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0UDC 577.112.386:615.015:578.6
MOLECULAR MODELING AND DOCKING OF CISHIOOT AS A PROSPECTIVE INHIBITOR OF VIRAL TARGETS
KURBONOV SAFARMUKHAMMAD SAIDAKHMADOVICH
PhD student at the Department of Systems and Information Technologies, Technological University of Tajikistan, Dushanbe, Tajikistan
KHAMIDOVA DILOROM NASRULLOEVNA
PhD, Associate Professor, Department of Programming Technology and AI, Kulob Institute of technology and innovation management, Kulob, Tajikistan
Abstract. This article explores the potential of quercetin (CisHioO?) as an inhibitor of viral proteins, such as proteases and spike proteins, using molecular modeling and docking. Computational experiments were conducted to evaluate the binding affinities, energy profiles, and interaction mechanisms of quercetin with biological targets. The results confirm the promise of quercetin as a lead compoundfor antiviral drug development. The study emphasizes the importance of natural compounds in addressing global health challenges and recommends further experimental validation to optimize the structure and pharmacokinetic properties of quercetin.
Key words: quercetin, molecular docking, antiviral agents, viral proteins, protease inhibition, spike protein, computational modeling, drug discovery, natural compounds.
Introduction
Research at the intersection of biology, medicine and chemistry is becoming an important source of new knowledge that contributes to the development of innovative methods and approaches to solving specific problems. One of the key areas that has been actively developing in recent decades is theoretical modeling of new organic compounds with specified characteristics. This area has become especially promising in the field of creating new drugs. Modern computing technology and specialized software that have appeared in recent years have made these methods widely in demand [1]. Although they do not provide direct information on the mechanisms of interaction that determine biological activity, their application allows us to effectively solve significant practical problems. These methods represent a set of approaches associated with the use of pattern recognition theory. Within the framework of this theory, a number of research methods have been created and described. One of these methods is the Hansch method [2], based on the linear dependence of free energies. It arose due to the integration of methods of physical organic chemistry and multivariate statistical analysis in the problems of medicinal chemistry.
The ongoing search for effective antiviral agents has led to increased interest in naturally occurring compounds with potential therapeutic properties. Among these, quercetin (C15H10O7), a flavonoid found in a wide variety of fruits and vegetables, has garnered attention due to its reported biological activities, including antioxidant, anti-inflammatory, and antiviral effects [3]. Its structural properties and ability to interact with biomolecules make it a promising candidate for drug development.
This study focuses on the interaction of quercetin with viral targets, such as proteases and spike proteins, which play critical roles in viral replication and host cell entry. Using molecular docking and computational modeling, the binding affinities, interaction profiles, and potential inhibitory mechanisms of quercetin were evaluated. The findings provide insights into its suitability as a lead compound for antiviral drug discovery and highlight the potential of natural compounds in addressing global health challenges.
Materials and Methods
Molecular docking is a molecular modeling method [4-9] used to predict interactions between drug compounds and their biological targets, such as receptors and ligands. This approach can
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significantly speed up the process of developing new drugs by reducing the number of expensive and time-consuming laboratory experiments. The molecular docking process includes two key stages: sampling and scoring [10]. The sampling stage involves exploring the conformational space of the interacting molecules. This space is extensive because both the receptor and the ligand have flexibility, allowing them to adapt their shape under the influence of each other. The second important stage is the use of a scoring function. Each of the selected conformations is evaluated by a scoring function, which determines their probability of being biologically significant. This function allows one to identify the most probable conformations, which are then ranked in order of their estimated accuracy. The result of molecular docking is a set of conformations of interacting molecules, which can be used for further analysis and drug development [11].
In this work, quercetin (Fig. 1a) was obtained using the Avogadro molecular editor (Fig. 1b). The Avogadro program is designed to visualize and construct the initial conformations of molecules and allows you to easily construct or change the structure of a molecule, open a huge number of file formats, can serve as a graphical shell for many quantum chemical packages and independently carry out semi-empirical calculations with visualization in the program window [12].
Fig. 1. Quercetin (C15H10O7): (a) Chemical Structure, (b) Computer model built in the molecular editor Avogadro
The structures of viral proteins (Fig. 2.), including protease and spike proteins, were obtained from the Protein Data Bank (PDB) [13]. All water molecules and non-essential ligands were removed using the Biopython library of the Python programming language, followed by hydrogen atoms and optimization using AutoDockTools. A special filter based on the Select class from the Biopython library was used to remove water. The resulting file contained only the atoms of the protein chain and other essential molecules. The resulting PDB file was created and saved for future use.
a
b
Fig. 2. SARS-CoV-2 viral protein structures visualized using the molecular editor
PyMOL
Number of atoms in chains before water removal Number of atoms in chains after water removal
Fig. 3. Figure 4 - Pre-processing of dataA (a) data histogram before water removal, (b) data histogram after water removal
Molecular docking was performed using the SwissDock platform. Protein-ligand interactions were analyzed to predict the binding poses and calculate binding energies (AG). The docking was conducted in the blind mode to identify all potential binding sites.
Docking results were analyzed based on binding energy, clustering, and interaction profiles. The clusters with the lowest binding energy were considered for further analysis. Key residues involved in hydrogen bonding, van der Waals interactions, and electrostatic interactions were identified using PyMOL and Chimera.
Results and discussion
The docking results were analyzed based on binding energy (deltaG), clustering, and interaction profiles. The binding energies of the identified clusters ranged from -7.64 kcal/mol to
higher values, indicating varying levels of ligand-protein interaction stability. The best cluster (Rank 0) demonstrated the lowest binding energy (deltaG = -7.64 kcal/mol), suggesting a highly favorable interaction between C15H10O7 (quercetin) and the protein target. The distribution of binding energies is shown in Fig. 4, where most clusters exhibit energies below -5.0 kcal/mol, supporting the potential inhibitory activity of the ligand.
Binding energy distribution (deltaG)
Binding energy (kcal/mol)
Fig. 4 Distribution of binding energies (deltaG) for all clusters
The van der Waals interactions (deltaGvdw) significantly contributed to the binding energy, with values as low as -45.26 kcal/mol for the best cluster. Conversely, the solvation energy (deltaGligsolvpol) contributed positively (+7.22 kcal/mol), indicating the balance between stabilizing interactions and desolvation effects. Fig. 5 illustrates the relationship between deltaGvdw and deltaGligsolvpol, highlighting the dominance of van der Waals interactions in stabilizing the ligand-protein complex.
Binding energy as a function of cluster rank
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Fig. 5. Scatter plot of deltaGvdw vs. deltaGligsolvpol
The clustering of docking poses showed that the majority of stable conformations were grouped into a few clusters, with Cluster 0 having the highest population and the most favorable binding energy. Fig. 6 demonstrates the energy distribution across clusters, indicating that quercetin maintains stable interactions in multiple configurations.
Binding energy in different clusters
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Fig. 6. Binding energy distribution across identified clusters
The combination of structure-based docking and energy analysis confirms the high potential of quercetin as a lead molecule for antiviral drug development. The strong van der Waals contributions and specific residue interactions suggest that quercetin could effectively inhibit viral targets, such as SARS-CoV-2 proteases. Further experimental validation is necessary to confirm the predicted interactions and their therapeutic relevance.
Conclusion
This study employed molecular docking to investigate the potential of quercetin (C15H10O7) as an inhibitor of viral targets, with a focus on its interactions with SARS-CoV-2 proteins. Visualization of the ligand-protein complex confirmed critical interactions, including hydrogen bonds and hydrophobic contacts, that contribute to the stability and specificity of binding. These findings underscore quercetin's potential as a lead compound for antiviral drug development, especially in addressing emerging viral threats. Future work will involve experimental validation of these computational predictions and exploration of quercetin derivatives to optimize binding affinity and pharmacokinetic properties. This approach aligns with the urgent need for effective and rapidly deployable antiviral agents in the face of global health challenges.
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