# Quinoline and Quinazoline Alkaloids against COVID-19: AnIn Silico Multitarget Approach

Authors: Esraa M. O. A. Ismail; Shaza W. Shantier; Mona S. Mohammed; Hassan H. Musa; Wadah Osman; Ramzi A. Mothana
Journal: Journal of Chemistry
 (2021)

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## Abstract

The recent outbreak of the highly contagious coronavirus disease 2019 (COVID-19) caused by the novel coronavirus SARS-CoV-2 has created a global health crisis with socioeconomic impacts. Although, recently, vaccines have been approved for the prevention of COVID-19, there is still an urgent need for the discovery of more efficacious and safer drugs especially from natural sources. In this study, a number of quinoline and quinazoline alkaloids with antiviral and/or antimalarial activity were virtually screened against three potential targets for the development of drugs against COVID-19. Among seventy-one tested compounds, twenty-three were selected for molecular docking based on their pharmacokinetic and toxicity profiles. The results identified a number of potential inhibitors. Three of them, namely, norquinadoline A, deoxytryptoquivaline, and deoxynortryptoquivaline, showed strong binding to the three targets, SARS-CoV-2 main protease, spike glycoprotein, and human angiotensin-converting enzyme 2. These alkaloids therefore have promise for being further investigated as possible multitarget drugs against COVID-19.


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## Body

## 1. Introduction

In December 2019, a global health concern had been raised by the outbreak of coronavirus disease 2019 (COVID-19) which is caused by SARS-CoV-2. It belongs to the single-stranded positive-sense RNA coronavirus family [1]. Its genome consists of different regions including a spike protein (S) gene, an envelope protein (E) gene, a membrane protein (M) gene, and a nucleocapsid protein (N) gene [2]. The sequence of SARS-CoV-2 showed more than 50% identity to SARS-CoV and MERS-CoV and closer relation to bat-SL-CoVZC45 [3, 4]. SARS-CoV produces several functional proteins while its main protease is emerging as a promising therapeutic target as it is responsible for the processing of translated polyprotein. Thus, inhibition of the main protease was confirmed to affect the viral replication [5]. Its high sequence conservation with SARS-CoV main protease suggests the effectiveness of HIV-1 protease inhibitors such as nelfinavir against it [6]. SARS-CoV-2 shares the mode of transmission with SARS-CoV and MERS-CoV, after which it binds to ACE2 on the surface of host cells via the receptor-binding domain (RBD) in its spike proteins [7, 8]. Blocking the ACE2 and RBD interaction by antibodies and inhibitors would be an effective way to stop the virus infection [9].Symptoms of COVID-19 greatly resembled viral pneumonia ranging from mild to more severe eventually ending in several organ malfunction [6]. Discovery of efficacious drugs for this deadly disease could be achieved by one of the three options: testing the existing antiviral drugs which are already used to treat viral infections, secondly, screening of different existing drugs, and finally, discovery of new specific drugs based on the individual coronavirus genome [10]. Chloroquine, HIV protease inhibitors, ACE-2 inhibitors, and many other drugs were predicted to be COVID-19 drug candidates [11, 12].Medicinal plants have pulled in noteworthy consideration since they incorporate bioactive components that could be utilized to design medications against a few ailments with insignificant side effects [13]. The medicinal active compounds of plants have been widely used to treat microbial diseases being antifungal [14], antibacterial [15], and antiviral [16]. Quinoline and quinazoline alkaloids are N-based heterocyclic compounds with a wide range of activities, and many of them have been reported to have antiviral effects [17, 18]. It is therefore imperative to test their effectiveness in COVID-19.Computational methods are commonly used for structure-based drug discovery (SBDD) and ligand-based drug discovery (LBDD) [19, 20]. Since they accelerate the lengthy drug discovery and development process, recently, they have been extensively used for lead discovery against COVID-19 by virtually screening compounds with potential biological activity [21–36]. However, few studies were directed towards the discovery of multitarget drugs [37, 38].The present study computationally assesses the inhibitory effects of selected natural quinoline and quinazoline alkaloids on three potential SARS-CoV-2 drug targets and predicts their pharmacokinetics and toxicities identifying promising multitarget candidates against COVID-19.


## 2. Materials and Methods

### 2.1. Data Sources

Literature was surveyed for various phytochemicals with known antiviral and/or antimalarial activities. 71 bioactive alkaloids (quinoline and quinazoline) from different natural sources were selected to be investigated for their activity against COVID-19 virus [17, 18].


### 2.2. Target Selection and Preparation

COVID-19 main protease domain (PDB ID: 6LU7), COVID-19 spike glycoprotein (PDB ID: 6LZG), and human angiotensin-converting enzyme 2 (ACE2; PDB ID: IR42) were selected as the target proteins. Their 3D structures were retrieved from the RCSB PDB (protein data bank) database. Swiss PDB viewer V.4.1.0 software [39] was used for structure optimization, and the active sites were verified from the UniProt databases.


### 2.3. Ligands’ Preparation

The 3D SDF structures of some compounds were downloaded from the PubChem database, and 2D structures of the rest were illustrated using ChemSketch. The files were then converted into the PDB format with the aid of Open Babel, energy minimized, and converted into the PDBQT format using the graphical user interface version of PyRx virtual screening tool-python prescription 0.8.


### 2.4. Compound Screening Using PyRx Program

Virtual screening was performed using PyRx software and Vina wizard as the engine for docking [40, 41]. The amino acids’ residues in the active site of the protein were selected. The results less than 1.0 Å in positional root-mean-square deviation (RMSD) were considered ideal and clustered together for finding the favorable binding. The highest binding energy (most negative) was considered as the ligand with maximum binding affinity.


### 2.5.In Silico ADME Properties

The pharmacokinetic (ADME) properties of the selected compounds were predicted using the SwissADME web tool (http://www.swissadme.ch/) [42]. The compounds’ structure was retrieved from databases using the import tool on the input zone of the SwissADME submission page and converted into the SMILES format, and then calculations were run. In some cases, the structures were converted into the SMILES format using the Online SMILES Translator (available at https://cactus.nci.nih.gov/translate/).


### 2.6.In Silico Toxicity Risks’ Assessment and Drug Likeliness

OSIRIS Property Explorer open-source program (http://www.organicchemistry.org/prog/peo/) [43] was used to evaluate the toxicity risks of the compounds retrieved from PubChem.


### 2.7. Molecular Docking

Molecular docking was performed using AutoDock 4.0 software, based on the Lamarckian genetic algorithm, which combines energy evaluation through grids of affinity potential to find the suitable binding position for a ligand on a given protein [44, 45]. Polar hydrogen atoms were added to the protein targets, and Kollman united atomic charges were computed. The targets’ grid map was calculated and set to 60 × 60 × 60 points with the grid spacing of 0.375 Ǻ. The grid box was then allocated properly in the target to include the active residue in the center. The genetic algorithm and its run were set to 10 as the docking algorithms were set on default. Results were finally retrieved as binding energies.


### 2.8. Analysis and Visualization

The resultant docking files with poses showing the lowest binding energies were visualized using DS Visualizer Client (Windows 64 bit) (267 MB).


## 2.1. Data Sources

Literature was surveyed for various phytochemicals with known antiviral and/or antimalarial activities. 71 bioactive alkaloids (quinoline and quinazoline) from different natural sources were selected to be investigated for their activity against COVID-19 virus [17, 18].


## 2.2. Target Selection and Preparation

COVID-19 main protease domain (PDB ID: 6LU7), COVID-19 spike glycoprotein (PDB ID: 6LZG), and human angiotensin-converting enzyme 2 (ACE2; PDB ID: IR42) were selected as the target proteins. Their 3D structures were retrieved from the RCSB PDB (protein data bank) database. Swiss PDB viewer V.4.1.0 software [39] was used for structure optimization, and the active sites were verified from the UniProt databases.


## 2.3. Ligands’ Preparation

The 3D SDF structures of some compounds were downloaded from the PubChem database, and 2D structures of the rest were illustrated using ChemSketch. The files were then converted into the PDB format with the aid of Open Babel, energy minimized, and converted into the PDBQT format using the graphical user interface version of PyRx virtual screening tool-python prescription 0.8.


## 2.4. Compound Screening Using PyRx Program

Virtual screening was performed using PyRx software and Vina wizard as the engine for docking [40, 41]. The amino acids’ residues in the active site of the protein were selected. The results less than 1.0 Å in positional root-mean-square deviation (RMSD) were considered ideal and clustered together for finding the favorable binding. The highest binding energy (most negative) was considered as the ligand with maximum binding affinity.


## 2.5.In Silico ADME Properties

The pharmacokinetic (ADME) properties of the selected compounds were predicted using the SwissADME web tool (http://www.swissadme.ch/) [42]. The compounds’ structure was retrieved from databases using the import tool on the input zone of the SwissADME submission page and converted into the SMILES format, and then calculations were run. In some cases, the structures were converted into the SMILES format using the Online SMILES Translator (available at https://cactus.nci.nih.gov/translate/).


## 2.6.In Silico Toxicity Risks’ Assessment and Drug Likeliness

OSIRIS Property Explorer open-source program (http://www.organicchemistry.org/prog/peo/) [43] was used to evaluate the toxicity risks of the compounds retrieved from PubChem.


## 2.7. Molecular Docking

Molecular docking was performed using AutoDock 4.0 software, based on the Lamarckian genetic algorithm, which combines energy evaluation through grids of affinity potential to find the suitable binding position for a ligand on a given protein [44, 45]. Polar hydrogen atoms were added to the protein targets, and Kollman united atomic charges were computed. The targets’ grid map was calculated and set to 60 × 60 × 60 points with the grid spacing of 0.375 Ǻ. The grid box was then allocated properly in the target to include the active residue in the center. The genetic algorithm and its run were set to 10 as the docking algorithms were set on default. Results were finally retrieved as binding energies.


## 2.8. Analysis and Visualization

The resultant docking files with poses showing the lowest binding energies were visualized using DS Visualizer Client (Windows 64 bit) (267 MB).


## 3. Results

### 3.1. Compounds’ Screening

Seventy one natural alkaloids were virtually screened to predict their binding affinities on SARS-CoV-2 main protease (PDB ID: 6LU7), spike glycoprotein (PDB ID: 6LZG), and human ACE2 (PDB ID: 1R42). The obtained results are summarized in Supplementary Table1.


### 3.2. Toxicity Risks and Drug Likeliness

Twenty-seven compounds are predicted to have no risk of mutagenic, tumorigenic, irritant, or reproductive effects and have drug score >0.5 as shown in Table1. These were selected for subsequent steps.Table 1
Toxicity risks and drug likeliness predicted by OSIRIS Property Explorer.
No.Compound nameMETEIEREDLDS1Uranidine————0.500.7821-Methyl-2-[6′-(3″,4″-methylenedioxyphenyl)hexyl]-4-quinolone————−0.860.5534-Methoxy-1-methylquinolin-2-one————1.900.8942-Acetyl-4(3H)-quinazolinone————3.110.945Chimanine D————−0.280.666Cuspareine————4.790.697Galipeine————3.540.798Galipinine————2.450.599Acronydine————2.760.8610Veprisine————2.760.8611Isofebrifugine————4.190.91122-Methoxyrutaecarpine————5.250.8132-Methoxy-13-methylrutaecarpine————6.090.8114Tryptanthrin————3.280.8415Neosartoryadin A————3.320.7116Neosartoryadin B————1.150.6317Oxoglyantrypine————4.000.8718Norquinadoline A————7.040.7419Deoxynortryptoquivaline————4.440.6620Quinadoline A————7.190.72213-Hydroglyantrypine————4.20.8922Cladoquinazoline————3.910.8223Epi-cladoquinazoline————3.910.8224Glyantrypine————3.840.8925Deoxytrytoquivaline————5.930.5826Prelapatine B————3.720.8827Waltherione A————−0.010.54ME: mutagenic effect; TE: tumorigenic effect; IE: irritant effect; RE: reproductive effect; DL: drug likeliness; DS: drug score; (—): no risk.


### 3.3. Physicochemical Parameters

The physicochemical properties of the selected twenty-seven compounds were predicted by the SwissADME web tool. Ten compounds were found to be soluble, and seventeen were predicted to be moderately soluble as shown in Table2. The physicochemical properties influence the pharmacokinetic properties which in turn determines the ultimate biological effect.Table 2
Physicochemical properties of selected compounds.
Compound no.MW (g/mol)NRBsNHBAsNHBDsTPSA (A°2)Consensus logPo/wWater solubility class1193.1604493.550.66Soluble2259.324151.582.44Soluble3189.2112031.231.73Soluble4188.1813162.821.2Soluble5185.2212025.422.39Soluble6311.4252021.73.93Moderately soluble7283.3632132.73.29Moderately soluble8295.3832021.73.83Moderately soluble9301.3424049.692.61Soluble10301.3424049.692.61Soluble11301.3425276.381.04Soluble12317.3413159.913.09Moderately soluble13331.3713049.053.02Moderately soluble14248.2403051.962.16Soluble15472.49072118.71.58Moderately soluble16488.49082130.141.25Moderately soluble17358.35242101.791.9Moderately soluble18471.51263121.51.6Moderately soluble19516.55581124.772.45Moderately soluble20485.53263121.51.82Moderately soluble21360.37243104.951.51Soluble22418.45353118.261.47Moderately soluble23418.45353118.261.47Moderately soluble24344.3723284.722.04Moderately soluble25516.59571100.542.93Moderately soluble26342.3503284.721.79Moderately soluble27393.4335285.122.96Moderately solubleLogPo/w: partition coefficient; NHBDs: number of hydrogen bond donors; NHBAs: number of hydrogen bond acceptors; NRBs: number of rotatable bonds; TPSA: topological surface area.


### 3.4.In Silico ADME Properties

The selected compounds were subjected to pharmacokinetic analysis using the SwissADME tool. The obtained results are summarized in Table3.Table 3
Predicted ADME properties of selected compounds.
Molecule no.HIABBB permeantP-gp substrateCYP1A2 inhibitorCYP2C19 inhibitorCYP2C9 inhibitorCYP2D6 inhibitorCYP3A4 inhibitorLog Kp (cm/s)1HighNoNoNoYesNoNoYes−6.596HighYesYesYesNoNoYesNo−6.117HighYesNoYesNoNoNoNo−6.68HighYesNoYesNoNoNoNo−6.712HighYesNoNoNoNoNoNo−5.9220HighYesNoYesNoYesYesYes−4.8221HighYesNoYesNoNoYesNo−5.1422HighYesYesYesNoYesYesYes−4.8128HighYesNoYesYesYesYesNo−6.6429HighYesNoYesYesYesYesNo−6.6433HighNoYesNoNoNoYesNo−7.8834HighYesYesYesNoYesYesYes−6.1135HighYesYesYesNoYesNoYes−6.2336HighYesNoYesNoNoNoNo−6.3654HighNoNoNoNoNoYesNo−7.8355HighNoNoNoNoNoNoNo−7.9556HighNoYesNoYesNoNoNo−6.3157HighNoYesNoNoNoNoNo−7.5358HighNoNoNoNoNoYesYes−7.0760HighNoYesNoNoNoNoNo−7.4861HighNoNoNoYesNoNoNo−6.8962HighNoYesNoNoNoNoYes−7.2363HighNoYesNoNoNoNoYes−7.2364HighNoYesYesYesNoYesNo−6.465HighNoYesNoNoNoYesYes−7.3866HighNoYesYesYesNoNoNo−6.5269HighNoYesNoNoNoYesYes−6.7HIA: human gastrointestinal absorption; BBB permeant: blood-brain barrier permeability; P-gp substrate: permeability glycoprotein substrate; Kp: skin permeability coefficient.


### 3.5. Analysis and Visualization

Molecular docking using AutoDock 4.0 was conducted for most promising compounds with DS values ≥0.5, no risk of toxicity, and considerable solubility (Figure1 and Table 4).Figure 1
Chemical structures of selected ligands for molecular docking.Table 4
Molecular docking results of promising compounds against the three targets.
Compound nameMin. binding energy on main protease (6LU7; kcal/mol)Min. binding energy on human ACE2 (IR42; kcal/mol)Min. binding energy on spike glycoprotein (6LZG; kcal/mol)1-Methyl-2-[6′-(3″,4″-methylenedioxyphenyl)hexyl]-4-quinolone−8.11−6.25−7.404-Methoxy-2-phenylquinoline−6.50−6.737.26Cuspareine−6.59−6.62−5.86Galipeine−6.73−6.34−7.12Asimicilone−7.25−6.72−7.2Acronydine−7.20−6.31−6.26Veprisine−7.25−6.30−6.81Isofebrifugine−7.10−8.35−7.162-Methoxyrutaecarpine−7.35−7.31−8.702-Methoxy-13-methylrutaecarpine−7.11−7.37−7.58Tryptanthrin−6.95−6.35−6.68Neosartoryadin A−8.34−9.08−8.6Neosartoryadin B−7.49−8.09−8.1Oxoglyantrypine−8.76−8.26−7.5Norquinadoline A−8.75−10.63−8.98Deoxynortryptoquivaline−9.64−10.24−9.53Trytoquivaline, quinadoline A−8.61−11.01−8.953-Hydroglyantrypine−9.19−8.13−7.6Cladoquinazoline−7.77−8.24−7.4Epi-cladoquinazoline−8.08−8.84−7.2Glyantrypine−8.60−8.02−7.7Deoxytrytoquivaline−9.34−10.05−9.22Prelapatine B−7.91−8.38−8.5Visualization was then performed for five compounds, against each target, showing the best docking score (lowest binding energies). Figures2–4 show the intermolecular interaction and hydrogen bonding between the ligands and proteins at the active sites. Important active site residues in each of the three targets are involved in ligands’ binding (Table 5).Figure 2
Promising alkaloids in the active site of SARS-CoV-2 main protease (PDB ID: 6LU7).: hydrogen bonding, : pi-pi stacked, : alkyl/pi-alkyl, : pi-sigma, : pi-anion, and : carbon hydrogen bond. Nelfinavir, a prototype protease inhibitor, is also shown within the active site.
(a)(b)(c)(d)(e)(f)Figure 3
Promising alkaloids in the active site of human ACE2 (PDB ID: 1R42).: hydrogen bonding, : pi-pi stacked, : alkyl/pi-alkyl, : pi-sigma, : pi-anion, and : carbon hydrogen bond. Lisinopril, a prototype ACEI, is also shown within the active site.
(a)(b)(c)(d)(e)(f)Figure 4
Promising alkaloids bound to the SARS-CoV-2 spike glycoprotein binding domain (PDB ID: 6LZG).: hydrogen bonding, : pi-pi stacked, : alkyl/pi-alkyl, : pi-sigma, : pi-anion, and : carbon hydrogen bond.
(a)(b)(c)(d)(e)Table 5
Amino acid residues involved in the binding of promising alkaloids on the three targets.
Target name, PDB IDAmino acids involved in binding interactionsSARS-CoV-2 main protease, 6LU7Ser1, Thr24, Thr26, Met49, Phe140, Asn142, Gly143, Ser144, Cys145, His163, His164, Glu166, Pro168, His172, Gln189SARS-CoV-2 spike receptor-binding domain, 6LZGGlu35, Gln493, Glu484, Asp38, Leu452, Leu492, Ser494, Tyr453, Tyr495, Tyr449, Phe490Human angiotensin-converting enzyme-related carboxypeptidase (ACE2), 1R42Asn394, Asp382, Tyr385, Arg393, Asp350, Leu351, Gly352, Trp349, His401, His378, Ala348


## 3.1. Compounds’ Screening

Seventy one natural alkaloids were virtually screened to predict their binding affinities on SARS-CoV-2 main protease (PDB ID: 6LU7), spike glycoprotein (PDB ID: 6LZG), and human ACE2 (PDB ID: 1R42). The obtained results are summarized in Supplementary Table1.


## 3.2. Toxicity Risks and Drug Likeliness

Twenty-seven compounds are predicted to have no risk of mutagenic, tumorigenic, irritant, or reproductive effects and have drug score >0.5 as shown in Table1. These were selected for subsequent steps.Table 1
Toxicity risks and drug likeliness predicted by OSIRIS Property Explorer.
No.Compound nameMETEIEREDLDS1Uranidine————0.500.7821-Methyl-2-[6′-(3″,4″-methylenedioxyphenyl)hexyl]-4-quinolone————−0.860.5534-Methoxy-1-methylquinolin-2-one————1.900.8942-Acetyl-4(3H)-quinazolinone————3.110.945Chimanine D————−0.280.666Cuspareine————4.790.697Galipeine————3.540.798Galipinine————2.450.599Acronydine————2.760.8610Veprisine————2.760.8611Isofebrifugine————4.190.91122-Methoxyrutaecarpine————5.250.8132-Methoxy-13-methylrutaecarpine————6.090.8114Tryptanthrin————3.280.8415Neosartoryadin A————3.320.7116Neosartoryadin B————1.150.6317Oxoglyantrypine————4.000.8718Norquinadoline A————7.040.7419Deoxynortryptoquivaline————4.440.6620Quinadoline A————7.190.72213-Hydroglyantrypine————4.20.8922Cladoquinazoline————3.910.8223Epi-cladoquinazoline————3.910.8224Glyantrypine————3.840.8925Deoxytrytoquivaline————5.930.5826Prelapatine B————3.720.8827Waltherione A————−0.010.54ME: mutagenic effect; TE: tumorigenic effect; IE: irritant effect; RE: reproductive effect; DL: drug likeliness; DS: drug score; (—): no risk.


## 3.3. Physicochemical Parameters

The physicochemical properties of the selected twenty-seven compounds were predicted by the SwissADME web tool. Ten compounds were found to be soluble, and seventeen were predicted to be moderately soluble as shown in Table2. The physicochemical properties influence the pharmacokinetic properties which in turn determines the ultimate biological effect.Table 2
Physicochemical properties of selected compounds.
Compound no.MW (g/mol)NRBsNHBAsNHBDsTPSA (A°2)Consensus logPo/wWater solubility class1193.1604493.550.66Soluble2259.324151.582.44Soluble3189.2112031.231.73Soluble4188.1813162.821.2Soluble5185.2212025.422.39Soluble6311.4252021.73.93Moderately soluble7283.3632132.73.29Moderately soluble8295.3832021.73.83Moderately soluble9301.3424049.692.61Soluble10301.3424049.692.61Soluble11301.3425276.381.04Soluble12317.3413159.913.09Moderately soluble13331.3713049.053.02Moderately soluble14248.2403051.962.16Soluble15472.49072118.71.58Moderately soluble16488.49082130.141.25Moderately soluble17358.35242101.791.9Moderately soluble18471.51263121.51.6Moderately soluble19516.55581124.772.45Moderately soluble20485.53263121.51.82Moderately soluble21360.37243104.951.51Soluble22418.45353118.261.47Moderately soluble23418.45353118.261.47Moderately soluble24344.3723284.722.04Moderately soluble25516.59571100.542.93Moderately soluble26342.3503284.721.79Moderately soluble27393.4335285.122.96Moderately solubleLogPo/w: partition coefficient; NHBDs: number of hydrogen bond donors; NHBAs: number of hydrogen bond acceptors; NRBs: number of rotatable bonds; TPSA: topological surface area.


## 3.4.In Silico ADME Properties

The selected compounds were subjected to pharmacokinetic analysis using the SwissADME tool. The obtained results are summarized in Table3.Table 3
Predicted ADME properties of selected compounds.
Molecule no.HIABBB permeantP-gp substrateCYP1A2 inhibitorCYP2C19 inhibitorCYP2C9 inhibitorCYP2D6 inhibitorCYP3A4 inhibitorLog Kp (cm/s)1HighNoNoNoYesNoNoYes−6.596HighYesYesYesNoNoYesNo−6.117HighYesNoYesNoNoNoNo−6.68HighYesNoYesNoNoNoNo−6.712HighYesNoNoNoNoNoNo−5.9220HighYesNoYesNoYesYesYes−4.8221HighYesNoYesNoNoYesNo−5.1422HighYesYesYesNoYesYesYes−4.8128HighYesNoYesYesYesYesNo−6.6429HighYesNoYesYesYesYesNo−6.6433HighNoYesNoNoNoYesNo−7.8834HighYesYesYesNoYesYesYes−6.1135HighYesYesYesNoYesNoYes−6.2336HighYesNoYesNoNoNoNo−6.3654HighNoNoNoNoNoYesNo−7.8355HighNoNoNoNoNoNoNo−7.9556HighNoYesNoYesNoNoNo−6.3157HighNoYesNoNoNoNoNo−7.5358HighNoNoNoNoNoYesYes−7.0760HighNoYesNoNoNoNoNo−7.4861HighNoNoNoYesNoNoNo−6.8962HighNoYesNoNoNoNoYes−7.2363HighNoYesNoNoNoNoYes−7.2364HighNoYesYesYesNoYesNo−6.465HighNoYesNoNoNoYesYes−7.3866HighNoYesYesYesNoNoNo−6.5269HighNoYesNoNoNoYesYes−6.7HIA: human gastrointestinal absorption; BBB permeant: blood-brain barrier permeability; P-gp substrate: permeability glycoprotein substrate; Kp: skin permeability coefficient.


## 3.5. Analysis and Visualization

Molecular docking using AutoDock 4.0 was conducted for most promising compounds with DS values ≥0.5, no risk of toxicity, and considerable solubility (Figure1 and Table 4).Figure 1
Chemical structures of selected ligands for molecular docking.Table 4
Molecular docking results of promising compounds against the three targets.
Compound nameMin. binding energy on main protease (6LU7; kcal/mol)Min. binding energy on human ACE2 (IR42; kcal/mol)Min. binding energy on spike glycoprotein (6LZG; kcal/mol)1-Methyl-2-[6′-(3″,4″-methylenedioxyphenyl)hexyl]-4-quinolone−8.11−6.25−7.404-Methoxy-2-phenylquinoline−6.50−6.737.26Cuspareine−6.59−6.62−5.86Galipeine−6.73−6.34−7.12Asimicilone−7.25−6.72−7.2Acronydine−7.20−6.31−6.26Veprisine−7.25−6.30−6.81Isofebrifugine−7.10−8.35−7.162-Methoxyrutaecarpine−7.35−7.31−8.702-Methoxy-13-methylrutaecarpine−7.11−7.37−7.58Tryptanthrin−6.95−6.35−6.68Neosartoryadin A−8.34−9.08−8.6Neosartoryadin B−7.49−8.09−8.1Oxoglyantrypine−8.76−8.26−7.5Norquinadoline A−8.75−10.63−8.98Deoxynortryptoquivaline−9.64−10.24−9.53Trytoquivaline, quinadoline A−8.61−11.01−8.953-Hydroglyantrypine−9.19−8.13−7.6Cladoquinazoline−7.77−8.24−7.4Epi-cladoquinazoline−8.08−8.84−7.2Glyantrypine−8.60−8.02−7.7Deoxytrytoquivaline−9.34−10.05−9.22Prelapatine B−7.91−8.38−8.5Visualization was then performed for five compounds, against each target, showing the best docking score (lowest binding energies). Figures2–4 show the intermolecular interaction and hydrogen bonding between the ligands and proteins at the active sites. Important active site residues in each of the three targets are involved in ligands’ binding (Table 5).Figure 2
Promising alkaloids in the active site of SARS-CoV-2 main protease (PDB ID: 6LU7).: hydrogen bonding, : pi-pi stacked, : alkyl/pi-alkyl, : pi-sigma, : pi-anion, and : carbon hydrogen bond. Nelfinavir, a prototype protease inhibitor, is also shown within the active site.
(a)(b)(c)(d)(e)(f)Figure 3
Promising alkaloids in the active site of human ACE2 (PDB ID: 1R42).: hydrogen bonding, : pi-pi stacked, : alkyl/pi-alkyl, : pi-sigma, : pi-anion, and : carbon hydrogen bond. Lisinopril, a prototype ACEI, is also shown within the active site.
(a)(b)(c)(d)(e)(f)Figure 4
Promising alkaloids bound to the SARS-CoV-2 spike glycoprotein binding domain (PDB ID: 6LZG).: hydrogen bonding, : pi-pi stacked, : alkyl/pi-alkyl, : pi-sigma, : pi-anion, and : carbon hydrogen bond.
(a)(b)(c)(d)(e)Table 5
Amino acid residues involved in the binding of promising alkaloids on the three targets.
Target name, PDB IDAmino acids involved in binding interactionsSARS-CoV-2 main protease, 6LU7Ser1, Thr24, Thr26, Met49, Phe140, Asn142, Gly143, Ser144, Cys145, His163, His164, Glu166, Pro168, His172, Gln189SARS-CoV-2 spike receptor-binding domain, 6LZGGlu35, Gln493, Glu484, Asp38, Leu452, Leu492, Ser494, Tyr453, Tyr495, Tyr449, Phe490Human angiotensin-converting enzyme-related carboxypeptidase (ACE2), 1R42Asn394, Asp382, Tyr385, Arg393, Asp350, Leu351, Gly352, Trp349, His401, His378, Ala348


## 4. Discussion

SARS-CoV-2 is an RNA virus which tends to mutate more commonly than the DNA viruses. It has killed thousands of people around the globe with an increase in death rate every single day. In order to control and treat such pandemics, drug repurposing is highly recommended as it involves the use of derisked compounds with potentially lower development cost and shorter timelines. Moreover, discovery of multitarget drugs can be accelerated byin silico screening of potential ligands on a number of promising drug targets. In our present study, natural quinoline and quinazoline alkaloids were screened in silico against three macromolecules selected as potential therapeutic targets for treatment of COVID-19 infection.


### 4.1. Compounds’ Screening

The obtained results (Supplementary Table1) revealed that most of the compounds have good affinity with binding energies ranging from −5.0 to −10.8 kcal/mol. Therefore, in order to select the most promising and potential candidates, evaluation of toxicity risks and drug likeliness was performed.


### 4.2.In Silico ADME Properties

Results predicted that twenty-three compounds have promise being drugs or drug leads since they showed no risk of mutagenic, tumorigenic, reproductive, or irritant effects, with drug scores (DS) >0.5. Their physicochemical properties indicated that they are less likely to have solubility problems which could lead to poor bioavailability as well as formulation problems [46]. Indeed, this acceptable profile of solubility has been translated in the ADME profile as solubility can also influence it. All the selected compounds were predicted to be highly absorbed from the gastrointestinal tract. Twelve compounds were predicted to permeate the BBB, of which four were also potential substrates for P-gp which could pump them out of the central nervous system (CNS) preventing any serious effects.Many drugs are metabolized by cytochrome P450 (CYP) enzymes [47]; therefore, assessment of the effect of potential drug candidates on these enzymes is essential. Most of the selected compounds were predicted to inhibit one or two of the five CYP enzymes tested in this study. However, cuspareine, galipinine, acronydine, veprisine, 2-methoxyrutaecarpine, and 2-methoxy-13-methylrutaecarpine were predicted to inhibit four CYP enzymes indicating possible adverse effects and drug-drug interactions [48, 49].


### 4.3. Molecular Docking Analysis and Visualization

The most potential 23 compounds with DS score ≥0.5 and no risk of toxicity were redocked against the three targets using AutoDock 4.0. Table4 shows their different binding energies with 6LU7 (ranging between −6.5 and −9.64 kcal/mol), IR42 (ranging between −6.2 and −11.01 kcal/mol), and 6LZG (ranging between −5.86 and −9.53 kcal/mol). Compared to nelfinavir, a known protease inhibitor, all docked compounds show lower binding energy to the main protease of SARS-CoV-2 indicating that they have the potential to inhibit viral replication. It is important to note that residues Glu166, His163, His164, Phe140, Cys145, Ser144, and Gln189 are involved in binding of nelfinavir and the selected alkaloids. These residues were recently reported in an inhibitor-bound SARS-CoV-2 protease [50].The receptor-binding domain of SARS-CoV-2 spike glycoprotein is involved in the virus entry to host cells by binding to ACE2. Our results predicted that the tested alkaloids bind to the receptor-binding domain of SARS-CoV-2 spike glycoprotein which could therefore inhibit its binding to its receptor. Notably, important amino acids (Gln493 and Glu484) are responsible for the higher binding of SARS-CoV-2 to human ACE2 compared to SARS-CoV as confirmed by mutagenesis studies. Moreover, they are predicted to bind to human ACE2 in its active site which is not the binding site for virus spike glycoprotein. However, conformational changes in the three-dimensional structure of the protein were reported which affect residues which are important for the binding of the spike protein [51]. In addition, a novel ACE2 inhibitor discovered by molecular docking has also shown effectiveness in blocking SARS-CoV spike protein-mediated cell fusion [9]. Given the high similarity of the SARS-CoV-2 and SARS-CoV binding to the host receptor (ACE2), the same could happen with SARS-CoV-2. Eighteen of the alkaloids docked within the active site of human ACE2 exhibit higher binding affinities than lisinopril, which is a prototype the ACE2 inhibitor, with binding energies ranging from 7.31 to 11.01 kcal/mol compared to −7.2 kcal/mol for lisinopril.Interestingly, three alkaloids, namely, norquinadoline A, deoxytryptoquivaline, and deoxynortryptoquivaline, are predicted to strongly bind the three proteins studied here. These alkaloids are biologically active secondary metabolites isolated from the mangrove-derived fungusCladosporium sp. PJX-41 and showed anti-influenza A (H1N1) activity [52]. A recent study in which a number of fungal secondary metabolites (including a number of quinoline and quinazoline alkaloids) were virtually screened against five targets of SARS-CoV-2 has also identified norquinadoline A and deoxynortryptoquivaline as potential inhibitors to main protease [38]. The reported binding energy of norquinadoline A (−8.1 kcal/mol) is comparable to our result here (−8.75 kcal/mol). Both alkaloids showed stronger binding to the papain-like protease of the virus.Many late-stage failures in drug discovery occur as a result of poor pharmacokinetic and toxicity profiles. Therefore, earlier prediction is essential. The three alkaloids were predicted to have good pharmacokinetic and safety profiles indicating their promise of being taken forward to be testedin vitro and in vivo to prove their effectiveness against SARS-CoV-2.


## 4.1. Compounds’ Screening

The obtained results (Supplementary Table1) revealed that most of the compounds have good affinity with binding energies ranging from −5.0 to −10.8 kcal/mol. Therefore, in order to select the most promising and potential candidates, evaluation of toxicity risks and drug likeliness was performed.


## 4.2.In Silico ADME Properties

Results predicted that twenty-three compounds have promise being drugs or drug leads since they showed no risk of mutagenic, tumorigenic, reproductive, or irritant effects, with drug scores (DS) >0.5. Their physicochemical properties indicated that they are less likely to have solubility problems which could lead to poor bioavailability as well as formulation problems [46]. Indeed, this acceptable profile of solubility has been translated in the ADME profile as solubility can also influence it. All the selected compounds were predicted to be highly absorbed from the gastrointestinal tract. Twelve compounds were predicted to permeate the BBB, of which four were also potential substrates for P-gp which could pump them out of the central nervous system (CNS) preventing any serious effects.Many drugs are metabolized by cytochrome P450 (CYP) enzymes [47]; therefore, assessment of the effect of potential drug candidates on these enzymes is essential. Most of the selected compounds were predicted to inhibit one or two of the five CYP enzymes tested in this study. However, cuspareine, galipinine, acronydine, veprisine, 2-methoxyrutaecarpine, and 2-methoxy-13-methylrutaecarpine were predicted to inhibit four CYP enzymes indicating possible adverse effects and drug-drug interactions [48, 49].


## 4.3. Molecular Docking Analysis and Visualization

The most potential 23 compounds with DS score ≥0.5 and no risk of toxicity were redocked against the three targets using AutoDock 4.0. Table4 shows their different binding energies with 6LU7 (ranging between −6.5 and −9.64 kcal/mol), IR42 (ranging between −6.2 and −11.01 kcal/mol), and 6LZG (ranging between −5.86 and −9.53 kcal/mol). Compared to nelfinavir, a known protease inhibitor, all docked compounds show lower binding energy to the main protease of SARS-CoV-2 indicating that they have the potential to inhibit viral replication. It is important to note that residues Glu166, His163, His164, Phe140, Cys145, Ser144, and Gln189 are involved in binding of nelfinavir and the selected alkaloids. These residues were recently reported in an inhibitor-bound SARS-CoV-2 protease [50].The receptor-binding domain of SARS-CoV-2 spike glycoprotein is involved in the virus entry to host cells by binding to ACE2. Our results predicted that the tested alkaloids bind to the receptor-binding domain of SARS-CoV-2 spike glycoprotein which could therefore inhibit its binding to its receptor. Notably, important amino acids (Gln493 and Glu484) are responsible for the higher binding of SARS-CoV-2 to human ACE2 compared to SARS-CoV as confirmed by mutagenesis studies. Moreover, they are predicted to bind to human ACE2 in its active site which is not the binding site for virus spike glycoprotein. However, conformational changes in the three-dimensional structure of the protein were reported which affect residues which are important for the binding of the spike protein [51]. In addition, a novel ACE2 inhibitor discovered by molecular docking has also shown effectiveness in blocking SARS-CoV spike protein-mediated cell fusion [9]. Given the high similarity of the SARS-CoV-2 and SARS-CoV binding to the host receptor (ACE2), the same could happen with SARS-CoV-2. Eighteen of the alkaloids docked within the active site of human ACE2 exhibit higher binding affinities than lisinopril, which is a prototype the ACE2 inhibitor, with binding energies ranging from 7.31 to 11.01 kcal/mol compared to −7.2 kcal/mol for lisinopril.Interestingly, three alkaloids, namely, norquinadoline A, deoxytryptoquivaline, and deoxynortryptoquivaline, are predicted to strongly bind the three proteins studied here. These alkaloids are biologically active secondary metabolites isolated from the mangrove-derived fungusCladosporium sp. PJX-41 and showed anti-influenza A (H1N1) activity [52]. A recent study in which a number of fungal secondary metabolites (including a number of quinoline and quinazoline alkaloids) were virtually screened against five targets of SARS-CoV-2 has also identified norquinadoline A and deoxynortryptoquivaline as potential inhibitors to main protease [38]. The reported binding energy of norquinadoline A (−8.1 kcal/mol) is comparable to our result here (−8.75 kcal/mol). Both alkaloids showed stronger binding to the papain-like protease of the virus.Many late-stage failures in drug discovery occur as a result of poor pharmacokinetic and toxicity profiles. Therefore, earlier prediction is essential. The three alkaloids were predicted to have good pharmacokinetic and safety profiles indicating their promise of being taken forward to be testedin vitro and in vivo to prove their effectiveness against SARS-CoV-2.


## 5. Conclusion

Computer-based drug discovery usually involves the search for small-molecule leads with attractive pharmacokinetic and toxicity profiles. These molecules are then testedin vitro and in vivo to confirm their therapeutic potential. Since natural products have played and continue to play a great role in efficacious drug discovery, this study was conducted in order to investigate the potential of selected natural alkaloids against the highly contagious virus SARS-CoV-2 which is responsible for the current pandemic. The study identifies twenty-three alkaloids as possible candidates with the potential to inhibit targets that prevent virus entry and/or preventing their replication. Of these, norquinadoline A, deoxytryptoquivaline, and deoxynortryptoquivaline inhibit three protein targets and exhibit good pharmacokinetic and safety profiles, suggesting them as possible natural multitarget drugs against COVID-19. Therefore, these three alkaloids could be starting points for future drug development efforts, indeed after confirming their effectiveness and mechanisms of action thereafter.



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*Source: 3613268-2021-03-25.xml*