Insilico Evaluation of the Antiviral Potentials of Selected Natural Compounds Against Sars-Cov-2 Viral Main Protease

Background

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)—the pathogen responsible for COVID-19—at the end of 2019 presented an unparalleled global public health crisis. The pandemic resulted in millions of deaths worldwide and caused severe disruptions to economic systems, healthcare delivery, and social structures. Although substantial progress has been made in vaccine research and deployment, the continuous emergence of new viral variants and the limitations in vaccine efficacy and accessibility highlight the pressing need for effective antiviral therapeutics (Zhu et al., 2020; World Health Organization [WHO], 2021). Within the viral replication cycle, the main protease (Mpro or 3CLpro) plays a critical role by catalyzing the cleavage of viral polyproteins essential for replication and transcription. Inhibition of this protease has been demonstrated to effectively halt viral replication, establishing it as a key molecular target for antiviral drug development (Ullrich & Nitsche, 2020).

Traditional drug discovery processes are often expensive and time-consuming; consequently, computational or in silico methods have become invaluable in modern pharmacological research. Techniques such as molecular docking and molecular dynamics simulations allow for rapid and efficient screening of large compound libraries by predicting the binding affinities and interaction patterns between potential ligands and target proteins (Lionta et al., 2014). These approaches have significantly contributed to the identification of potential SARS-CoV-2 inhibitors derived from natural sources.

Medicinal plants have historically served as vital reservoirs of therapeutic agents, offering structurally diverse phytochemicals with documented antiviral, anti-inflammatory, and immunomodulatory properties. Notably, Nigella sativa (black seed), Zingiber oficinale (ginger), and Allium sativum (garlic) have long-standing applications in traditional medicine and have been scientifically validated for their broad pharmacological effects, including antimicrobial and antiviral activities [1,2]. Bioactive compounds such as thymoquinone from Nigella sativa, gingerols and shogaols from Zingiber oficinale, and allicin and ajoene from Allium sativum have demonstrated capacities to inhibit viral replication and modulate host immune responses [3,4].

Given their diverse bioactive constituents and documented antiviral efficacy, these plants represent promising candidates for the discovery of novel inhibitors targeting the SARS-CoV-2 main protease. Therefore, in silico evaluation of their phytochemical components could yield valuable insights for the development of natural antiviral agents and serve as a foundation for subsequent experimental validation.

Accordingly, this study seeks to investigate the antiviral potentials of selected phytochemicals from Nigella sativa and Allium sativum against the SARS-CoV-2 main protease through computational molecular docking techniques, thereby contributing to ongoing global efforts aimed at identifying safe, accessible, and cost-effective antiviral compounds.

Materials and Methods

Proteins and Ligands Retrieval

The 3D conformations of the ligands were retrieved from the NCBI-PubChem and downloaded in an SDF file format. The ligands were optimized using Avogadro version 1.2 and docked using AutoDock and Python Molecular Viewer version 1.5.7. The Covid-19 protein was retrieved from the Protein Data Bank (PDB). The retrieved protein is the Covid-19 main protease (M pro) of SARS-COV-2 which is a key enzyme of coronaviruses and has a pivotal role in mediating viral replication and transcription, making it an attractive drug target for SARS-CoV-2. The enzyme has 306 amino acid residues with the crystal structure shown in figure 1.

Binding energies, inhibition constants (Ki), and ligand-receptor interactions were analyzed through Lamarckian Genetic Algorithm at 50 conformations (runs), selecting the conformations with lowest inhibition constant and the highest binding energy for further analysis.

Molecular Docking Analysis

Molecular docking refers to the computational technique used to envisage the preferred orientation of a ligand (small molecule) when it binds to a protein (macromolecule). The orientation helps to regulate the strength and specificity of the interaction of the molecules with SARS-COV-2 viral main protease.

Flexible Molecular docking was performed on selected bioactive compounds from Nigella sativa and Allium sativum against the main protease of the SARS-CoV-2 virus, following the methodology described by (Bouchentouf et al., 2020; Khan et al., 2022), and others [5]. Four active compounds (ligands), including Dithymoquinone, thymoquinone from Nigella sativa, and Allicin, Diallyl-disiulfide from Allium sativum, were docked against the COVID-19 main protease. Chloroquine was used as a control ligand.

Ligands were retrieved in 3D conformations from the NCBI-PubChem database, optimized using Avogadro version 1.2, and docked using AutoDock and Python Molecular Viewer version 1.5.7. Binding energies, inhibition constants (Ki), and ligand-receptor interactions were analyzed through Lamarckian Genetic Algorithm at 50 conformations (runs), selecting the conformations with lowest inhibition constant and the highest binding energy for further analysis. The structure-based virtual screening approach utilised AutoDock tools with Python Molecular Viewer version 1.5.7 to ascertain the binding energy, inhibition constant (ki), conformation, and interactions between the ligands and the Protease receptor sites.

Hydrogen, atoms, and charges (Kollman charges) were added to the protein after it had been optimised by removing water molecules and other hetero-atoms. The PDBQT format was used to store both the optimised protein and the ligands. The docking parameters were built up using the Lamarckian Genetic Algorithm at 50 conformations (runs), and the ligand-protein grid was set up, executed, and produced as a GLG file. Runs (conformations) were chosen and studied based on their binding energies and inhibition constants.

Results & Discussion

Table 1: The Conformations, Binding Energy, Inhibition Constant and Involved Amino Acids

The molecular docking results against the SARS-CoV-2 main protease indicate variable binding affinities and predicted inhibition constants among the tested compounds (see Table 1). The control ligand, chloroquine, achieved a binding energy of –5.33 kcal/mol and an inhibition constant of ~124.62 µM, interacting with residue MET17. Among the phytochemicals studied, dithymoquinone (from Nigella sativa) showed the strongest interaction, with a binding energy of –7.39 kcal/mol and a low predicted inhibition constant of 3.84 µM, binding via SER158. This suggests a comparatively higher binding affinity than chloroquine. Other Nigella sativa compound showed weaker affinity, thymoquinone (–4.39 kcal/mol, 253.08 µM), primarily interacting with ARG217. Compounds from Allium sativum exhibited the weakest interactions: allicin (–3.60 kcal/mol, 3290 µM) binding LYS5, and diallyl disulfide (–2.92 kcal/mol, 7290 µM) interacting with THR257. Overall, dithymoquinone emerges as the most promising candidate among the tested natural compounds, showing superior predicted binding affinity and inhibitory potency. The dominant interacting residues include ARG217, THR257, and SER158, suggesting these may be critical binding sites for potential inhibitors.

Figure 1: The Crystal Structure of Covid-19 Main Protease

The crystal structure of the SARS-CoV-2 main protease (M_pro). The enzyme is shown as a ribbon diagram, illustrating the arrangement of α-helices and β-sheets that form the catalytic domains. The catalytic dyad (His41 and Cys145) lies within the cleft between the domains, constituting the active site responsible for proteolytic processing of viral polyproteins essential for replication

Figure 2: Molecular Binding of Chloroquine with Covid-19 Main Protease (M ^pro)

Figure 2 illustrated the molecular docking interaction between Chloroquine and the SARS-CoV-2 main protease (M_{pro<\sub>) a critical enzyme responsible for viral replication and transcription. The ligand (Chloroquine) is represented within the receptor’s active site cavity, surrounded by key amino acid residues such as PRO9, ASN95, TRP31, MET17, VAL18, VAL70, GLY71, GLY120, and ASN119, which form stabilizing contacts through hydrogen bonding, van der Waals interactions, and hydrophobic effects. The green region on the ligand denotes the presence of a chlorine atom, which may contribute to halogen bonding and hydrophobic stabilization within the enzyme’s pocket. The observed interaction pattern in this docking model aligns closely with previously reported computational studies of Chloroquine against SARS-CoV-2}

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