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

Tunga Kuhana A. (  tunga_tunga@yahoo.frFR ) University of Kinshasa Jason T. Kilembe University of Kinshasa Aristote Matondo University of Kinshasa Khamis M. Yussuf Zhengzhou University Lauraine Nininahazwe Zhengzhou University Fils K. Nkatu University of Kinshasa Milka N. Tshingamb University of Kinshasa Emmanuel K. Vangu University of Kinshasa Junior T. Kindala University of Kinshasa Shetonde O. Mihigo University of Kinshasa Sungula J. Kayembe University of Kinshasa Yves S. Kafuti University of Kinshasa Agboyibor Clement Zhengzhou University Kalulu M. Taba University of Kinshasa


Introduction
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 signi cant homology with SARS virus [6][7][8]. It was rst 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, di culty breathing, and various fatal complications including organ failure, septic shock, pulmonary edema, severe pneumonia, and Acute Respiratory Distress Syndrome (ARDS) and frost-glasslike 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 ower) 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 oral 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 e ciency against the virus protease using molecular docking tool, and thus pinpoint the potential inhibitors.  [33,34] Materials And Methods

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].

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 a nity 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].

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 a nity 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 de ne the binding site. So, scoring function was calculated using the standard protocol of Lamarckian genetic algorithm [39]. Result And Discussion

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 a nity (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 a nity 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 ( To the best of our knowledge, this is the rst 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 a nities ranging from 8.2 to 8.5 kcal/mol were identi ed [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 identi ed 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 πalkyl 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.

Prediction of pharmacokinetic and toxicity
In the pipeline of computer-aided drug design, after the identi cation 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 ve (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 ve of all ligands were calculated and are listed in 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 (  [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.

Conclusion
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 nding 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 a nities 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 rst 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 ndings.