INTRODUCTION:
Benzimidazole derivatives are promising therapeutic agents for addressing diseases such as cancer and hormone imbalances due to their ability to inhibit receptors, enzymes like aromatase and dihydrofolate reductase (DHFR), and specific protein kinases.
These compounds are often identified through high-throughput screening (HTS), a method that enables rapid evaluation of large chemical libraries to find lead compounds with strong inhibitory activity against target enzymes. While HTS is highly effective in discovering drug candidates, its implementation can be costly, which may restrict its use in drug development.1 The rapid expansion of computing capabilities and data availability has significantly enhanced in silico drug discovery techniques, including cheminformatics, molecular modelling, and artificial intelligence. These approaches provide a faster and more economical means of identifying potential drug candidates.2,3,4,5 Structure-based approaches in drug discovery leverage 3D models of target structures to assess and prioritize molecules based on their spatial and electronic compatibility with the target. Molecular docking is a key component of this process, involving the prediction of how a ligand aligns within a receptor, followed by an evaluation of their compatibility using a scoring system.6 This research offers valuable insights into the structural requirements of benzimidazole molecules, which can inform the design of new therapeutic agents.
In situations where the need for swift energy evaluation is particularly demanding, advanced docking techniques can be employed to enhance accuracy. Traditional methods often use a rigid receptor model, which can lead to inaccurate results for proteins that undergo significant structural changes during the binding process.7 Moreover, the interactions between ligands and receptors are often facilitated by structured water molecules. Auto Dock has developed methods to explicitly account for specific water molecules, enhancing the accuracy of these interactions.8 Initially developed to elucidate molecular recognition, molecular docking has become a crucial technique in drug discovery. It now plays a key role in identifying potential compounds, optimizing lead molecules, repurposing existing medications, designing ligands that target multiple sites, identifying therapeutic targets, and repositioning drugs, as depicted in Figure 1.
Figure 1: Applications of Molecular Docking
Software used in Molecular Docking:
In the past twenty years, the development of docking tools and programs has been prolific, with over sixty different software packages emerging for both academic and commercial use, as illustrated in Figure 2.
Figure 2: Molecular Docking Software
Several tools are commonly utilized for analyzing molecular docking studies, including PyMOL and BIOVIA Discovery Studio Visualizer. The specific docking tools employed in the molecular docking analysis of benzimidazole and its derivatives are detailed below.
AutoDock [1990]: AutoDock, developed by David Goodsell and Arthur Olson in 1990, is a widely used molecular docking tool designed to predict how small molecules interact with biological targets, aiding in drug discovery and development. By utilizing the structures of proteins and nucleic acids, AutoDock helps design bioactive compounds for disease control and biological research. Known for its flexibility and accuracy, AutoDock employs a Lamarckian genetic algorithm in version 4.2, which combines random conformational exploration of small molecules with optimization techniques such as crossover, mutation, and recombination. This iterative process adjusts search parameters based on success or failure, refining the docking process until the step size meets the final threshold.5
FlexX [1996]: FlexX, developed by BioSolveIT, is a molecular docking software specifically designed for flexible ligand docking into protein binding sites. It is widely utilized in drug discovery for tasks such as structure-based virtual screening and lead optimization. The software employs a fragment-based approach, where ligands are divided into smaller fragments. An initial fragment is placed within the binding site, and the ligand is progressively reconstructed by adding additional fragments. This method ensures efficient exploration of ligand flexibility while maintaining accuracy.7
GOLD [1997]: Genetic Optimization for Ligand Docking (GOLD) is a widely used protein-ligand docking software developed by the Cambridge Crystallographic Data Centre (CCDC). It utilizes a genetic algorithm to predict how flexible ligands interact with protein binding sites, making it a versatile tool in drug discovery. GOLD is applied in various tasks, including virtual screening, lead optimization, and determining the correct binding modes of active molecules. The software generates realistic binding poses of candidate ligands within the active site of proteins through a process involving pose generation and evaluation. Scoring functions are then employed to assess the quality of these poses, ensuring accurate predictions of ligand-protein interactions.8
HDOCK [2017]: The HDOCK server, introduced in 2017 by the School of Physics at Huazhong University of Science and Technology in Wuhan, China, is a robust platform for protein–protein docking. It seamlessly integrates various functionalities, including homology search, template-based modelling, structure prediction, macromolecular docking, incorporation of biological information, and job management. This server can accept inputs in the form of amino acid sequences or Protein Data Bank (PDB) structures for both receptor and ligand molecules. It utilizes a hybrid docking algorithm that combines template-based and template-free methods to predict molecular interactions accurately. A notable feature of the HDOCK server is its ability to use amino acid sequences as input and incorporate experimental data, such as information about protein–protein binding sites and small-angle X-ray scattering (SAXS), during the docking process. Furthermore, it supports docking between proteins and RNA/DNA molecules using an intrinsic scoring function. The server provides users with both template-based and docking-based models of molecular interactions, allowing for easy download and interactive visualization.6
HEX [2019]: Hex Technologies, established in October 2019 by Barry McCardel, Caitlin Colgrove, and Glen Takahashi, developed an advanced molecular docking and superposition program known as Hex. This software utilizes a unique spherical polar Fourier (SPF) correlation algorithm to efficiently predict interactions between proteins, DNA, and small molecules. Hex supports rigid-body docking and leverages GPU acceleration to significantly reduce computational times. It can complete constrained searches in seconds and global searches in minutes. The program offers high-resolution docking correlations and employs novel surface sphere smothering algorithms to determine optimal receptor-ligand orientations. Additionally, it incorporates molecular mechanics energy minimization to refine docking solutions. Hex has demonstrated its effectiveness in blind docking trials, such as CAPRI, by accurately predicting binding modes for complex macromolecules with increasing confidence. Its capabilities make it a valuable tool for researchers in structural biology and drug discovery.9
Visualizing Software:
BIOVIA Discovery Studio Visualizer [1990]: BIOVIA Discovery Studio Visualizer is a free, feature-rich application for molecular modelling, designed to facilitate the viewing, sharing, and analysis of protein and small molecule data. It enables researchers and their colleagues to efficiently exchange results without compromising time or scientific integrity. As part of the comprehensive BIOVIA Discovery Studio software suite, it supports advanced molecular modelling and simulation tasks. Key applications include protein-ligand docking, predicting binding modes, estimating interaction strengths, visualizing results, preparing ligands and proteins, and conducting virtual screenings. This suite is widely utilized in drug discovery, bioinformatics, and structural biology, providing a robust platform for understanding molecular interactions and developing new therapeutic agents.10
Data and methods:
Molecular Docking Method: Investigation of the binding affinity of the new benzimidazole derivatives in to oxidoreductase receptor (PDBcode:3PQ5) was performed for the purpose of lead optimization and to find out the interaction between the benzimidazole compounds and oxidoreductase receptor follow the procedure in Figure 3.
Figure 3: Procedure for Docking
· Use AutoDockTools(ADT) to remove water, add hydrogens/charges, define ligand torsions, save as PDBQT.
· In ADT, open 3PQ5, define a grid box around the binding site, and save the grid parameter file (GPF).
· In ADT, open both PDBQT files, set docking parameters (LGA algorithm), save the docking parameter file (DPF), and run AutoGrid and AutoDock from the command line.
· Access the docking log, examine the poses, and analyze the energy profiles. Use visualization tool such as BIOVIA Discovery Studio to generate interaction diagram and visual representation, as exemplified in Table 1 and Figure 4. This approach enables a detailed interpretation of docking results, molecular interaction, and binding mechanism.
Table 1: Docking Result of Benzimidazole
|
Final Intermolecular Energy |
VDW + H-bond + Desolvation Energy |
Electrostatic Energy |
Free Energy of Binding |
|
-4.48 kcal/mol |
-4.42 kcal/mol |
-0.06 kcal/mol |
-4.63 kcal/mol. |
Figure 4: Structure of PDB: 3PQ5
Molecular modelling calculations and local docking were done using AUTODOCK and BIOVIA discovery studios visualizer to evaluate the binding free energy of these inhibitors into the target Oxidoreductase receptor.
To validate the docking accuracy of the program used, docking
of the native co-crystallized (
1H-1,3-benzimidazole) ligand was done into its
binding site of Oxidoreductase receptor. The docked ligand was exactly superimposed
on the native co crystallize done with RMSD being 53.188 Å and binding free energies
of (-4.63) kcal/mol. The hydrogen bonds between the docked ligand and the amino
acids were the same as those between the native ligand and the amino acids. The
binding affinities of the synthesized compounds into oxidoreductase receptor Molecular
docking study was performed to find out interactions between ligand and receptor
and to compare affinities of the synthesized compounds to the target Oxidoreductase
receptor.
For docking calculations, the protein structure (PDB code:3PQ5)
was first separated from the inhibitor molecule and refined using molecular minimization
with added hydrogens. Docking calculations were carried out using standard default
variables for the BIOVIA Discovery studios visualizer. The binding affinity was
evaluated by the binding free energies and hydrogen bonds. The Benzimidazole derivatives
were docked into same groove of the binding site of the native co-crystalized (1𝐻-1,3-benzimidazole) ligand. The structure of benzimidazole
presented pi-alkyl interaction with PRO C:11 and ALA C: 143, Pi-Pi interaction with
HIS C:179, Hydrogen bonding with HIS D:33 and van der waal interaction with GLN
C:180, GLU D:35, SER C: 135, PRO C:176, ALA C:136 as shown in Figure 5.
Figure 5: Amino Acid Residue for Benzimidazole
The derivatives of benzimidazole (Albendazole and Mebendazole) were also subjected to Molecular docking studies using AutoDock by following the similar procedure and then the results were visualized using BIOVIA discovery studio. The observed results are shown in the following Tables 2 and 3 respectively.
Table 2: Docking Result of Albendazole
|
Final Intermolecular Energy |
VDW + H bond + Desolvation Energy |
Electrostatic Energy |
|
|
-5.11 kcal/mol |
-5.03 kcal/mol |
-0.07 kcal/mol |
-3.62 kcal/mol |
Table 3: Docking Result for Mebendazole
|
Final Intermolecular Energy |
VDW + H bond + Desolvation Energy |
Electrostatic Energy |
Free Energy of Binding |
|
-6.51 kcal/mol |
-6.36 kcal/mol |
-0.15 kcal/mol |
-5.31 kcal/mol |
In Figure 6, it is clearly depicted that Albendazole shows; Pi-Pi interaction with TYR A:88; Hydrogen bond interaction at TRP A:84, ASP A:97; Alkyl interaction with ILE A:99, ALA A:130, TRP A: 111, VAL B:250, MET A:89, MET B:253; and vander waal interaction with LEU A:85, VAL A:75, ILE A:153, PHE A:115, PHE A:126, TYR A:80, SER A:125, LEU A:57, LEU A:100, LEU A:72.
Figure 5: Amino Acid Residue for Albendazole
In Figure 7, Interactions of Mebendazole are depicted ; Pi-sigma interaction with TRP C: 111, Hydrogen bond interaction with TRP C:84, TYR C:80 shows unfavourable bump, Alkyl interaction with ILE C:99, VAL D:250 , MET C:89, and vander waal forces with LEU C:85, LEU C:100, LEU C:72, LEU C:57, ILE C:153, ASP C:97, ALA C:130, PHE A:115, PHE C:126, TYR A:80, SER A:155.
Figure 7: Amino Acid Residue for Mebendazole
CONCLUSIONS AND IMPLICATIONS:
Employing AutoDock software for a variety of targets has proven to be consistently successful. Our team has looked at a docking between benzimidazole and its derivatives with oxidoreductase inhibitor. The bound conformations and free energies of binding of small molecule ligands to macromolecular targets can be predicted using computational docking which predicts the binding of and interaction between drug and receptor. Docking is a popular technique used in structure-based drug design and the study of biomolecular interactions and processes. Because the techniques are quick enough, ligand libraries of tens of thousands of molecules can be virtually screened.
Molecular docking, calculating free energy, intermolecular energy, and bond energy is crucial because it provides a quantitative measure of the strength and stability of a potential ligand-receptor interaction, allowing researchers to predict which molecules are most likely to bind to a target protein based on their calculated binding affinity, essentially guiding the selection of promising compounds.
Molecular docking studies were conducted to evaluate the binding affinities of Benzimidazole and its derivatives, Albendazole and Mebendazole, against an oxidoreductase receptor (PDB: 3PQ5). The affinities were determined based on the free binding energy of each compound. The results showed that Mebendazole exhibited the highest affinity with a binding energy of (-5.31 kcal/mol), significantly surpassing Benzimidazole (-4.63 kcal/mol) and Albendazole (-3.62 kcal/mol). This indicates that Mebendazole has a stronger interaction with the oxidoreductase receptor compared to the other two compounds and therefore used for further studies on its anticancer activity.
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Received on 26.03.2025 Revised on 28.04.2025 Accepted on 20.05.2025 Published on 19.06.2025 Available online from June 23, 2025 Asian J. Research Chem.2025; 18(3):123-128. DOI: 10.52711/0974-4150.2025.00020 ©A and V Publications All Right Reserved
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