Available online on 15.06.2025 at http://jddtonline.info

Journal of Drug Delivery and Therapeutics

Open Access to Pharmaceutical and Medical Research

Copyright  © 2025 The   Author(s): This is an open-access article distributed under the terms of the CC BY-NC 4.0 which permits unrestricted use, distribution, and reproduction in any medium for non-commercial use provided the original author and source are credited

Open Access Full Text Article                                                                        Research Article

Molecular Docking: An Important Computational Tool in Virtual Screening of Some Imidazole Phenanthroline Derivatives 

Shahana Salam *, Dr Rakesh Kumar Jat

Department of Pharmacy, Shri Jagdishprasad Jhabarmal Tibrewala University, Jhunjhunu, Rajasthan, India

 

 

INTRODUCTION

The use of molecular docking research is necessary for the discovery and development of novel medications.  It allows researchers to predict the orientation of a ligand or drug candidate when it binds to a protein target like an enzyme or receptor ¹. This provides information related to the strength and nature of binding interaction, thus helping in the design of synthesis of novel therapeutic compounds ^2-5.

Auto Dock Vina, Schrodinger, Haddock, Rosetta etc. are some popular software tools and platforms used for research on molecular docking. Auto Dock Tools (ADT) serves as the graphical interface for configuring and executing the Auto Dock software. It is among the most accurate docking tools effective for protein docking of ligands. It is easily available software, accessible to the public at no cost with better efficiency and time-saving benefits ^6,7. 

The binding site's energy is calculated using the Auto Grid approach, followed by a comparison of the ligand's energetics with the values produced by the interaction terms derived from the affinity grid calculations. The Auto Dock applications include protein-protein docking, virtual screening (HTS), structure-based drug design, X-ray crystallography, combinatorial library construction and chemical mechanism studies. The knowledge about how a ligand can block the enzyme's active site assists in designing suitable enzyme inhibitors. Large chemical libraries may be quickly virtual screened to find hit compounds for biological testing. Details about a drug's mechanism of action may be found in its ligand binding modes. The COVID-19 drug discovery was a prime illustration of docking: Molecular docking was utilized in the urgent search for anti-SARS-CoV-2 medicines, to identify potential inhibitors of vital viral proteins, including spike protein and the major protease (Mpro). Researchers have targeted specific pathways in cancer biology using docking studies to identify small molecules, leading to the discovery of promising anti-cancer agents. Docking is also utilized in the medication design process to investigate how heterocyclic compounds interact with various proteins ⁸.

Numerous methods are being used to find novel medications by targeting drug targets such as enzymes or receptors ⁹. One attractive target in E. coli is DNA gyrase, a type II topoisomerase, which participates in the replication and transcription of this bacterium ^{10, 11}. Two GyrA subunits and two GyrB subunits make up the heterotetramer. This enzyme is seen in micro-organisms as it is important for maintaining its cellular functions but is absent in humans. While the B subunit possesses the ATPase active site, the A subunit connects with DNA and has the tyrosine active site, which promotes DNA breaking. The gyrase enzyme works by inducing double-stranded breaks in the DNA, passing another segment of the DNA by way of the break, followed by closing the break again. This results in negative supercoils, which are essential for the DNA in the bacterial cell to function correctly. ^12,13.

There could be different approaches to DNA gyrase in antimicrobial docking studies ¹⁴. (i) Molecular Docking: It is a computer method that calculates how strongly ligands attach to receptor proteins. (ii) Docking Based on Ligands: It involves docking a library of compounds onto the receptor to identify potential binders. (iii) Structure-Based Docking: It uses a known protein structure (eg: DNA gyrase) to dock ligands and assess their binding affinity. Several factors such as structural features of imidazole phenanthroline compounds, AT-rich regions in DNA, and binding mechanism affect binding. 

METHOD

Instrumentation

Auto Dock Tools (ADT) 1.5.6 is an analysis software program that works in collaboration with the Auto Dock suite (including Auto Dock Vina) for molecular docking studies. 

Compounds 

4a: 2-(4-chloro-2-methoxyphenyl)-1H-imidazo[4,5-f] [1,10] phenanthroline

4b: 4-chloro-2-(1H-imidazo[4,5-f] [1,10] phenanthroline-2-yl)-6-iodophenol

4c: 2-(3-iodo-4-methoxyphenyl)-1H-imidazo[4,5-f][1,10]phenanthroline

4d: 2-(2-bromo-4-chlorophenyl)-1H-imidazo[4,5-f] [1,10] phenanthroline

Docking procedure 

The stepwise procedure for docking is as follows. (i) The receptor is loaded first into the program, usually in the PDB (Protein Data Bank) file format. This file contains all data related to its structure about the target macromolecule, including atomic coordinates and connectivity. (ii) The receptor is prepared for docking by the addition of hydrogen, setting charges etc. (iii) Next, the ligand or the desired compound (either in .pdb or mol2 format) is loaded with defined grid parameters. The grid creates the search space for docking since the binding point is located in the middle of the grid box. (iv). The docking program is run. (v) After the docking procedure is finished, the binding poses are visualized to examine the docking findings. The docking scores are generated that determines the attraction strength between a ligand and a receptor. The other key features relevant to binding such as hydrogen bonds, hydrophobic interactions etc. can be seen ¹⁵. In general, the stronger the expected binding affinity, the lower (more negative) the binding energy score.

Imidazole phenanthroline chemicals' interaction to DNA's minor groove is a topic of significant interest. The minor groove in DNA is a typical location for small molecules or ligands to interact, and their binding can interfere with DNA replication and transcription processes ^16-18. It can also produce significant biological effects. Hydrogen bonds between the nucleobases in DNA constitute the double helix of DNA's backbone. The pair that cytosine (C) and guanine (G) create is different from that of adenine (A) and thymine (T). Adenine (A) is a purine which forms two hydrogen bonds with thymine (T) which is a pyrimidine. This binding is important for the stability of the DNA structure and ensures that genetic information is correctly replicated and transmitted ¹⁹. 

The Adenosine-thymine (AT) - rich regions are parts of DNA that have a higher concentration of adenine and thymine bases. These regions possess unique structural properties and biological functions. Many genes have AT-rich promoter regions that promote the binding of transcription factors and RNA polymerase. AT-rich DNA regions (with two hydrogen bonds) have lower melting temperatures compared to GC-rich regions (with three hydrogen bonds). This can influence the flexibility and accessibility of these DNA regions. Proteins that recognize and bind to AT-rich regions regulate gene transcription and hence are essential for cell functions. Therefore, small molecules or drugs that bind specifically to AT-rich regions in the DNA minor groove can modulate the activity of target genes, with potential biological activities. The newly synthesized compounds were screened to study all possible ligand-protein interactions based on docking scores. These interactions are depicted pictorially ²⁰. 

RESULTS

The software program generates images which helps us to better understand the binding interactions between the ligands and the receptor. Pictorial representations of ligand interactions with DNA gyrase were obtained from the software, which clearly gives information related to the binding poses, distances of key interactions and any conformational changes brought about by binding in the ligand or protein. The binding of compounds 4a with DNA is shown by Figure 1, 4b by Figure 2, 4c by Figure 3 and 4d by Figure 4 respectively.

 

 

 

  

 

The binding free energy and interaction calculations between synthesized compounds and DNA gyrase are essential for understanding their potential as antimicrobial agents or drug candidates. The ligand-receptor complex's stability is determined by the binding free energy (ΔG bind). It describes the degree to which the synthetic compounds and DNA gyrase interact to create complexes. A lower binding free energy indicates stronger binding affinity, reflecting effective enzyme inhibition. Nonpolar interactions, hydrogen bonds, and Van der Waals forces are examples of interactions that have a big impact on the compound's binding affinity to DNA gyrase active site residues.

When it comes to drug design and molecular interactions, the calculation of binding affinity using binding free energy, which is expressed in kcal/mol, is a crucial element of pharmacological research. The interactions between the synthesized chemicals and their binding free energy with DNA-gyrase through hydrogen bonding is summarized in the table below:

 

 

Table 1: Binding free energy and interactions among the synthesized compounds [4a-4d] with DNA-gyrase

 

 

The docking scores generated determine the affinity of the ligand for the receptor. Docking scores are inversely proportional to their binding affinity. In other words, a lower docking score indicates a stronger binding affinity, while a higher score suggests weaker binding. Based on these scores, compounds with better affinities can be ranked and synthesized accordingly. Structural modifications in the resulting compounds can be made and their binding interactions can well be predicted. 

DISCUSSION

The binding interactions and characteristics of four synthesized compounds (4a to 4d) with the target enzyme, DNA gyrase was evaluated based on parameters such as binding free energy and docking scores using thorough computational investigations. The Molecular Mechanics Generalized Born Surface Area (MM-GBSA) approach is used in the study to compute a number of binding metrics.   Amongst all, compound 4d has the best docking score (-5.286), which suggests that it binds more effectively to target DNA gyrase compared to the others. MMGBSA dG Bind (in kcal/mol) values closer to zero or more negative indicates better binding affinity. Here, compound 4c has the most favourable free energy (-55.46), suggesting it has a strong interaction with the protein. MMGBSA Bind Coulomb measures the binding energy contributions from electrostatic forces. It is the energy obtained via the Coulombic interactions between the protein and the ligand's charged groups. Lower values (more negative) indicate stronger favourable interactions. Compound 4b (-15.99) has notable favourable contributions in this regard, suggesting strong electrostatic interactions. Conversely, 4c has a positive value (8.21), indicating repulsion which may affect its overall binding affinity. MMGBSA dG Bind Covalent refers to the energy from covalent bonding. Compound 4a shows the highest positive value (6.15), which implies that while there may be benefits from covalent interactions, it also increases the strain on binding.  As most of these compounds are not covalent bond inhibitors, positive values indicate a less favourable energetics. MMGBSA dG Bind Lipo refers to the lipophilic energy contribution to binding. For this, negative values indicate favourable hydrophobic interactions. Compound 4c has the most negative lipophilic contribution (-16.50), which suggests that it has strong hydrophobic interactions. MMGBSA dG Bind Solv GB is the  solvation free energy. Lower (or less positive) values are favourable and indicates the energy cost of solvation. Van der Waals interactions that are non-polar between the protein and the ligand are known as MMGBSA dG Bind vdW. Negative values suggest stronger favourable van der Waals interactions. Compound 4c shows strong van der Waals contributions (-45.03). Ligand Strain Energy accounts for the internal strain the ligand experiences in achieving its bound conformation. Lower values indicate a more favourable or less hindered structure for the ligand. Compound 4c has the lowest strain energy (3.46), making it structurally favourable for binding.

In short, compound 4d is the strongest candidate in terms of docking score overall but has higher ligand strain energy compared to others. While 4c has a strong binding energy, but the positive Coulombic contribution may indicate it may not be the best binder among the candidates.

CONCLUSION

Docking of molecules is an important asset in structure-oriented rational drug design. The mechanism of interaction between the produced compounds and the receptor or target is made clearer by the docking studies. Molecular docking studies using Auto Dock Tools (ADT) has become an important  approach in computational biology and drug discovery. By preparing the receptor and ligand, running the docking program and analysing their outcomes, researchers can get valuable information regarding binding interactions that are required for understanding their mechanisms and development of newer therapies. 

Acknowledgments: The authors thank Sophisticated Analytical Instrument Facility, IIT Madras, Chennai, Tamil Nadu for molecular docking studies.

Sponsorship: “This research received no specific grant from any funding agency.”

Conflict of interest statement: There is no conflict of interest with this work.

Authors’ contributions: The authors declare that this work was done by the authors named in this article and all liabilities pertaining to claims relating to the content of this article will be borne by them.

REFERENCES

1. Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. Computational protein ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc. 2016; 11(5):905-919. https://doi.org/10.1038/nprot.2016.051 PMid:27077332 PMCid:PMC4868550

2. Tang B, Shen F, Wan D, Guo BH, Wang YJ, Yi QY, Liu YJ. DNA-binding, molecular docking studies and biological activity studies of ruthenium(II) polypyridyl complexes. RSC Adv. 2017; 7:34945-34958. https://doi.org/10.1039/C7RA05103D

3. Ulucam G, Bagci U, Yılmaz AS, Yenturk B. Schiff-base ligands containing phenanthroline terminals: Synthesis, characterization, biological activities and molecular docking study. Spectrochim Acta A Mol Biomol Spectrosc. 2022; 279:121429. https://doi.org/10.1016/j.saa.2022.121429 PMid:35653808

4. Unver H, Berber B, Kanat B, Arafat M, Koparal AT, Doluca O. DNA Binding Effects of 2,2'-bipyridine and 1,10-phenanthroline ligands synthesized with benzimidazole copper (II) complexes: Crystal Structure, Molecular Docking, DNA Binding and Anti-Cancer Studies. Polyhedron 2022; 221:115834. https://doi.org/10.1016/j.poly.2022.115834

5. Iqbal M, Karim A, Ullah S, Ali S, Tahir MN. Synthesis, characterization, structural elucidation, molecular docking and antidiabetic studies of 1,10-phenanthroline based mononuclear Cu(II) and Zn(II) complexes. Polyhedron 2023; 246:116698. https://doi.org/10.1016/j.poly.2023.116698

6. Siddiquee K, Zhang S, Guida WC, Blaskovich MA, Greedy B, Lawrence HR, Yip ML, Jove R, McLaughlin MM, Lawrence NJ, Sebti SM, Turkson J. Selective chemical probe inhibitor of Stat3, identified through structure-based virtual screening, induces antitumor activity. Proc. Natl. Acad. Sci. 2007; 104(18):7391-7396. https://doi.org/10.1073/pnas.0609757104 PMid:17463090 PMCid:PMC1863497

7. Li C, Xu L, Wolan DW, Wilson IA, Olson AJ. Virtual screening of human 5-aminoimidazole-4-carboxamide ribonucleotide transformylase against the NCI diversity set by use of AutoDock to identify novel nonfolate inhibitors. J. Med. Chem. 2004; 47(27):6681-6690. https://doi.org/10.1021/jm049504o PMid:15615517

8. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 2004; 3(11):935-949. https://doi.org/10.1038/nrd1549 PMid:15520816

9. Collin F, Karkare S, Maxwell A. Exploiting bacterial DNA gyrase as a drug target: current state and perspectives. Appl. Microbiol. Biotechnol. 2011; 92(3):479-497. https://doi.org/10.1007/s00253-011-3557-z PMid:21904817 PMCid:PMC3189412

10. Heddle J, Maxwell A. Quinolone-binding pocket of DNA gyrase: role of GyrB. Antimicrob. Agents. Chemother. 2002; 46(6):1805-1815. https://doi.org/10.1128/AAC.46.6.1805-1815.2002 PMid:12019094 PMCid:PMC127264

11. Thomsen R, Christensen MH. MolDock: a new technique for high-accuracy molecular docking. J. Med. Chem. 2006; 49(11):3315-3321. https://doi.org/10.1021/jm051197e PMid:16722650

12. Garrett RH, Grisham CM. Biochemistry. 5th, International ed. United States: Mary Finch 2013: 949.

13. Wigley DB, Davies GJ, Dodson EJ, Maxwell A, Dodson G. Crystal structure of an N-terminal fragment of the DNA gyrase B protein. Nat. 1991; 351(6328):624-629. https://doi.org/10.1038/351624a0 PMid:1646964

14. Goldman BB, Wipke WT. Molecular docking using quadratic shape descriptors (QSDock). Proteins 2000; 38(1):79-94. https://doi.org/10.1002/(SICI)1097-0134(20000101)38:1<79::AID-PROT9>3.0.CO;2-U

15. Mazumder A, Sutton CL, Sigman DS. 1,10-Phenanthroline-Linked Escherichia coli Trp Repressor as a Site-Specific Scission Reagent. Metal Ion Requirement. Inorg. Chem. 1993; 32(16):3516-3520. https://doi.org/10.1021/ic00068a022

16. Bales BC, Kodama T, Weledji YN, Pitie M, Meunier B, Greenberg MM. Mechanistic studies on DNA damage by minor groove binding copper-phenanthroline conjugates. Nucleic Acids Res. 2005; 33(16):5371-5379. https://doi.org/10.1093/nar/gki856 PMid:16186134 PMCid:PMC1235636

17. Lauria T, Slator C, McKee V, Muller M, Stazzoni S, Crisp AL, Carell T, Kellett A. A Click Chemistry Approach to Developing Molecularly Targeted DNA Scissors. Chemistry 2020; 26(70):16782-16792. https://doi.org/10.1002/chem.202002860 PMid:32706904

18. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 2009; 30(16):2785-2791. https://doi.org/10.1002/jcc.21256 PMid:19399780 PMCid:PMC2760638

19. Sanner MF. Python: a programming language for software integration and development. J. Mol. Graph 1999; 17(1):57-61.

20. Magdalena Rajewska, Katarzyna Wegrzyn, Igor Konieczny. AT-rich region and repeated sequences - the essential elements of replication origins of bacterial replicons. FEMS Microbiol Rev. 2012; 36(2):408-434. https://doi.org/10.1111/j.1574-6976.2011.00300.x PMid:22092310