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Article

Molecular Screening of Bioactive Compounds of Garlic for Therapeutic Effects against COVID-19

by
Huma Ashraf
1,
Erum Dilshad
1,*,
Tayyaba Afsar
2,
Ali Almajwal
2,
Huma Shafique
3 and
Suhail Razak
2,*
1
Department of Bioinformatics and Biosciences, Faculty of Health and Life Sciences, Capital University of Science and Technology (CUST), Islamabad 44000, Pakistan
2
Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia
3
Institute of Cellular Medicine, Newcastle University Medical School, Newcastle University, Newcastle NE1 7RU, UK
*
Authors to whom correspondence should be addressed.
Biomedicines 2023, 11(2), 643; https://doi.org/10.3390/biomedicines11020643
Submission received: 30 January 2023 / Revised: 13 February 2023 / Accepted: 17 February 2023 / Published: 20 February 2023
(This article belongs to the Section Immunology and Immunotherapy)
Browse Figures
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Abstract

:
An outbreak of pneumonia occurred on December 2019 in Wuhan, China, which caused a serious public health emergency by spreading around the globe. Globally, natural products are being focused on more than synthetic ones. So, keeping that in view, the current study was conducted to discover potential antiviral compounds from Allium sativum. Twenty-five phytocompounds of this plant were selected from the literature and databases including 3-(Allylsulphinyl)-L-alanine, Allicin, Diallyl sulfide, Diallyl disulfide, Diallyl trisulfide, Glutathione, L-Cysteine, S-allyl-mercapto-glutathione, Quercetin, Myricetin, Thiocysteine, Gamma-glutamyl-Lcysteine, Gamma-glutamylallyl-cysteine, Fructan, Lauricacid, Linoleicacid, Allixin, Ajoene, Diazinon Kaempferol, Levamisole, Caffeicacid, Ethyl linoleate, Scutellarein, and S-allylcysteine methyl-ester. Virtual screening of these selected ligands was carried out against drug target 3CL protease by CB-dock. Pharmacokinetic and pharmacodynamic properties defined the final destiny of compounds as drug or non-drug molecules. The best five compounds screened were Allicin, Diallyl Sulfide, Diallyl Disulfide, Diallyl Trisulfide, Ajoene, and Levamisole, which showed themselves as hit compounds. Further refining by screening filters represented Levamisole as a lead compound. All the interaction visualization analysis studies were performed using the PyMol molecular visualization tool and LigPlot+. Conclusively, Levamisole was screened as a likely antiviral compound which might be a drug candidate to treat SARS-CoV-2 in the future. Nevertheless, further research needs to be carried out to study their potential medicinal use.

1. Introduction

An outbreak of pneumonia pandemic occurred in China, in December 2019, which caused a serious public health emergency by spreading around the globe. Finally, it was officially announced on 9 January 2020 that the outbreak in Wuhan is caused by a novel coronavirus 2019-nCoV. On 11 March, COVID-19 was declared a pandemic disease by the WHO, as it is easily transferable from one to another human. Globally, nearly 1.9 million new cases and over 12,000 deaths were reported in the week of 16 to 22 January 2023 [1]. The unique coronavirus was named Severe Respiratory Disease [2]. Coronaviruses (CoVs) are responsible for causing infection in humans as well as in animals, and they cause several other diseases related to respiratory issues. These spreader viruses are grouped into alpha, beta, gamma, and delta variants, with a new Omicron variant appearing recently [3].
The pandemic which occurred in the period 2002–2003, in China and Asia Pacific regions, was caused by SARS-CoV and infected more than 8000 people around the globe, with a 10% mortality rate. Fever, cough, and lowering of oxygen level in blood were the common symptoms that were seen in patients suffering from the illness [4]. The sequence similarity of the SARS-CoV-2 virus is nearly 80% when compared with the SARS-CoV virus, but the coronavirus is much more severe and dangerous [5]. Whole-genome sequencing revealed that coronavirus is more related to bat CoV RaTGI3, with a 96.2% sequence similarity.
The virus transmits by contact in any form from one to another human being, i.e., direct or indirect contact [6]. Approximately 2–14 days is the incubation period of the virus. Moreover, few infected people are asymptomatic, which means no symptoms of the disease are shown. COVID-19 is mostly not so severe; sometimes, patients experience health issues such as hypertension, diabetes, immunodeficiency, etc. For such patients, multi-organ failure may occur in case of severe conditions which can cause death [4].
Globally, for the treatment of COVID-19, without having proof of inflammation’s role in the cure of the illness, many immune modulators, such as glucocorticoids and anti-inflammatory therapies are being used for this purpose. The major determinant in the host’s survival is dependent on the host’s ability to clear the viral infection in the lung, which provides an advantage to the host by aiding effective viral clearance [7]. The pathophysiological mechanism of COVID-19 is not well understood, and several pieces of evidence have revealed that COVID-19-infected patients have high levels of cytokine and are referred to as cytokine storm or cytokine release syndrome. This abnormal rise in cytokine level is considered a severe decline in health conditions in the infected patients. Thus, the severity of disease in COVID-19-infected patients can be reduced by suppressing elevated inflammatory response [8].
Effective measures, such as vaccines [9], small-molecule inhibitors [10], and bioactive natural products [11,12], are greatly needed to reduce SARS-CoV-2 transmission. However, promising drugs for SARS-CoV-2 treatment do not exist [11]. As a key component of the COVID-19 treatment regimen, transactional medicine [13], including garlic, may demonstrate potential value in countering SARS-CoV-2 infection. This is because the secondary metabolites that are present in medicinal plants can prevent viral penetration and replication by binding with viral proteins and enzymes. Plants contain a lot of bio-active compounds and essential oils which are beneficial for human health. Another factor that increased the demand for chemical-free herbal drugs was the toxic and adverse effects of allopathic medicines. The problem is that the distribution of medicinal plants is not the same worldwide, and usually, medicinal herbs are collected from wildlife populations [14]. Moreover, the demand for wildlife resources in Europe, North America, and Asia has increased from 8 to 15% per year. Naturally occurring spices and their isolated active components have been reported for targeting anti-inflammatory pathways, inducing anti-inflammatory effects in several life-threatening ailments. Spices and herbs are thought to be excellent immunity boosters; therefore, they are prevalently used in Asian countries [15].
The viral main proteinase (Mpro, also called 3CLpro), which controls the activities of the coronavirus replication complex, is an attractive target for therapy [16]. The objective of the current study was to screen the bioactive compounds of the Allium sativum plant, effective against COVID-19 by determining their binding confirmation with the target protein (3CL protease) of COVID-19. An additional aim was to study the interaction between targeted proteins and the selected ligands computationally.

2. Materials and Methods

2.1. Selection of Protein

3CL protease of COVID-19 was selected as the target protein for the current study. The structure of the SARS-CoV-2 selected protein was retrieved from Protein Data Bank (PDB ID: 6M2Q) in .pdb format [16].

2.2. Primary Sequence Retrieval

The primary sequence of target proteins was taken in FASTA format from the protein sequence database UniProt (http://www.uniprot.org accessed on 12 February 2023 (PRJNA318322)) [17].

2.3. Analysis of Physiochemical Properties

This was to determine the function of proteins. ProtParam was used to predict the physicochemical parameters of SARS-CoV-2 protein including molecular weight, number of amino acids, isoelectric point, instability index, and grand average of hydropathicity (GRAVY). ProtParam tool of ExPASy was used to determine the negatively charged residues (Asp + Glu), positively charged residues (Arg + Lys), aliphatic index, and atomic composition [18].

2.4. Identification of Functional Domains

Interpro (https://www.ebi.ac.uk/interpro/D1/D344/5958491 accessed on 12 February 2023) was used to detect and predict the functional domain of targeted protein. Conserved domains are involved in sequence/structure/relationship study. InterPro provides a practical analysis of proteins by classifying them into families and predicting domains and active sites [19].

2.5. Active Site Identification

The ligand shows maximum or highest interaction with the protein where the target protein has its active site. Amino acids are highly involved in the formation of a complex of ligands to protein. Protein binding pockets were identified by CASTp [19].

2.6. Ligand Preparation

The 3-dimensional (3D) structure of ligands was obtained from PubChem. PubChem is the world’s largest collection of freely available chemical information. We can search several ligands by their names, molecular formula, and structure and by other information. If the targeted structure is not available in PubChem, then can be drawn via ChemDraw by inserting Canonical smileys derived from PubChem [16].

2.7. Bioactivity Analysis of Ligands and Toxicity Measurement

Selected ligands from the PubChem database should follow the Lipinski rule of five, having required chemical and physical properties. This was performed by using PkCSM (https://omictools.com/pkcsmtool/database/id/1618 accessed on 12 February 2023) [18].

2.8. Molecular Docking Process

The purpose of molecular docking is to find the best conformational interaction between target proteins and compounds. Molecular docking of protein and ligands was performed through Cavity detection-guided Blind Docking (CB-Dock) [20].

2.9. Visualization of Ligand/Protein

Docked complex of ligands and protein was visualized by PyMol. Docking poses generated via CB-Dock were visualized and saved as a molecule in .pdb form in one file for further analysis [18].

2.10. Analysis of Docked Complex

Analysis of docked complex was performed by using LigPlot+, which automatically generates schematic diagrams of protein–ligand interactions for a given PDB file. These interactions are modified by hydrogen bonds and through hydrophobic contact [18].

2.11. Ligand ADMET Properties

The main aim of predicting ADMET is to choose strong candidates by eliminating weak drug candidates in the early stages of drug development. Optimization of the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties of the drug molecule was performed by using PkCSM [21].

2.12. Active Inhibitor Identification

After a detailed analysis of protein and ligand interactions, docking scores, and toxicity studies, the most active inhibitor was identified. The selected compound was our lead compound [19].

2.13. FDA-Approved Drug-Proposed Antiviral Agent Comparison

Finally, the comparison was made between the selected antiviral drug Remdesivir and the proposed antiviral agents by comparing all the parameters described above [22].

3. Results and Discussion

3.1. Target Proteins Structure and Properties

The primary sequence of the target protein (3CL Protease) was taken in FASTA format from the UniProt database under accession numbers P0DTD1 with 7096 residues length. The 3D structure of 3CLpro of SARS-CoV-2 was obtained from Protein Data Bank (PDB ID: 6M2Q) in .pdb format [16]. Physiochemical properties of 3CL protease were determined by ProtParam under accession No. [A0A6C0M8P6-SARS2]. In physicochemical parameters of selected protein 3CLpro of SARS-CoV-2, Mol. weight, atomic composition, isoelectric point, no. of amino acids, instability index, grand average of hydropathicity (GRAVY), No. of negatively charged residues (Asp + Glu), No. of positively charged residues (Arg + Lys), Aliphatic index, and amino acid and atomic composition were included, and these properties were investigated using the ProtParam ExPASy tool. InterPro (https://www.ebi.ac.uk/interpro/14231 accessed on 12 February 2023) was used to identify active domains of 3CLpro domains [16]. The selected target protein is shown in Figure 1.
SARS-CoV-2 is the virus which is responsible to cause COVID-19 and up till now there is no proper treatment for this pandemic which has affected the world. To know about the virus, it is compulsory to obtain information about the structure of the involved virus. So, this structure (Figure 1) can be understood in a better way. Accordingly, 3CL protease comes from the class of highly conserved viruses, and it is now the target of broad-spectrum antiviral drugs which kill the virus as it is the site of replication of the virus [23]. In the early studies on the SARS-CoV-2 models, Mpro shows a close relation to the main proteases named coronaviral in terms of structure: 99% of the amino acid structure is common to bat CoV RaTG13 Mpro and 97% similar to SARS CoV Mpro [24].
The functional analysis of protein sequences was obtained by Interpro to determine the conserved and functional domain and sequence/structure/relationship. More than one functional domain can be there to perform different functions [19]. These conserved domains and families of the target protein are shown in Figure 2.
Figure 3 shows functional domains and pockets present in red color along with the structure of the protein. Moreover, Table 1 shows the area and volume of these pockets which were obtained by using CASTp software.

3.2. Ligand Selection and Molecular Docking

The 3D structures and information of selected ligands that are 3-(Allylsulphinyl)- L-alanine, Allicin, Diallyl sulfide, Diallyl disulfide, Diallyl trisulfide, Glutathione, L-Cysteine, S-allyl-mercapto-glutathione, Thiocysteine, gamma-glutamyl-Lcysteine, gamma-glutamyl allyl cysteine, Quercetin, myricetin Kaempferol, Fructan, Lauric acid, Linoleic acid, Allixin, Ajoene, diazinon, levamisole, caffeinated, Ethyl linoleate, Scutellarein, and S-allyl cysteine methyl ester were downloaded from PubChem in SDF format [25].
The 3D structure of the target proteins and the ligands was taken as the input for docking, which was performed by the CB dock [21]. The CB dock gave possible poses with receptor models, and among these poses, the best one was selected by observing certain properties such as vena score, size of cavity, etc. [19]. The CB Dock also projected the predictable binding site for protein and premeditated centers and sizes with an innovative rotation cavity detection method and performed docking with the popular docking program known as Auto dock Vina [19]. So, the obtained data is given in Table 2, which shows the minimum and maximum energy, cavity size, binding score, and grid map of ligands.
LigPlot+ (version v.1.4.5) and PyMol Edu (v1.7.4.5) were used for analyzing docking results. LigPlot+ (version v.1.4.5) also determined the interactions of ligands and target proteins [26]. The graphical system of LigPlot+ automatically generates multiple 2D diagrams of interactions from 3D coordinates. The 2D diagrams of the best binding score ligands with respective proteins were obtained from LigPlot+, shown in Figure 4A–P. As evident from the 2D diagram, ligands show only hydrophobic interactions with the protein.
The ligand consisted of 10 carbons and showed hydrophobic interactions with Pro132, Pro293, Pro108, Thr292, Gly109, Ile200, Ile249, Glu240, His246, Val202, and Phe294 residues, and it included Allicin, Diallyl Sulfide, Diallyl Disulfide, Diallyl Trisulfide, levamisole, diazinon, thiocysteine, gamma-glutatmylS-allyl cysteine, and ajoene. These ligands were without hydrogen bonds, as it is evident from the 2D structures they are mostly without active oxygen atoms. S-allyl cysteine methyl ester, ethyl linoleate, and linoleic acid had one hydrogen bond. S-allylmercapto-glutathiole, caffeic acid, scutellarein, allixin, lauric acid, and quercetin had two hydrogen bonds. L-cystein had 3 hydrogen bonds, whereas 3-L-alanine, kaempferol, and glutathione had 4 hydrogen bonds. Maximum hydrogen bonds are shown by fructan, myricetin, and gamma-glutamyl-L-cysteine as five hydrogen bonds each [27,28].

3.3. ADMET Properties of Ligands

Lipinski’s five drug laws, when applied, served as a first filter in assessing the drug likelihood of the selected ligands. In our thesis, 25 different ligands were taken, and when filtered by different software, few were left. So, when Lipinski’s rule of five was applied, Myricetin, Fructan, Linoleic Acid, Ethyl Linoleate, Glutathione, and S-Allyl-Mercapto-Glutathione were knocked out as shown in Table 3.
Further, ligands were screened by calculating the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties as a measure of pharmacokinetics using the online tool PkCSM [29]. ADMET properties are shown in Table 4, Table 5, Table 6, Table 7 and Table 8, respectively.

4. Lead Compounds Identification

Myricetin, Fructan, Linoleic Acid, Ethyl Linoleate, Glutathione, and S-Allyl-Mercapto-Glutathione were knocked out from Lipinski’s rule of five. 3-(Allylsulphinyl)-L-Alanine, Scutellarein, Diazinon, Glutathione, L-Cysteine, S-Allyl-Mercapto-Glutathione, Thiocysteine, Kaempferol, Quercetin, Myricetin Fructan, Lauric Acid, Linoleic Acid, Allixin, Gamma-Glutamyl-L-Cysteine, Gamma-Glutamyl-S-Allylcysteine, S-allylcysteine methylester, and Caffeic Acid had a logBB value > 0.3. 3-(Allylsulphinyl)-L-Alanine, Scutellarein, Diazinon, Glutathione, L-Cysteine, Gamma-Glutamyl-S-Allylcysteine, S-allylcysteine methylester, Caffeic Acid, S-Allyl-Mercapto-Glutathione, Thiocysteine, Gamma-Glutamyl-L-Cysteine, Kaempferol, Quercetin, Myricetin Fructan, Lauric Acid, Linoleic Acid, Allixin, and Ethyl Linoleate had a logBB value > 0.3 and logPS value > −2.
Linoleic Acid and Ethyl Linoleate have a logPS value > −2. So, the lig can be identified as lead compounds. The best five compounds were Allicin, Diallyl Sulfide, Diallyl Disulfide, Diallyl Trisulfide, Ajoene, and Levamisole. The lead compound of this research work was Levamisole, as is also indicated by molecular docking [20].

5. Drug Identification against COVID-19

With the emergence of the disease, many FDA-approved drugs were utilized for drug repurposing, finding the best treatment against the virus. One of the drugs that has been in use in different countries such as the UK, Brazil, India, Pakistan, and many more is Remdesivir. Though the use of this medicine has been increased during this whole pandemic, this drug is still in clinical trials [22]. The first FDA-approved drug to treat SARS-CoV-2 is Remdesivir, which is an antiviral nucleotide analogue prodrug [22]. Because of its broad-spectrum nature and mechanism of action against various viral families, it is suggested to the patients. This medicine is a non-obligate chain terminator of RdRp from SARS-CoV-2 and the related SARS-CoV and MERS-CoV, and it has been investigated and suggested in many different clinical trials against COVID-19 [30].

6. Reference Drug ADMET Properties

The drug ADMET properties were studied by using the same software as above, which is PkCSM.

6.1. Absorption Properties

Table 9 shows the absorption properties of Remdesivir. The values show that Remdesivir shows a very low CaCO2 solubility and water solubility. Though intestinal absorption is high, it is still in the safe range. Remdesivir also has a lower value of skin permeability. Remdesivir is also a P-glycoprotein substrate and an inhibitor of P-glycoprotein I but not a P-glycoprotein II inhibitor.

6.2. Distribution Properties

Table 10 shows the distribution properties of Remdesivir. The distribution parameters value shows that the value of VDss is low, which means the drug would not be distributed properly. Remdesivir can penetrate in CNS and also can pass the blood–brain barrier.

6.3. Metabolic Properties

Table 11 shows the metabolic properties of Remdesivir. It indicates that Remdesivir is not a CYP2D6 substrate; rather, it is a CYP3A4 substrate. With those, Table 11 shows that Remdesivir is not a CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4 inhibitor.

6.4. Excretion Properties

Table 12 shows the excretion properties of Remdesivir. The above table gives the values of the Excretory properties of Remdesivir. It shows that Remdesivir is not a renal OCT2 substrate, which means it will not help in clearing the drug. With that, the value of total clearance as 0.198 is also given with respect to its liver.

6.5. Toxicity Prediction of Reference Drug

Table 13 shows the Toxicity Properties of Remdesivir. The toxicity parameters value of Remdesivir shows that this drug can be toxic towards the liver, but other parameters are in the range of positive values.
This indicates that Remdesivir can cause any skin sensitivity, and it also is not an inhibitor of hERG I but an hERG II inhibitor. The dose value of 0.291 is also tolerable. With that, a no to AMES toxicity indicates that it is not carcinogenic.

7. Remdesivir Molecular Docking

Table 14 shows the docking result of Remdesivir. The table indicates that Remdesivir has a binding score of −8.1. The docking results of Remdesivir show that it has quite a good binding score. Additionally, has four hydrogen bond donors and thirteen hydrogen bond acceptors that break two of Lipinski’s rules, as the molecular weight is above 500 g/mol.

8. Remdesivir Comparison with Lead Compound

The standard drug Remdesivir was compared with the lead compound Levamisole and its physicochemical and pharmacokinetic properties. Table 15 shows that Remdesivir breaks two of Lipinski’s rules relating to molecular weight and H-bond acceptor: the molecular weight of Remdesivir is 602.585, which is greater than the 500 allowed according to Lipinski, and the H-bond acceptor of Remdesivir accepts 13 hydrogens, but according to Lipinski, it should not be more than 10; in contrast, Levamisole follows all rules of LogP, Molecular weight, H-bond acceptor, and H bond donor according to Lipinski.

9. ADMET Properties Comparison

The ADMET properties comparison was performed to check the absorption, distribution, metabolic excretion, and toxicity properties of the drug and the lead compound, in order to find a better drug candidate.

9.1. Absorption Properties Comparison

The parameter of absorption is based on 6 models. The water solubility model gives the value of the compound’s solubility in the water at 25‰. A model of CaCO2 solubility is used to detect the absorption of the drug. Values greater than 0.90 are considered to have high intestinal absorption, which means Levamisole is absorbed more than Remdesivir. The value of the intestinal absorption model is less than 30%, which means the drug is not well absorbed. The given values of both the standard and lead compound show that Levamisole has high intestinal absorption.
For the transdermal drugs skin permeability model, a value less than log Kp > −2.5 is considered low; according to this, both compounds pass the skin permeability test. The P-glycoprotein substrate model is very important, as P-glycoprotein is an ABC transporter. Both Levamisole and Remdesivir act as substrates. The last model of P-glycoprotein inhibitors shows whether the compound is an inhibitor or not [31]. Table 16 shows that Levamisole is an inhibitor of P-glycoprotein II, whereas Remdesivir is the inhibitor of P-glycoprotein I.

9.2. Metabolic Properties Comparison

Cytochrome P450 is mainly found in the liver and is held responsible for oxidizing the xenobiotic so that they can be excreted easily out from the body, hence making cytochrome P450 a detoxification enzyme. Some drugs are activated by it, and some are deactivated [32]. Table 17 shows that Remdesivir is a CYP3A4 substrate, and Levamisole is a CYP3A4 substrate and CYP2D6 inhibitor.

9.3. Distribution Properties Comparison

Table 18 shows the comparative distribution properties of Remdesivir and Levamisole. The distribution parameter is based on 4 models. The volume of distribution (VDss) is a uniform distribution of the drug in the blood plasma, and if this value is above 2.81 L/kg, then the drug is distributed more in the tissues rather than in the blood plasma. Both Remdesivir and Levamisole have a reasonable VDss value. The 2nd model is based upon the unbound fraction of the drugs in the plasma, as bounded drugs affect the efficiency of the drugs. The given value is the amount of the drug which remains unbounded [33]. For BBB permeability, if the value is greater than 0.3 logBB, then that drug can easily cross the blood–brain barriers, and if the value is less than −1 logBB, then the drug does not properly reach the brain [34]. By these values, it is clear that Remdesivir has a low value; hence, it would be poorly distributed to the brain. Similarly, the model for CNS is based on the values that if the logPS > −2, then that drug can easily penetrate the CNS, while those having value of logPS < −3 are unable to reach the CNS. Remdesivir has a low value, and hence, it will not cross and reach the CNS.

9.4. Excretion Properties Comparison

Levamisole has more total clearance than Remdesivir. The 2nd model is of the Renal OCT2 (organic cation transporter 2), and this transporter helps in renal clearance. Being an OCT2 substrate, it can show an adverse effect in correlation with inhibitors [35]. So, both Remdesivir and Levamisole are not Renal OCT2 substrates. Table 19 shows the values of excretory properties of Remdesivir and Levamisole.

9.5. Toxicity Comparison

The toxicity of both the standard drug and lead compound was based on nine models. Model 1 of AMES toxicity shows that both the standard and lead compounds are not mutagenic. Model 2 of the maximum tolerated dose shows that if the value is equal to or less than 0.477 log mg/kg/day, then it is considered low, and greater values are considered high.
Table 15 below shows that Levamisole has a low value of the tolerated dose. The 3rd model is of hERG I and II inhibitors, where only levamisole is an inhibitor of both, while remdesivir inhibits only II inhibitors. The 4th model of oral rat acute toxicity is used to assess the relative toxicity. Model 5 of oral rat chronic toxicity gives the values of the lowest dose that could result in an adverse effect [34].
Model 6 of hepatotoxicity shows that the drug can cause damage to the liver. Table 20 shows that Remdesivir is hepatotoxic. For the dermal products model, Model 7 is used for checking the sensitivity towards the skin. Both the standard and lead compounds are not sensitive to skin. Model 8 uses T. pyriformis, and Model 9 uses minnows to check the toxicity [36].
For T. pyriformis, a value > −0.5 is considered toxic, according to which Remdesivir is somewhat toxic, and minnow toxicity values below 0.5mM are considered toxic, and both compounds pass this toxicity test. Table 20 shows the comparative values of toxicity of Remdesivir and Levamisole.

9.6. Physiochemical Properties Comparison

For determining the fundamental properties of the compounds, physiochemical properties were studied. This screening shows that Remdesivir has 27 carbon atoms, 35 hydrogen atoms, 6 nitrogen atoms, 8 oxygen atoms, and a phosphorous atom, whereas Levamisole has 11 carbon atoms, 12 hydrogen atoms, 2 nitrogen atoms, and Sulphur. Remdesivir can donate 4 hydrogen atoms, whereas Levamisole cannot donate hydrogen.
Remdesivir can accept 13 Hydrogen atoms which do not fall under the Lipinski rule. Although the Log P value of Remdesivir is more than that of Levamisole, the molecular weight of Remdesivir is far greater than Levamisole, and also, it does not fall under the Lipinski rule. Table 21 shows the comparison of the physiochemical properties of Remdesivir and Levamisole.

9.7. Docking Score Comparison

Both the standard and the lead compound were docked, and the docking result gives us the best binding score. Table 22 shows that the lead compound Levamisole, which has a much higher Vina score than that of the standard drug, which is Remdesivir. The binding score of Remdesivir is −8.1 and that for Levamisole is −5.7, which is higher. This result shows that Levamisole can block the 3CL pro or bind with it more efficiently than can Remdesivir.

10. Conclusions

The motive of the present research was to discover potential antiviral components from Allium sativum. Twenty-five phytocompounds (which represent almost all classes of natural antiviral compounds) were selected from the literature and databases. Molecular docking was performed by CB-dock, an online tool against 3CL protease of COVID-19 and the five best-scoring phytocompounds were identified as hit compounds. Physicochemical and pharmacokinetic properties determined the final destiny of compounds as drug or non-drug compounds. Levamisole was predicted as a lead compound by virtual screening results. As per the results of this research, the lead compound, Levamisole, can be explored as an important candidate to cure viral infections, especially COVID-19. These potential antiviral compounds of Allium sativum can also be tested for the pharmaceutical and medical industries.

Author Contributions

H.A., E.D. and S.R. together designed the research project. H.A. and E.D. performed the experiments, and T.A. and S.R. analyzed the data along with A.A., S.R., T.A., E.D. and H.S. and A.A. wrote the manuscript. E.D. and S.R. were co-supervisors, and A.A. provided technical assistance. H.S. improved the intellectual context of the research article. H.S., A.A. and S.R. supervised the project and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

The project was funded by the researchers supporting project number (RSP2023R502), King Saud University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

Approval for the study was obtained from the Institutional Review Board of Capital University of Science and Technology (CUST) (C56A123), Islamabad, Pakistan.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analyzed during this study are included in this article.

Acknowledgments

The authors extended their appreciation to the Researchers Supporting project number (RSP2023R502), as King Saud University, Riyadh Saudi Arabia, for funding this project.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

COVID-19: Coronavirus Disease; CoVs: Corona Viruses; GM: Genetically Modified; MERS: Middle East respiratory syndrome; RNA: Ribonucleic Acid; SARS: Severe Acute Respiratory Syndrome; WHO: World Health Organization; CNS: Central Nervous System; OCT2: Organic Cation Transporter 2.

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Biomedicines 11 00643 g001 550
Figure 1. Structure of SARS-CoV-2 3CL protease (3CL pro).
Figure 1. Structure of SARS-CoV-2 3CL protease (3CL pro).
Biomedicines 11 00643 g001
Biomedicines 11 00643 g002 550
Figure 2. Conserved domains of the target protein 3CL protease.
Figure 2. Conserved domains of the target protein 3CL protease.
Biomedicines 11 00643 g002
Biomedicines 11 00643 g003 550
Figure 3. Functional domains of the target protein.
Figure 3. Functional domains of the target protein.
Biomedicines 11 00643 g003
Biomedicines 11 00643 g004a 550Biomedicines 11 00643 g004b 550
Figure 4. Interactions of (A) Ajoen, (B) Allixin, (C) diallyl disulfide, (D) linoleate, (E) Gamma-Glutamyl-S-Allylcysteine, (F) lauric acid, (G) l-cystein, (H) Myrecetin, (I) 3-(Allylsulphinyl)-L-Alanine, (J) allicin, (K) caffeic acid, (L) diallyl sulfide, (M) diazinon, (N) fructan, (O) Gamma-Glutamyl-L-Cysteine, (P) Lauric acid, (Q) Levimisole, (R) Linoleic acid, (S) quercetin, (T) S-allylcysteine, (U) S-allyl-mercapto-glutathione, (V) scutellarein, (W) glutathonine, and (X) f diallyl trisulfide with target protein obtained by LigPlot+.
Figure 4. Interactions of (A) Ajoen, (B) Allixin, (C) diallyl disulfide, (D) linoleate, (E) Gamma-Glutamyl-S-Allylcysteine, (F) lauric acid, (G) l-cystein, (H) Myrecetin, (I) 3-(Allylsulphinyl)-L-Alanine, (J) allicin, (K) caffeic acid, (L) diallyl sulfide, (M) diazinon, (N) fructan, (O) Gamma-Glutamyl-L-Cysteine, (P) Lauric acid, (Q) Levimisole, (R) Linoleic acid, (S) quercetin, (T) S-allylcysteine, (U) S-allyl-mercapto-glutathione, (V) scutellarein, (W) glutathonine, and (X) f diallyl trisulfide with target protein obtained by LigPlot+.
Biomedicines 11 00643 g004aBiomedicines 11 00643 g004b
Table
Table 1. Area and volume of the obtained pockets by CASTp.
Table 1. Area and volume of the obtained pockets by CASTp.
Pocket IDArea (SA)Volume (SA)
1284.664292.690
2273.913214.993
353.18959.074
4104.30630.390
540.51425.655
627.4587.991
720.6347.083
813.6714.642
96.8173.399
1016.5443.174
1110.8962.007
1210.6761.878
136.6641.663
146.5280.747
154.2170.743
166.8180.470
174.6370.424
184.0310.356
191.0040.163
201.7700.100
210.9930.038
220.7440.020
230.2650.006
240.1030.002
250.0710.001
260.0460.000
270.0010.000
280.0220.000
290.0000.000
300.0130.000
310.0420.000
Table
Table 2. Results of CB dock of selected ligands with the target protein.
Table 2. Results of CB dock of selected ligands with the target protein.
Sr.
No		LigandBinding ScoreCavity SizeGrid MapMin-Energy
(Kcl/mol)		Max-Energy
(Kcl/mol)
13-(Allylsulphinyl)-L-Alanine−4.813852601.6 × 100
2Allicin−3.213852601.6 × 100
3Diallyl Sulfide−3.113852601.6 × 100
4Diallyl Disulfide−3.513852601.6 × 100
5Diallyl Trisulfide−5.52772101.6 × 100
6Glutathione−5.82772101.6 × 100
7L-Cysteine−3.713852601.6 × 100
8S-Allyl-Mercapto-Glutathione−62772201.6 × 100
9Thiocysteine−3.813852101.6 × 100
10Gamma-Glutamyl-L-Cysteine−7.213852401.6 × 100
11Gamma-Glutamyl-S-Allylcysteine−5.413852101.6 × 100
12Kaempferol−7.413852601.6 × 100
13Quercetin−7.613852101.6 × 100
14Myricetin−7.813852101.6 × 100
15Fructan−7.113852101.6 × 100
16Lauric Acid−5.213852201.6 × 100
17Linoleic Acid−5.713853001.6 × 100
18Allixin−5.813852601.6 × 100
19Ajoene−4.713852201.6 × 100
20Ethyl Linoleate−5.813853101.6 × 100
21Diazinon−5.713852601.6 × 100
22Levamisole−5.713852601.6 × 100
23Scutellarein−7.613852101.6 × 100
24S-allyl cysteine methyl ester−5.313852101.6 × 100
25Caffeic acid−5.813852601.6 × 100
Table
Table 3. Results of ligands under Lipinski’s rule of five.
Table 3. Results of ligands under Lipinski’s rule of five.
Sr. NoLigandLogPMolecular Weight(g/mol)Hydrogen Bond AcceptorHydrogen Bond Donar
1L-Alanine−0.667177.22532
2Allicin1.7553162.27920
3Diallyl Sulfide2.0916114.21310
4Diallyl Disulfide2.7398146.2820
5Diallyl Trisulfide3.388178.34730
6Glutathione−2.2061307.32866
7L-Cysteine−0.6719121.16133
8S-Allyl-Mercapto-
Glutathione		−0.741379.4676
9Thiocysteine−0.0237153.22843
10L-Cysteinel-
−0.0227		429.503l-
10
10		6
11Gamma-Glutamyl-
-
S-Allylcysteine		−3329290.34154
12Kaempferol2.2824286.23964
13Quercetin1.988302.23875
14Myricetin1.6936318.23786
15Fructan−7.5682504.4381611
16Lauric Acid3.9919200.32211
17Linoleic Acid5.8845280.45211
18Allixin2.39512226.27241
19Ajoene3.0022234.41130
20Ethyl Linoleate6.363308.50620
21Diazinon3.58472304.35260
22Levamisole2.1461204.29830
23Scutellarein2.2824286.23964
24S-allyl cysteine
methyl ester		1.9719275.3751
25Caffeic acid2.987180.15933
Table
Table 4. Absorption properties of ligands.
Table 4. Absorption properties of ligands.
Sr. NoLigandWater SolubilityCaco2 PermeabilityIntestinal Absorption (Human)Skin PermeabilityP-Glucoprotein SubstrateP-Glucoprotein I inhibitorP-glucoprotein II Inhibitor
1-L-Alanine−2.8880.61976.495−2.735NoNoNo
2Allicin−1.721.31696.229−1.877NoNoNo
3Diallyl Sulfide−2.6951.39496.268−1.488NoNoNo
4Diallyl Disulfide−3.2221.39994.769−1.429NoNoNo
5Diallyl Trisulfide−3.7811.40392.573−1.449NoNoNo
6Glutathione−2.892−0.5360−2.735YesNoNo
7L-Cysteine−2.8880.38674.807−2.737NoNoNo
8S-Allyl-Mercapto-
Glutathione		−2.205−0.4570−2.735YesNoNo
9Thiocysteine−2.8870.42478.653−2.737NoNoNo
10Gamma-Glutamyl-
L-Cysteine		−2.892−0.5980.259−2.735YesNoNo
11Gamma-Glutamyl-
S-Allylcysteine		−2.891−0.5178.312−2.735YesNoNo
12Kaempferol−3.040.03274.29−2.735YesNoNo
13Quercetin−2.925−0.22977.207−2.735YesNoNo
14Myricetin−2.9150.09565.93−2.735YesNoNo
15Fructan−1.2−0.8350−2.735YesNoNo
16Lauric Acid−4.1811.56293.379−2.693NoNoNo
17Linoleic Acid−5.8621.5792.329−2.723NoNoNo
18Allixin−3.0741.30193.438−3.141NoNoNo
19Ajoene−3.541.32995.186−1.745NoNoNo
20Ethyl Linoleate−7.5251.60892.241−2.774NoNoYes
21Diazinon−3.7571.50992.749−3.005NoNoNo
22Levamisole−3.1731.49193.678−2.075NoNoNo
23Scutellarein−3.156−0.35766.687−2.735YesNoNo
24S-allylcysteine
methylester		−2.2130.98693.247−3.061NoNoNo
25Caffeic acid−2.330.63469.407−2.722NoNoNo
Table
Table 5. Distribution properties of ligands.
Table 5. Distribution properties of ligands.
Sr. NoLigandVDss (Human)Fraction Unbound (Human)BBB
Permeability (Human)		CNS
Permeability
1-L-Alanine−0.5530.462−0.271−3.472
2Allicin−0.0450.5770.506−2.312
3Diallyl Sulfide0.2020.5520.69−2.102
4Diallyl Disulfide0.2110.5180.78−2.21
5Diallyl Trisulfide0.2160.4830.767−2.309
6Glutathione−0.3770.463−1.085−3.903
7L-Cysteine−0.4860.49−0.398−3.476
8S-Allyl-Mercapto-
Glutathione		−1.5170.588−1.475−4.217
9Thiocysteine−0.5010.47−0.376−3.5
10Gamma-Glutamyl-
L-Cysteine		−0.2030.495−1.994−4.159
11Gamma-Glutamyl-
S-Allylcysteine		−0.480.452−1.124−4.02
12Kaempferol1.2740.178−0.939−2.228
13Quercetin1.5590.206−1.098−3.065
14Myricetin1.3170.238−1.493−3.709
15Fructan−0.2760.499−1.886−4.815
16Lauric Acid−0.6310.260.057−2.034
17Linoleic Acid−0.5870.054−0.142−1.6
18Allixin−0.0080.4790.193−2.86
19Ajoene0.0830.3950.703−2.178
20Ethyl Linoleate0.3060.0150.776−1.562
21Diazinon−0.3480.329−0.438−3.029
22Levamisole0.4280.3580.358−2.011
23Scutellarein0.5870.192−1.398−2.363
24S-allylcysteine
methylester		-0.3960.434−0.119−2.911
25Caffeic acid-1.0980.529−0.647−2.608
Table
Table 6. Parameters measuring metabolism of ligands.
Table 6. Parameters measuring metabolism of ligands.
Sr. NoLigands CYP-
2D6 Substrate		CYP-
3A4 Substrate		CYP-
2D6 Inhibitor		CYP-
2C19 Inhibitor		CYP-
2C9 Inhibitor		CYP-
2D6 Inhibitor		CYP-
3A4 Inhibitor
1-L-AlanineNONONONONONONO
2AllicinNONONONONONONO
3Diallyl SulfideNONONONONONONO
4Diallyl DisulfideNONONONONONONO
5Diallyl TrisulfideNONONONONONONO
6GlutathioneNONONONONONONO
7L-CysteineNONONONONONONO
8S-Allyl-Mercapto-
Glutathione		NONONONONONONO
9ThiocysteineNONONONONONONO
10Gamma-Glutamyl-
L-Cysteine		NONONONONONONO
11Gamma-Glutamyl-
S-Allylcysteine		NONONONONONONO
12KaempferolNONOYESNONONONO
13QuercetinNONOYESNONONONO
14MyricetinNONOYESNONONONO
15FructanNONONONONONONO
16Lauric AcidNONONONONONONO
17Linoleic AcidNOYESYESNONONONO
18AllixinNONOYESNONONONO
19AjoeneNONONONONONONO
20EthylLinoleateNOYESYESNONONONO
21DiazinonNONONONONONOYES
22LevamisoleNONOYESNONONONO
23ScutellareinNONOYESNONOYESNO
24S-allylcysteine
methylester		NONONONONONONO
25Caffeic acidNONONONONONONO
Table
Table 7. Excretion properties of ligands.
Table 7. Excretion properties of ligands.
Sr. NoLigands Total ClearanceRenal OCT2 Substrate
1-L-Alanine0.365No
2Allicin0.714No
3DiallylSulfide0.555No
4DiallylDisulfide0.547No
5DiallylTrisulfide0.446No
6Glutathione0.308No
7L-Cysteine0.53No
8S-Allyl-Mercapto-
Glutathione		0.333No
9Thiocysteine0.369No
10Gamma-Glutamyl-
L-Cysteine		Gamma-Glutamyl-
0.159
L-Cysteine		No
11Gamma-Glutamyl-
S-Allylcysteine		-
0.3		No
12Kaempferol0.477No
13Quercetin0.407No
14Myricetin0.422No
15Fructan1.516No
16Lauric Acid1.623No
17Linoleic Acid1.936No
18Allixin0.419No
19Ajoene0.538No
20Ethyl Linoleate2.08No
21Diazinon0.391No
22Levamisole0.475No
23Scutellarein0.47No
24S-allylcysteine
Methylester		0.487No
25Caffeic acid0.508No
Table
Table 8. Predicts toxicity of ligands.
Table 8. Predicts toxicity of ligands.
Ligands Max. Tolerated Dose (Human) mg/KghERG
I
inhibitor		hERGII
inhibitor		Oral Rat Acute ToxicityOral Rat Chronic ToxicityHepa
toxicity		Skin Sensitizationt.Pyriformis ToxicityMinnow Toxicity
-L-Alanine1.164NoNo2.0511.9NoNo0.2682.598
Allicin0.737NoNo2.3661.406NoYes0.91.235
DiallylSulfide0.782NoNo2.0281.812NoYes0.631.154
DiallylDisulfide0.674NoNo2.3751.847NoYes1.3710.79
DiallylTrisulfide0.582NoNo2.7111.8
57		NoYes2.0080.516
Glutathione1.104NoNo2.4682.919NONO0.2854.569
L-Cysteine1.133NoNo1.9822.6NONO0.1492.992
S-Allyl-Mercapto-
Glutathione		1.196NoNo1.8042.902YESNO0.2854.569
Thiocysteine1.113NoNo1.9832.275NONO0.1492.992
Gamma-Glutamyl-
L-Cysteine		0.856NoNo2.4783.361YES NO0.2854.164
Gamma-Glutamyl-
S-Allylcysteine		1.119NoNo2.4383.361NONO0.1012.657
Kaempferol0.531NoNo2.4492.29NONO0.2854.306
Quercetin0.499NoNo2.4712.505NONO0.2852.928
Myricetin0.51NoNo2.4972.618NONO0.3142.885
Fructan0.667NoNo2.7752.718NONO0.2883.721
Lauric Acid−0.34NoNo1.5114.703NONO0.2865.023
Linoleic Acid−8.27NoNo1.4292.89NONO0.28513.29
Allixin−0.879NoNo2.1953.187NONO0.945−0.084
Ajoene0.462NoNo2.4720.899NOYES0.701−1.31
EthylLinoleate0.009NoNo1.6443.023NOYES0.3241.582
Diazinon1.362NoNo3.2580.953YESNO2.1970.155
Levamisole0.035NoNo2.7111.548NOYES1.497−1.765
Scutellarein0.626NoNo2.4523.135NONO0.366−0.148
S-allylcysteine
methylester		0.703NoNo2.60.908NONO1.3551.45
Caffeic acid1.145NoNo2.3832.092NONO0.3011.99
Table
Table 9. Absorption properties of Remdesivir.
Table 9. Absorption properties of Remdesivir.
LigandsWater SolubilityCaCO2 PermeabilityIntestinal Absorption (human)Skin PermeabilityP-Gluco Protein SubstrateP-Gluco Protein I InhibitorP-Gluco Protein II Inhibitor
Remdesivir−3.070.63571.1092.735YesYesNo
Table
Table 10. Distribution properties of Remdesivir.
Table 10. Distribution properties of Remdesivir.
LigandVDss (Human)Fraction Unbound (Human)BBB Permeability (Human)CNS Permeability
Remdesivir0.3070.005−2.056−4.675
Table
Table 11. Metabolic properties of Remdesivir.
Table 11. Metabolic properties of Remdesivir.
LigandCYP2D6 SubstrateCYP3A4 SubstrateCYP2D6 InhibitorCYP2C19 InhibitorCYP2C9 InhibitorCYP2D6 InhibitorCYP3A4 Inhibitor
RemdesivirNOYESNONONONONO
Table
Table 12. Excretion properties of Remdesivir.
Table 12. Excretion properties of Remdesivir.
LigandsTotal ClearanceRenal OCT2 Substrate
Remdesivir0.198NO
Table
Table 13. Toxicity properties of Remdesivir.
Table 13. Toxicity properties of Remdesivir.
LigandsMax. Tolerated Dose (Human)Herg I InhibitorHerg II InhibitorOral Rat Acute ToxicityOral Rat Chronic ToxicityHepatoxicitySkin SensitizationT.Pyriformis ToxicityMinnow Toxicity (Log Mm)
Remdesivir1.972NoYes2.0431.639YesNo0.2850.291
Table
Table 14. Docking result of Remdesivir.
Table 14. Docking result of Remdesivir.
Ligands Binding ScoreCavity SizeGrid MapHBAHBDlogPMol. Weight g/mol
Remdesivir−8.11385221342.31218602.585
Table
Table 15. Remdesivir comparison with the lead compound.
Table 15. Remdesivir comparison with the lead compound.
LigandLogPMolecular Weight (g/mol)Hydrogen Bond AcceptorHydrogen Bond Donar
Remdesivir2.31218602.585134
Levamisole2.1461204.2930
Table
Table 16. Comparative values of absorption of Remdesivir and Levamisole.
Table 16. Comparative values of absorption of Remdesivir and Levamisole.
LigandsWater SolubilityCaCO2 PermeabilityIntestinal Absorption (Human)Skin PermeabilityP-Glucoprotein SubstrateP-Glucoprotein I InhibitorPglucoproteinII Inhibitor
Remdesivir−3.070.63571.109−2.735YesYesNo
Levamisole−3.1731.49193.678−2.075NoNoNo
Table
Table 17. Comparative values of metabolic properties of Remdesivir and Levamisole.
Table 17. Comparative values of metabolic properties of Remdesivir and Levamisole.
LigandCYP2D6 SubstrateCYP3A4 SubstrateCYP2D6 InhibitorCYP2C19 InhibitorCYP2C9 InhibitorCYP2D6 InhibitorCYP2D6 Inhibitor
RemdesivirNoYesNoNoNoNoNo
LevamisoleNoNoYesNoNoYesNo
Table
Table 18. Comparative values of the distribution of Remdesivir and Levamisole.
Table 18. Comparative values of the distribution of Remdesivir and Levamisole.
Ligands VDss (Human)Fraction Unbound (Human)BBB Permeability (Human)CNS Permeability
Remdesivir0.3070.005−2.056−4.675
Levamisole0.4280.3580.358−2.011
Table
Table 19. Values of excretory properties of Remdesivir and Levamisole.
Table 19. Values of excretory properties of Remdesivir and Levamisole.
LigandsTotal Clearance Renal OCT2 Substrate
Remdesivir0.198No
Levamisole0.475No
Table
Table 20. Comparative values of toxicity of Remdesivir and Levamisole.
Table 20. Comparative values of toxicity of Remdesivir and Levamisole.
LigandMax. Tolerated Dose (Human) (mg/Kg)HergI InhibitorHergII InhibitorOral Rat Acute Toxicity (mol/Kg)Oral Rat Chronic Toxicity (mol/Kg)Hepa ToxicitySkin SensitizationT.Pyriforms Toxicity (LogUg/L)Minnow Toxicity (Log Mm)
Remdesivir0.196NoYes2.0431.639YesNo0.2850.291
Levamisole0.035NoNo2.7111.548NoYes1.3551.45
Table
Table 21. Comparison of physiochemical properties of Remdesivir and Levamisole.
Table 21. Comparison of physiochemical properties of Remdesivir and Levamisole.
LigandsLogPMolecular Weight (g/mol)Molecular FormulaHBond AcceptorHBond Donar
Remdesivir2.31218602.585C27H35N6O8P134
Levamisole2.1461204.29C11H12N2S30
Table
Table 22. Docking Score Comparison of Levamisole and Remdesivir.
Table 22. Docking Score Comparison of Levamisole and Remdesivir.
LigandsScore
Remdesivir−8.1
Levamisole−5.7
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Ashraf, H.; Dilshad, E.; Afsar, T.; Almajwal, A.; Shafique, H.; Razak, S. Molecular Screening of Bioactive Compounds of Garlic for Therapeutic Effects against COVID-19. Biomedicines 2023, 11, 643. https://doi.org/10.3390/biomedicines11020643

AMA Style

Ashraf H, Dilshad E, Afsar T, Almajwal A, Shafique H, Razak S. Molecular Screening of Bioactive Compounds of Garlic for Therapeutic Effects against COVID-19. Biomedicines. 2023; 11(2):643. https://doi.org/10.3390/biomedicines11020643

Chicago/Turabian Style

Ashraf, Huma, Erum Dilshad, Tayyaba Afsar, Ali Almajwal, Huma Shafique, and Suhail Razak. 2023. "Molecular Screening of Bioactive Compounds of Garlic for Therapeutic Effects against COVID-19" Biomedicines 11, no. 2: 643. https://doi.org/10.3390/biomedicines11020643

APA Style

Ashraf, H., Dilshad, E., Afsar, T., Almajwal, A., Shafique, H., & Razak, S. (2023). Molecular Screening of Bioactive Compounds of Garlic for Therapeutic Effects against COVID-19. Biomedicines, 11(2), 643. https://doi.org/10.3390/biomedicines11020643

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