTPN1 P18031 CHEMBL335 Phosphatase Image 3
11-β-hydroxysteroid dehydrogenase 1 HSD11B1 P28845 CHEMBL4235 Enzyme Image 4
DNA polymerase beta POLB P06746 CHEMBL2392 Enzyme Image 5
T-cell protein-tyrosine phosphatase PTPN2 P17706 CHEMBL3807 Phosphatase Image 6
Phospholipase A2 group 1B PLA2G1B P04054 CHEMBL4426 Enzyme Image 7
Guajaverin & Aviculerin Aldose reductase AKR1B1 P15121 CHEMBL1900 Enzyme Image 8
Carbonic anhydrase II CA2 P00918 CHEMBL205 Lyase Image 9
Carbonic anhydrase VII CA7 P43166 CHEMBL2326 Lyase Image 10
Carbonic anhydrase XII CA12 O43570 CHEMBL3242 Lyase Image 11
Carbonic anhydrase IV CA4 P22748 CHEMBL3729 Lyase Image 12
NADPH oxidase 4 NOX4 Q9NPH5 CHEMBL1250375 Enzyme Image 13
Adrenergic receptor alpha-2 ADRA2C P18825 CHEMBL1916 Family A G protein-coupled-receptor Image 14
Acetylcholinesterase ACHE P22303 CHEMBL220 Hydrolase Image 15
Quinone reductase 2 NQO2 P16083 CHEMBL3959 Enzyme Image 16
Ribosomal protein S6 kinase alpha 3 RPS6KA3 P51812 CHEMBL2345 Kinase Image 17
Neuromedin-U receptor 2 NMUR2 Q9GZQ4 CHEMBL1075144 Family A G protein-coupled receptor Image 18
Withaferin Protein kinase C alpha PRKCA P17252 CHEMBL299 Kinase Image 19
Cyclooxygenase-2 PTGS2 P35354 CHEMBL230 Oxidoreductase Image 20
Isoleucyl-tRNA synthetase IARS P41252 CHEMBL3235 Enzyme Image 21
Protein kinase C delta PRKCD Q05655 CHEMBL2996 Kinase Image 22
HMG-CoA reductase HMGCR P04035 CHEMBL402 Oxidoreductase Image 23
Phosphodiesterase 4D PDE4D Q08499 CHEMBL288 Phosphodiesterase Image 24
Telomerase reverse transcriptase TERT O14746 CHEMBL2916 Enzyme Image 25
Androgen Receptor AR P10275 CHEMBL1871 Nuclear receptor Image 26
Protein kinase C epsilon PRKCE Q02156 CHEMBL3582 Kinase Image 27
Proto-oncogene c-JUN JUN P05412 CHEMBL4977 Transcription factor Image 28
Protein-tyrosine phosphatase 1B PTPN1 P18031 CHEMBL335 Phosphatase Image 29

Table 6.

Predicted bioactive molecules from DrugBank.

Metabolites Drug bank id Name Score Status
Asiatic acid DB00741 Hydrocortisone 0.539 Approved
DB01160 Dinoprost Tromethamine 0.529 Approved
DB07886 (11alpha,14beta)-11,17,21-trihydroxypregn-4-ene-3,20-dione 0.539 Experimental
DB07209 (8R,9Z,12Z)-8-hydroxy-6-oxooctadeca-9,12-dienoic acid 0.510 Experimental
Guajaverin DB08995 Diosmin 0.280 Approved
DB02375 Myricetin 0.236 Experimental
Withaferin DB00410 Mupirocin 0.481 Approved
DB00641 Simvastatin 0.447 Approved
DB08224 hexahydro-7-methyl-8-[2-[(2r,4r)-tetrahydro-4-hydroxy-6-oxo-2h-pyran-2-yl]ethyl]-1-naphthalenol 0.501 Experimental
DB04775 Reidispongiolide C 0.479 Experimental
Avicularin DB08995 Diosmin 0.249 Approved
DB02375 Myricetin 0.210 Experimental

4. Discussion

Excessive infection rates and mortality of SARS-CoV-2 led the researchers to concentrate immensely on developing strategies for combating infections caused by the pathogen [[69], [70], [71]]. Regardless of this praiseworthy initiative, there are still no specific drugs or approved vaccines that could treat SARS-CoV-2 infected patients [72,73]. Though some candidates are in the investigational stages, many of them raise controversial issues [74,75]. Plant-derived natural products play a significant role by being a lead molecule in the development of drug candidates [76]. Hence, in the present study, attempts were taken to evaluate some plant-derived metabolites as inhibitory agents of SARS-CoV-2 based on their binding affinities to the key proteins of the pathogen.

The contribution of computational biology has accelerated the pace of drug discovery [77]. It is now used in the biopharmaceutical industry to discover and develop new lead compounds against many infectious pathogens [77,78]. By this route, one can visualize the possibilities of binding of potential small molecules as ligands/inhibitors [76]. Phytomolecules like Baicalein, Luteolin, Quercetin, and Kaempferol are potential antiviral agents against a wide range of important viruses including Dengue, HIV, H5N1 influenza A virus, Coxsackie virus, CHIKV, and Japanese encephalitis virus [79]. Recent studies have focused on MPP inhibitors of SARS-CoV-2 i.e. alpha-ketoamide, Hydroxy, Remdesivir, Chloroquine and Favipiravir to evaluate their potency as drugs [80,81]. Several in silico strategies were also adopted to screen putative drug candidates against SARS-CoV-2 [82,83]. However, all these experiments used either main protease proteins or RNA-dependent RNA polymerase of SARS-CoV-2 as probable drug targets. In this study, we screened some natural metabolites against SARS-CoV-2 main proteases (6W63, 6LU7), Nsp9 (Non-structural protein-9) RNA binding protein (6W4B), spike receptor binding domain (6M0J), spike ecto-domain (6VYB), and HR2 domain (6LVN) using a molecular docking approach [[84], [85], [86]]. The polyproteins of coronavirus are cleaved and transformed in mature non-structural proteins (Nsp) by proteases [87]. As a putative component in the replication complex, Nsp9 may possibly have an RNA binding activity. Viral replication complexes are frequently membrane associated and Nsp9 helps in this case. The entry of coronavirus into host cells, on the contrary, is mediated by the transmembrane spike glycoprotein that forms homotrimers protruding from the viral surface. S protein comprises two functional subunits responsible for binding to the host cell receptor (S1) and fusion of the viral and cellular membranes (S). After the attachment of the receptor-binding subunit to the receptor, the HR1 and HR2 domains in the membrane fusion subunit interact with each other and form a six-helix bundle, and this conformational change results in a close apposition of the fusion peptide, leading to virus-cell membrane fusion [88]. Thus, all of these proteins represent an attractive pharmacological target for SARS-CoV-2.

Results revealed that asiatic acid had highest binding affinity with SARS-CoV-2 main protease (−53.05 kcal/mol), Nsp9 RNA binding protein (−50.04 kcal/mol) and spike ecto-domain (60.68 kcal/mol) (Fig. 2 and Table 2). Remarkably, four metabolites i.e. asiatic acid, avicularin, guajaverin and withaferin scored best for each six macromolecules and bound with minimum global binding energy (Table 2 and Supplementary File 1). The scores of top candidates were either close or in some instances lower than alpha ketoamide, a positive control used in the present study (Table 2). Asiatic acid, a triterpenoid derivative from Centella asiatica, displayed antioxidative, anti-inflammatory, and protective properties against neurotoxicity induced by glutamate- or b-amyloid previously [89]. Bian et al. also reported the inhibitory activities of asiatic acid and effectivity against fibroproliferative disorders (Keloids) through blocking the TGF-β/Smad pathway [90]. Withanolides are nature-derived secondary metabolites produced in Withania somnifera via oxidation of steroids, which have medicinal value like anti-inflammation, anti-cancer, adaptogenic and anti-oxidant effects [91]. Withaferin, a steroidal lactone from this group, suppresses HIV-1 LTR transcription and viral replication and also has a vital function to inhibit herpes simplex virus [92,93]. It has anti-inflammatory properties and also shows neuro-protective activity against Aβ neurotoxicity [94,95]. A molecular docking and simulation study revealed the vital function of withaferin to attenuate the neuraminidase of H1N1 influenza virus [96].

Guajaverin (Quercetin 3-arabinopyranoside) and avicularin (quercetin- 3-O-α-L-arabinofuranoside) are the main bioactive components of guava leaves with hypoglycemic properties and inhibitory capacity against free fatty acid release [97]. Previously, anti-plaque activity of guajaverine was attributed to its microbicidial activity against the growth of Strep [38]. Avicularin, a flavonoid of plants, displayed diverse pharmacological properties such as anti-inflammatory effects and anti-infectious effects against pathogens [98,99]. Lee et al. reported the effective anti-oxidant potentiality of Avicularin from Lespedeza cuneata [100]. Researchers also identified hepatoprotective activity of avicularin extracted from the aerial parts of Lespedeza cuneata against lesions caused by t‐BHP in HepG2 cells [101]. It has also been suggested to inhibit activation of ERK signaling pathways through LPS-stimulated overproduction of pro-inflammatory mediators and cytokine [98]. Avicularin may suppress the inflammatory response, and causes apoptosis in human RA synovial cells through obstructing the activation of the MEK/NF-κB pathway, thus preventing rheumatoid arthritis (RA) in vitro [102].

In the present study, we revealed the molecular interactions of top drug candidates with SARS-CoV-2 key proteins (Fig. 2 and 3 and Table 2). The binding sites for each ligand occupied the catalytic domain of SARS-CoV-2 main protease protein [103]. Among the common binding residues, His41 and Cys145 form the catalytic dyad and act as a substrate recognition site [103,104]. The top candidates were well fitted into the active pocket of MPP where several hydrophobic amino acid residues including Met49, Gly143, Cys145, Met165, Pro168, Ala191 compose a relatively hydrophobic environment, which may help to stabilize its conformation [104]. The crucial binding sites of Nsp9 protein (39–73 region) are characterized by positively charged, glycine rich β-loops, which were proposed to be involved in RNA binding [105]. Moreover, we targeted three distinct domains of SARS-CoV-2 spike protein, all of which play essential roles in the mechanism of viral entry into the host cell [106]. The investigation may be useful to unravel the main drug target hotspot and medicinal chemistry of the investigational drugs currently under trials against SARS-CoV-2. ADME data, whether experimentally measured or computationally predicted, provide key insights into how a drug will ultimately be treated or accepted by the body. Hence, while a drug lead may exhibit phenomenal efficacy in vitro, poor ADME results often invariably terminate its development [107]. Computational methods play a key role in anticipating potential ADME and toxicity problems and reducing the number of experiments that involve animal testing. Therefore, the topmost drug candidates were employed for ADME analysis to investigate their drug profiles. None of the metabolites, however, showed any undesirable consequences that could reduce their drug likeness properties. SARS-CoV-2 appears as a severe acute respiratory disease not a neuro disease [108]. Thus, there is no need to permeate the blood brain barrier (BBB) for being an effective molecule against SARS-CoV-2. However, no BBB permeants were found among the top drug candidates. Most of the target class for the top drug candidates belonged to the categories of enzymes (e.g. oxidoreductases, hydrolase, phosphatases, lyases (Table 5). The major protease proteins (protein hydrolase) of SARS-CoV-2 thus can be a specific target for these natural metabolites. Guajaverin and avicularin are two isomers and derivatives of quercetin with a glycoside substituent in their chemical structure [109,110]. The significant similarity between these two polar compounds in terms of structure, chemical formula, molar mass, and other physicochemical parameters (Table 3) may be responsible for covering the same targets by these two flavonoids (Table 5). The toxicity of drug impurities is closely related to their structure. Structure-activity relationships (SARs) have been widely used in Europe and the United States to predict toxicity by computer [111]. The toxicity prediction results in the present study revealed negligible tumorigenic, mutagenic, irritating, or reproductive effects by the drug candidates, though withaferin was found to be comparatively toxic among the top four compounds. Despite having medicinal importance [112,113], the ability of withaferin to inhibit cell growth and induce apoptosis in in vitro and in vivo models were reported [114,115]. Moreover, dose dependent toxicity and other adverse effects such as elevation of liver enzymes, skin rash, fever etc. were observed by researchers [115,116]. Our study also revealed the hepatotoxic and cytotoxic nature of withaferin through computational investigations.

However, drug similarity prediction identified two approved structural analogs of withaferin, Mupirocin (DB00410) and Simvastatin (DB00641) which could be alternative choices, and therefore require further in vivo investigations. Ligand-based virtual screening using asiatic acid predicted two other biologically active compounds, Hydrocortisone (DB00741) and Dinoprost-tromethamine (DB01160) from DrugBank. Interestingly, Hydrocortisone, a cortisone based drug, was previously used during the SARS-CoV-1 and MERS outbreak [117]. Diosmin, on the contrary, is used as a supplementary drug and is found in various natural plants [118]. Myricetin showed the potential to inhibit reverse transcriptase of the RLV and HIV viruses, while characterized as having antioxidative and prooxidative properties. It is also a potent anticarcinogen and antimutagen [119]. The most significant finding of this study is Simvastatin, which can block downstream molecules (key factors in virus infectivity) and can control severe influenza and pneumonia through prevention of excess cytokine release [120]. The results suggest that all these compounds could be potential drug candidates against SARS-CoV-2. The study may pave the way to develop effective medications and preventive measures against SARS-CoV-2 in the future.

5. Conclusion

The results suggest that asiatic acid, avicularin, and guajaverin could be options to treat SARS-CoV-2 associated infections. Furthermore, two biologically active structural analogs from DrugBank i.e. Hydrocortisone and Simvastain may be effective and show potency against the viral pathogen. However, all the investigational drugs of SARS-CoV-2 are under strict regulation by the World Health Organization. Due to the encouraging results, we highly recommend further in vivo trials for the experimental validation of our findings.

Declaration of competing interest

The authors declare that they have no conflict of interests.

Acknowledgements

The authors would like to acknowledge the Department of Microbial Biotechnology, Department of Pharmaceuticals and Industrial Biotechnology, and the Department of Plant and Environmental Biotechnology of Sylhet Agricultural University, Sylhet-3100, Bangladesh for the technical support provided for this research work.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.imu.2020.100367.

Funding information

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (20KB, docx)

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