Computational analysis by molecular docking of thirty alkaloid compounds from medicinal plants as potent inhibitors of SARS-CoV-2 main protease

doi:10.21203/rs.3.rs-94752/v1

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Computational analysis by molecular docking of thirty alkaloid compounds from medicinal plants as potent inhibitors of SARS-CoV-2 main protease

https://doi.org/10.21203/rs.3.rs-94752/v1

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2020 has been highly affected by the COVID-19 outbreak. The urgent needs for a potent and effective drug for treatment of this malignance put pressure on researchers and scientists worldwide to develop potential drug or a vaccine to resist SARS-CoV-2. We report in this paper the assessment of the efficiency of thirty alkaloid compounds derived from African medicinal plants against the SARS-CoV-2 main protease through molecular docking and bioinformatics approaches. The results reveal four potential inhibitors (ligands 18, 21, 23 and 24) with the highest binding energies up to 12.26 kcal/mol with good profile of ADMET, as well as fully obey the Lipinski’s rule of five.

Computational Biology

COVID-19

medicinal plants

binding affinity

ADMET properties

molecular docking

The coronaviruses have recently become a serious problem in the world. It is family of coronavirinae which contains four genera based on genetic properties: alpha coronavirus, delta coronavirus, gamma coronavirus and beta coronavirus. Approximately, the genome size of coronavirus compare to other RNA viruses range from 26 to 32 kilobases. The Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) also belong to the beta coronavirus genus and are zootopic pathogens that can cause severe respiratory diseases in humans [1,2].

The novel coronavirus pneumonia (coronavirus disease 2019, COVID-19 or SARS-CoV-2) is a highly contagious acute respiratory infectious disease and constitutes a major public health problem. In fact, the nucleic acid of the novel coronavirus is a positive-stranded RNA [3]. Its structural proteins include: spike protein (S), envelope protein (E), membrane protein (M), and nucleocapsid phosphoprotein; while its transcribed non-structural proteins include: ORF1ab, ORF3a, ORF6, ORF7a, ORF10 and ORF8.

The novel coronavirus is highly homologous to the coronavirus in bats [4,5], and has significant homology with SARS virus [6-8]. It was first detected in December 2019 in Wuhan, Hubei Province (China), and became a global pandemic, killing hundreds of thousands of people [9]. The infected patients by SARS-CoV-2 have a fever and the temperature above 38°C with symptoms such as dry cough, fatigue, dyspnea, difficulty breathing, and various fatal complications including organ failure, septic shock, pulmonary edema, severe pneumonia, and Acute Respiratory Distress Syndrome (ARDS) and frost- glass-like symptoms in the lungs [10].

There are only two ways of transmission of COVID-19 that have been reported, by droplets and contact (direct and indirect) [11].

Currently, there are no specialized treatments available for COVID-19 and several explorations relevant to the therapies of COVID-19 are becoming inadequate [12]. Consequently, other than vaccine development, academics and researchers have undertook several strategies, by using on one hand traditional medicinal plants as possible alleviating drugs [13-14], and one the other hand by exploring in silico studies in order to pinpoint potential inhibitors from a set of secondary metabolites and other chemical compounds [15-16]. Nevertheless, it is worthy to mention that some studies have encountered the combinations of existing drug candidates (FDA approved drugs) involving anti-HIV drugs such as lopinavir/ritonavir, remdesivir, etc for therapeutic use against COVID-19 [17].

The recognition of protease as an attractive target to inhibit COVID-19 replication has emerged as an interesting pathway to investigate both natural and synthetic drugs to target the viral protease [18,19].

Medicinal plants provide a wide variety of integral and alternative drugs which may assist to solve the many puzzles behind several viral diseases [20]. So, the different part of plant (stem, roots, seed, bark, food and flower) are used in treating disease vary from frequent to rare infectious and non-infectious ailments [21].

In this study, we evaluated the potential inhibitors of SARS-CoV-2 main protease by thirty alkaloid compounds derived from African medicinal plants as one of rich in floral biodiversity and its plant materials are endowed with natural products (NPs) with intriguing chemical structures and promising biological activities which can be used in searching the solution against COVID-19 [22]. According to the World Health Organization (WHO), more than 80% of the population in Africa use traditional medicine to solve their primary health problem [9]. A survey of literature allowed us to identify 30 alkaloids compounds derived from plants used in African Traditional Medicine (ATM), harvested from the following countries: Republic of the Congo, Nigeria, Tanzania, South Africa, Ivory Coast West African countries, Kenya and East African countries (Table 1) [22]. These 30 phytochemicals have been put in interaction with the SARS-CoV-2 main protease in order to assess their efficiency against the virus protease using molecular docking tool, and thus pinpoint the potential inhibitors.

Table 1 Summary of selected alkaloids derived from the African plant: indoles and naphthoisoquinolines

Compound subclass	Isolated metabolites	Plant species (family)	Part of plant studied	Harvest place (locality, country)	Reference
Indole alkaloids	1, 2, 3, 4, 5, 6	Monodora angolensis (Annonaceae)	Stem and root bark	Kiwanda, Tanzania	[23]
	7, 8	Isolona cauliflora (Annonaceae)	Stem and root bark, and flower stalks	Namikwe Island, Tanzania	[23]
	9	strychnos usambarensis (loganiaceae)	leaves	Akagera National park,Rwanda	[24]
	10 and 11	Penianthus longifolius (Menispermacea)	Stem bark	Cameroon	[25]
	12	Fagara zanthoxyloides (Rutaceae)	roots	Nigeria	[26]
	13	Picralima nitida (Apcynaceae)	fruits	Nnewi, Nigeria	[27]
	14 and 15	Strychnos usambarensis (loganiaceae)	leave	Akagera National park, Rwanda	[28]
Naphthoisoquinolines	16, 17, 18,19 and 20	Ancistrocladus robertsoniorum (Acistrocladaceae)	Stems and leaves	Buda Mafisini Forest, Kenya	[29]
	21, 22, 23 and 24	Ancistrocladus tanzaniensis (Acistrocladaceae)	leaves	Uzungwa Mountains, Tanzania	[30]
	25	Triphyophyllum peltatum (Dioncophyllaceae)	roots	Parc de Taï, West Ivory Coast	[31]
	26	Triphyophyllum peltatum (Dioncophyllaceae)	Root bark	West Ivory Coast	[32]
	27, 28, 29 and 30	Triphyophyllum peltatum (Dioncophyllaceae)	Leaves and Twigs	Mt. Nabemba, Congo Republica and West Ivory Coast	[33,34]

2.1 Receptor preparation

The crystal structure of COVID-19 Mpro (PDB ID: 6LU7) (Figure 1) was retrieved from the Protein Data Bank and imported into Auto dock 4.2 where the inhibitor and water molecules were removed before the docking and hydrogen atoms were added to the protein in order to correct the ionization and tautomeric states of the amino acid residues. Further, Kollman charges were added and the protein was saved in .pdbqt format [35].

2.2 Ligands preparation and pharmacokinetic study

The selected alkaloid compounds derived from African medicinal plant were drawn using ChemDraw Ultra (8.0). Figures 2 and 3 show the 2D structures of the sketched compounds. From ChemDraw 2D the ligands were imported to obtain 3D structures. The 3D ligands were then saved in .pdb format for molecular docking with the SARS-CoV-2 main protease.

Since the binding affinity of ligand-protein interactions merely gives an idea of the thermodynamic stability of the formed complex, it is important to analyze the pharmacodynamics of the potential inhibitors. To do so, predictions of ADMET (Adsorption, Distribution, Metabolism, Excretion and Toxicity) of all investigated compounds were assessed by means of SwissADME database available at https://www.swissadme.ch, and preADMET server (Korea) [36, 37].

2.2.3 Molecular recognition ligand-protein by molecular docking

Molecular docking is used to estimate the scoring function and evaluate protein–ligand interactions in order to predict the binding affinity and activity of the ligand molecule [38]. Autodock tool was used to generate the bioactive binding poses of ligands dataset in the active site of SARS-CoV-2 Mpro. The protein coordinates from the bound ligand of 6LU7 was used to define the binding site. So, scoring function was calculated using the standard protocol of Lamarckian genetic algorithm [39]. The grid map for docking calculations was centered on the target protein. Accelrys Discovery Studio 2019 software [40] was used to model non-bonded polar and hydrophobic contacts in the inhibitor site of 6LU7. The docking results were visualized using Pymol 2.3.4.0 (see Figures S1 and S2 in supplementary data) and Discovery Studio Visualizer 4.0.

3.1 Energetics and ligands-protein interactions

The docking calculations of thirty alkaloid compounds with SARS-CoV-2 protease were carried out by using Autodock virtual screening tool. The results of docking calculations in terms of binding affinity (kcal/mol) and interactions of different orientations of alkaloid compounds in the active site of the SARS-CoV-2 main protease are shown in Table 2. Also gathered in this table are the drug-likeness properties of the ligands.

The binding affinity values of the virtual screening between the 30 selected compounds and the SARS-CoV-2 main protease range from 5.52 to 12.26 kcal/mol. It should be noted that the best candidate against COVID-19 is a compound (a hit molecule) that binds to the target (SARS-CoV-2 main protease) and has the desired effect, in addition to form a stable complex. Thermodynamically, this is a compound with the highest possible binding energy expressed in terms of Gibbs free energy variation (∆G) [9,15]. This allows us to identify in this initial step 22 hits mainly: ligand 2 (7.49 kcal/mol), from ligand 8 (7.88 kcal/mol) to ligand 12 (8.17 kcal/mol), from ligand 14 (9.73 kcal/mol) to ligand 28 (10.70 kcal/mol), and ligand 30 (7.81 kcal/mol). These ligands were retained in comparison of their binding affinity with those reported in this paper of the FDA approved drugs used to treat erectile dysfunction (tadalafil : 8.80 kcal/mol) and human immunodeficiency virus/HIV (lopinavir : 8.19 kcal/mol), as well as in comparison with the binding energy of the reference ligand (8.80 kcal/mol). The best docked compounds (∆G ≤ 8.2 kcal/mol; see also ref. [41] are hits 10 (9.33 kcal/mol), 11 (9.05 kcal/mol), 12 (8.17 kcal/mol), 14 (9.73 kcal/mol), 18 (12.26 kcal/mol), 21 (9.97 kcal/mol), 22 (9.60 kcal/mol), 23 (10.99 kcal/mol), 24 (11.28 kcal/mol), 25 (9.99 kcal/mol), 26 (8.27 kcal/mol), and 28 (10.70 kcal/mol). To the best of our knowledge, this is the first computational study that reports binding energies higher than 10 kcal/mol of ligands bind to one of the pharmacological targets of the SARS-CoV-2. In fact, Olubiyi and coworkers performed high throughput virtual screening of over one million compounds, but only six with the strongest computed affinities ranging from 8.2 to 8.5 kcal/mol were identified [41].

The interactions analysis of the 12 best docked ligands can be summarized as follows:

Other than hydrogen bonding interaction which is the main force among non-covalent interactions stabilizing the complexes [42], ligands 10, 11 and 12 show some similarities in interactions involving their aromatic rings. The presence of four aromatic rings in both compounds offer much possibilities to π-π interactions (stacked and T-shaped) to take place [43]. Other interactions such as π-alkyl interaction with VAL104, π-sigma interaction with ILE106, for all three ligands are established; and amide-π interaction with ASN151 for ligands 10 and 11. Ligands 10 and 12 are stabilized only by one hydrogen bonding interaction with GLN107 (ligand 10) and ARG105 (ligand 11) as the interacting residue of the amino acid. With regards to van der Waals (vdW) interactions as one of the main forces, six vdW interactions (GLN110, ARG 105, SER158, ASP153, ILE 152 and PHE8) occur in ligand 10, supported by two hydrogen bonds with GLN107 and ILE152 as amino acids residues. Six vdW interactions are also take place in ligand 11 with ARG105, SER158, ASP153, PHE8, PHE294 and GLN110 as AA residues, while seven vdW interactions (PHE 8, ILE 152, ASN151, SER158, GLN107, GLN110, TH111 and ASP295) are identified in the complex ligand 12-Mpro.

Ligand 14 is characterized by one H-bonding interaction with GLN110, π-alkyl interaction with ILE249 and eleven vdW interactions with ASN203, THR292, ILE106, THR111, PHE 8, ASP295, ASN151, SER158, VAL104, PHE294 and CYS160. Surprisingly, none H-bonding interaction occurs in the ligand 18, albeit the strongest one with highest binding energy (12.26 kcal/mol). However, this ligand is stabilized by two π-alkyl interactions with MET165, CYS145, π-π interaction with HIS41 and ten vdW interactions with LEU141, GLY143, HIS164, ASP187, TYR54, GLN189, ARG188, THR190, GLN192 and GLU166. This result supports the works from Kasende et al, in which π-π and vdW interactions are primary forces in stabilizing two polyaromatic macromolecules, even when H-bonding interaction occurs [43, 44].

With none H-bonding interaction, except for ligand 22, one can refer to ligand 18 to understand the stability of ligands 21, 22, 23 and 24 with ΔG values between 10-11 kcal/mol. The complex formed between ligand 25 and the SARS-CoV-2 Mpro is stabilized by three hydrogen bonds with GLU166, CYS145, and HIS163 AA residues; a π-alkyl interaction with MET165 and six vdW interactions with interacting residues GLN192, GLN189, GLY143, ASN142, PHE140, and LEU141.

Finally, the complexes wherein ligands 26 and 28 are involved are stabilized by three (with LYS5, GLN127, LYS137 AA residues) and two hydrogen bonds (CYS145 and HIS163 being the AA residue), respectively. The stability of complex with ligand 26 is supported by a π-π interaction with TYR126 and six vdW with CYS128, GLY138, GLU290, SER 139, MET6 and VAL125, whereas the stability of complex with ligand 28 is supported by nine vdW interaction with ALA191, GLN189, THR190, GLN192, HIS164, GLY143, ASN142, PHE 140, LEU141.

3.2 Prediction of pharmacokinetic and toxicity

In the pipeline of computer-aided drug design, after the identification of hit molecules, the next step to deal with is the pre-clinical optimization that concerns the physicochemical properties, mainly the ADME/T prediction. The physicochemical property is an important parameter of a molecule which can be used as a drug and can be predicted by using Lipinski’s rule of five (RO5) that is: molecular mass < 500; Hydrogen-bond donors (HBD) < 5; Hydrogen-bond acceptors (HBA) < 10; and Log P < 5 [45]. Toxicity and pharmacokinetic studies such as absorption, distribution and metabolism of alkaloid compounds were assessed by using the web based application PreADMET (https://preadmet.bmdrc.kr/) and SwissADME database (https://www.swissadme.ch).

The drug-likeness properties accommodated in Lipinski’s rule of five of all ligands were calculated and are listed in Table 2. The results reveal that only ligand 14 does not fully obey the Lipinski’s rule of five criteria, with only 1 violation (MW 549.75 Da > 500 Da). Consequently, the best 12 docked ligands among the 30 investigated alkaloids may emerge as potential major inhibitors of COVID-19 protease.

Turning next to the pharmacokinetic and toxicity properties of eleven potential inhibitors ligands, the results displayed in Table 3 reveal that there are potential drug candidates (Table 3) among the 12 best docked compounds. First of all, the hit molecule to be tested in clinical phase must be non-carcinogenic. The rodent carcinogenicity in rat predicted by the preADMET server reveals that only ligand 22 is carcinogenic. The Ames test that assesses mutagenicity reveals that 7 ligands are no mutagen, in addition of being no-carcinogenic: ligands 18, 21, 23, 24, 25, 26 and 28. Interestingly, the ligand 18 with highest binding energy is predicted to be no-carcinogenic and no mutagen. The pharmacokinetic evaluation related to inhibition of Cytochrome P450 and substrate of P-glycoprotein shows that ligands 18, 21, 23 and 24 are found to be non-inhibitors of all CYPs. Ligands 10, 11, 12, 22, 25, 26 and 28 inhibit one or two of the cytochromes responsible for drug metabolism (CYP2D6 and CYP3A4), and cannot be presented as potent inhibitors drugs [46]. In the case of the hERG inhibition, all the ligands presented medium risk. Thus, the toxicity prediction shows that the ligands 18, 21, 23 and 24 are safe and represent potential therapeutic candidates against COVID-19.

Table 3 Pharmacokinetics and toxicity properties of the three potential inhibitors.

Ligand	Ames-test	Carcino-Rat	BBB p.	hERG	P-gp S.	1A2	2C19	2C9	2D6	3A4
10	Mutagen	Negative	Yes	Medium risk	Yes	No	No	No	Yes	Yes
11	Mutagen	Negative	Yes	Medium risk	Yes	Yes	No	No	Yes	Yes
12	Mutagen	Negative	Yes	Medium risk	Yes	Yes	Yes	No	Yes	Yes
18	No mutagen	Negative	Yes	Medium risk	Yes	No	No	No	No	No
21	No mutagen	Negative	Yes	Medium risk	Yes	No	No	No	No	No
22	Mutagen	positive	Yes	Medium risk	Yes	No	No	Yes	No	Yes
23	No mutagen	negative	Yes	Medium risk	Yes	No	No	No	No	No
24	No mutagen	negative	Yes	Medium risk	Yes	No	No	No	No	No
25	No mutagen	negative	Yes	Medium risk	Yes	No	Yes	No	Yes	Yes
26	No mutagen	negative	Yes	Medium risk	Yes	No	Yes	No	Yes	No
28	No mutagen	negative	Yes	Medium risk	Yes	No	Yes	No	Yes	No

The COVID-19 pandemic continues to spread in the world and most of countries are currently facing the second phase of the virus propagation. Several strategies are used by researchers to help finding a solution to this public health issue. The present study used computational drug design approach by molecular docking to identify potential inhibitors of SARS-CoV-2 main protease from a set of thirty alkaloid compounds from African medicinal plants as potential inhibitors. Scrutiny of the binding affinities leads to 22 hits with highest binding energies, up to 12.26 kcal/mol, but pharmacokinetic investigations as important pre-clinical phase reveal only four compounds as potential therapeutic agents to be used in the treatment of COVID-19: ligands 18, 21, 23 and 24. To the best of our knowledge, this computational study is the first to report binding energies higher than 10 kcal/mol of ligands bind to one of the pharmacological targets of the SARS-CoV-2. To support this encouraging results, we recommend further in vivo trials for the experimental validation of our findings.

Acknowledgement

Our sincere thanks go to Professor Kalulu M. Taba for agreeing to supervise this work.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

All authors read and approved the final manuscript.

Funding

This study did not receive any particular funding.

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Computational analysis by molecular docking of thirty alkaloid compounds from medicinal plants as potent inhibitors of SARS-CoV-2 main protease

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