In silico homology modeling and molecular docking analysis of phytochemicals against Haemonchus contortus galectin

Introduction

Haemonchosis caused by Haemonchus contortus is a highly pathogenic gastrointestinal nematode affecting small ruminants. Distribution of the parasite is across the tropical and subtropical regions. The parasite is responsible for severe anemia, and significant economic losses in the livestock sector due to lower weight accretion, decline in milk production [1] . The control of H. contortus largely depends on the use of anthelmintics; however, the rapid emergence and global spread of drug resistance have significantly compromised the effectiveness of anthelmintics available [2,3,4] . In this context, medicinal plants have emerged as promising alternatives to available anthelmintics due to their rich diversity of bioactive compounds with antiparasitic properties [5,6,7] . India is particularly known for its longstanding tradition of indigenous medicinal systems such as Ayurveda, Siddha, and Unani, which utilize plant-based preparations for parasite control. Various phytochemicals, including alkaloids, flavonoids, tannins, and terpenoids obtained from medicinal plants such as Azadirachta indica, Butea monosperma, and Vitex negundo, have demonstrated notable anthelmintic potential against H. contortus [8,9] . Among potential molecular targets, Galectin of H. contortus is involved in host-parasite interactions. Galectins are β-galactoside-binding lectins that play a key role in immune modulation, thereby promoting parasite survival through the alteration of host immune responses [10] . Targeting galectins may disrupt parasite establishment and persistence within the host, thereby making them promising candidates for drug discovery. Molecular docking has become a powerful computational approach in structure-based drug design in recent years. It enables the prediction of binding affinity and interactions between International Journal of Advanced Biochemistry Research https://www.biochemjournal.com ligands and target proteins, thereby accelerating the identification of potential therapeutic compounds [11] . This method is particularly useful for screening phytochemicals from medicinal plants, reducing the need for extensive time consuming experimental trials. Medicinal plants such as Coriandrum sativum (Coriander), Cinnamomum verum (Cinnamon), Terminalia bellerica (Bahera), and Ocimum sanctum (Tulsi) are reported to possess anthelmintic activity [12, 13, 14, 15] . Therefore, the present study aims to perform molecular docking analysis of H. contortus galectin with bioactive compounds derived from medicinal plants such as C. sativum, C. verum, T. bellerica, and O. sanctum available in India. This study seeks to identify potential plant-based anthelmintic compounds that may serve as effective, ecofriendly, and sustainable alternatives to conventional anthelmintics in controlling haemonchosis.

Materials and Methods

Homology modeling of three dimensional model of H. contortus Galectin amino acid sequence

The 283 amino acid sequence of H. contortus Galectin was retrieved from NCBI database (Accession number: AAP05998.1). The amino acid sequence was submitted to SWISS-MODEL (https://swissmodel.expasy.org/) server to identify the template similar to the H. contortus Galectin. The suitable structural template was identified based on the percentage of identity of sequence, and GMQE score to build the three dimensional model. The three-dimensional structure of the target protein was constructed using the selected template through automated homology modeling [16] . The initial protein model was refined in GalaxyRefine (https://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE) to improve the structural quality and conformations [17] . The refined model was then validated in SAVESv6.1 server using ERRAT, VERIFY3D, and PROCHECK program to perform the structural validation (https://saves.mbi.ucla.edu/). The PROCHECK program generates Ramachandran plot which was used to assess backbone dihedral angles and overall model quality [18] .

Preparation of three dimensional model of H. contortus Galectin for docking study

The validated protein structure was further prepared for molecular docking analysis using AutoDock Tools [21] . The preparation steps included removal of water molecules, checking for missing atoms and repair of them, addition of polar hydrogen atoms, and assignment of AutoDock4 (AD4) atom type to the protein structure.

Ligand identification, ADMET analysis, and preparation for the docking study

The phytochemical constitutents of plants with known anthelmintic property such as C. sativum, C. verum, T. bellerica, and O. sanctum were identified through literature survey of published articles [14,20,21,22,23,24] . The SMILES representations of the selected phytochemicals were retrieved from the PubChem database, and their pharmacokinetic properties were evaluated using the SwissADME server (https://swissadme.ch/). The ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) analysis was performed to assess drug-likeness, pharmacokinetic behavior and toxic property of the compounds. Drug-likeness was evaluated based on key physicochemical and pharmacokinetic parameters, including lipophilicity, aqueous solubility, gastrointestinal absorption, P-glycoprotein substrate, cytochrome P450 1A2, 2C19, 2C9, 2D6, 3A4 and compliance with Lipinski's rule of Five, Veber's rule, and Egan's rule. The toxicity (T) of the ligands were analysed in ProTox-III server (https://tox.charite.de/protox3/index.php?site=home) [25] . The chemical structures of the identified ligands were retrieved in SDF format from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The SDF format of the ligands were then converted into PDB format using OpenBabel tool [26] . The energy minimization of the ligand was performed using Merck Molecular Force Field 1994 (MMFF94) and minimization was run up to 250 steps [27] . The ligands were subsequently prepared for the molecular docking study in AutoDock tool by addition of polar hydrogens and gasteiger charges.

Molecular docking study of H. contortus Galectin and ligands

A grid box was defined to encompass the entire protein structure with appropriate dimensions, ensuring full ligand flexibility within the docking space and complete exploration of potential binding sites by the ligands. Molecular docking study was performed using AutoDock to predict the binding affinity and interaction pattern between galectin and selected ligands. The docking algorithm uses a scoring function based on binding free energy (kcal/mol) to evaluate ligand conformations.

Analysis of Docking study results

The best docked conformations were selected based on the lowest binding energy. Protein-ligand interactions were analyzed for hydrogen bonds, hydrophobic interactions, and other non-covalent interactions using visualization tool, Discovery Studio Visualizer [28] , and a web tool, PLIP (Protein -Ligand Interaction Profiler) [29] (https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index). Key interacting amino acid residues involved in ligand binding were identified and bond distance were analyzed to evaluate the strength and stability of protein-ligand binding.

Visualization

The docked complexes were visualized in 3D and 2D formats to analyze binding orientation and interaction patterns in Discovery Studio Visualizer and PyMol structural images were generated for presentation and interpretation.

Results

The 283 amino acid sequence of H. contortus Galectin was modeled using SWISS-MODEL (Fig 1). The server identified a 32 kDa Beta-galactosidase-binding lectin alpha fold DB model of LEG1_HAECo(gene: GAL-1) as template with the sequence identity of 98.94% and GMQE score of 0.95, indicating the higher reliability. The generated model was refined using GalaxyRefine server. The refined model was validated using SAVESv6.1 server, yielding an ERRAT score of 93.609 (Fig 2). ERRAT score indicated the model is highly reliable non-bonded atomic interactions. Generally, models with ERRAT score of more than 90% indicates high quality of the model. Generally, protein models with an ERRAT quality factor greater than 90% are considered to be of high quality, indicating a low level of errors in nonbonded atomic interactions and good overall structural reliability [30] . In VERIFY3D, 92.58% of residues are located in favorable structural environments, indicating the good compatibility between the amino acid residues sequence and the predicted three dimensional structure (Fig 2). Residues with ≥ 80% of residues with score ≥ 0.2, the protein model is considered structurally reliable [31] . Ramachandran plot evaluates the stereochemical quality of protein structures based on backbone dihedral angles (φ and ψ), and a reliable model typically exhibits more than 90% residues in the most favored regions with minimal residues in disallowed regions [32] . Ramachandran plot generated had 95.5% residues in most favored region, 4.1% residues in allowed region, 0.0% residues in generously allowed region, and 0.4% residues in disallowed region. 0.4% residue in disallowed region indicating the overall stereochemical quality of the model (Fig 2). Collectively, these validation metrics indicate that the modeled protein possesses high structural integrity and is well-suited for downstream molecular docking studies. The refined H. contortus Galectin was then prepared for the docking study. The phytochemicals of C. sativum, C. verum, T. bellerica, and O. sanctum identified through literature study are enlisted in Table 1. These phytochemicals were then evaluated for their physiochemical and pharmacokinetic properties in SwissADME. The initial screening was based on lipophilicity, solubility, gastrointestinal (GI) absorption, and bioavailability score. Lipophilicity was calculated by five prediction methods (iLOGP, XLOGP3, WLOGP, MLOGP, SILICOS-IT) and average of these methods were obtained as consensus LogP value. The compounds with LogP value between 0 and 5 were selected [33] . The solubility (Log S) of the compounds was predicted by three methods (ESOL, Ali, and SILICOS-IT). The solubility of the compounds were categorized as Insoluble, poorly soluble, moderately soluble, soluble, very soluble, and highly soluble in SwissADME. The compounds that were categorized as moderately soluble, soluble, very soluble or highly soluble in atleast two methods were selected [34] . Gastrointestinal (GI) absorption was classified as high or low absorption. and the compounds with high GI absorption were filtered. Bioavailability score of atleast 0.5 or above was considered good, the drug compounds which has more than 0.55 score in SwissADME was selected [34,35] . These parameters supported the selection of compounds with favorable pharmacokinetic profiles. The selected phytochemical compounds were further screened based on drug-likeness criteria such as Lipinski's rule (Molecular weight ≤ 500 Da, LogP ≤ 5, Hydrogen bond donors ≤ 5, hydrogen bond acceptors ≤ 10, Veber's rule (Number of rotatable bonds ≤ 10, Topological Polar Surface Area (TPSA) ≤ 140 Ų), and Egan's rule (WLOGP ≤ 5.88, TPSA ≤ 131 Ų). Only phytochemicals that obeyed the above three rules were selected. Furthermore, the compounds were analyzed for cytochrome (CYP) P450 enzyme inhibition, including CYP1A2, CYP2C19, CYP2C9, CYP2D6, CYP3A4. The compounds that were non-CYP enzyme inhibitors or inhibitor of only one of the CYPs excluding CYP3A4, and CYP2D6 are considered acceptable. Inhibitor of multiple CYPs and inhibitor of CYP3A4, and CYP2D6 individually were excluded. The compounds were also evaluated for P-glycoprotein (P-gp) substrate potential, all compounds except catechin are found not to be P-gp substrates. Toxicity assessment of the selected phytochemicals using ProTox 3.0 categorized them into 6 classes. Compounds belonging to toxicity classes 5 and 6 were considered for further study due to their relatively lower toxicity profiles, whereas class 4 compounds (harmful category) were considered with caution depending on overall ADMET properties. This multi-step screening strategy enabled the identification of compounds with optimal pharmacological properties and acceptable safety profiles. A total of 29 ligands were docked against the prepared protein using AutoDock, where ellagic acid demonstrated the most favorable binding energy of -4.72 kcal/mol, compared to the other molecules. The molecular interaction analysis of ellagic acid was carried out using Biovia Discovery Studio and the PLIP server, revealed a welldefined binding pattern supported by multiple interaction types. Analysis using Biovia Discovery Studio identified key interacting amino acids, including LYS 225, PRO 227, GLU 249, GLU 252, and PHE 254, indicating that the ligand is positioned within a functionally relevant binding pocket (Fig 3). In PLIP, hydrophobic interactions were observed with PRO 227 (3.35 Å) and PHE 254 (3.33 Å), contributing to the stabilization of ellagic acid within the binding cavity through van der Waals forces (Fig 4). These interactions likely assist in maintaining the ligand orientation and reducing solvent exposure. Hydrogen bond analysis from both Biovia Discovery Studio and the PLIP server demonstrated consistent interactions with critical residues. Hydrogen bond interactions in molecular docking studies are typically considered significant when the bond distance is ≤3.5 Å, as shorter distances indicate stronger electrostatic attraction and improved ligand-protein binding stability. Strong hydrogen bonds were formed with GLU 249 (H-A: 1.99 Å; D-A: 2.71 Å), and PHE 254 (H-A:2.20, D-A:3.10), while an additional hydrogen bond with LYS 225 (H-A: 2.41 Å; D-A: 3.28 Å) was identified by PLIP. The short hydrogen bond distances, particularly with GLU 249, suggest high stability and specificity of these interactions, which play a crucial role in ligand binding affinity. A salt bridge interaction with LYS 225 (5.31 Å) further contributes to electrostatic stabilization, although the relatively longer distance indicates a weaker interaction compared to the hydrogen bonds. In docking studies, interactions within a distance of ≤4 Å are generally considered significant, although their strength is influenced by solvent effects and is typically less directional than hydrogen bonds [36,37] . Additionally, pocket analysis revealed the involvement of neighboring residues such as GLY 224, MET 226, ASN 250, and ARG 253, which may indirectly support ligand binding by shaping the binding environment. Overall, the interaction profile indicates that ellagic acid is stabilized within the galectin binding site through a combination of hydrogen bonding, hydrophobic interactions, and electrostatic forces. This multi-faceted interaction network supports favorable ligand accommodation and suggests that ellagic acid can serve as a potential lead compound. However, the moderate binding energy suggests that while ellagic acid is a promising candidate, it may not act as a strong inhibitor in its current form. Instead, it can be considered a potential lead compound for further optimization through structural modification or derivatization. In conclusion, the study identifies ellagic acid of T. bellerica as a biologically relevant and pharmacokinetically favorable ligand with moderate binding affinity toward H. contortus Galectin. These findings provide a foundation for future experimental validation and rational drug design efforts aimed at enhancing binding affinity and therapeutic efficacy.

Acknowledgement

AcknowledgementsThe authors declare that no specific funding, institutional support, or individuals contributed to this work that require acknowledgment.

References

1. Kaplan RM. Biology, epidemiology, diagnosis, and management of anthelmintic resistance in gastrointestinal nematodes of livestock. Veterinary Clinics of North America: Food Animal Practice. 2020;36(1):17-30.
2. Kaplan RM, Vidyashankar AN. An inconvenient truth: global worming and anthelmintic resistance. Veterinary Parasitology. 2012;186(1-2):70-78.
3. Kotze AC, Prichard RK. Anthelmintic resistance in Haemonchus contortus: a review of mechanisms and management strategies. International Journal for Parasitology: Drugs and Drug Resistance. 2021;16:4757.
4. Beesley NJ, et al. Anthelmintic resistance in ruminant parasites: challenges and sustainable control. Parasites & Vectors. 2022;15:1-15.
5. Eguale T, Giday M. In vitro anthelmintic activity of medicinal plants: a review of evidence. Journal of Ethnopharmacology. 2020;252:112-116.
6. Tariq T, Chishti KA, Chishti MZ, Ahmad F, Shawl AS. Anthelmintic activity of plant-derived compounds against gastrointestinal nematodes. Veterinary Parasitology. 2021;289:109345.
7. Kamaraj C, Rahuman AA, Elango G, Bagavan A, Zahir AA. Anthelmintic activity of botanical extracts against sheep gastrointestinal nematodes, Haemonchus contortus. Parasitology Research. 2011;109(1):37-45.
8. Githiori JB, Athanasiadou S, Thamsborg SM. Use of plants in novel approaches for control of gastrointestinal helminths in livestock with emphasis on small ruminants. Veterinary Parasitology. 2006;139(4):308-320.
9. Hoste H, Torres-Acosta JFJ, Sandoval-Castro CA, et al. Tannin-containing legumes as a model for nutraceuticals against digestive parasites in livestock. Veterinary Parasitology. 2015;212(1-2):5-17.
10. Naqvi MA, Memon MA, Jamil T, et al. Galectin domain containing protein from Haemonchus contortus modulates immune functions of goat PBMCs and regulates CD4+ T-helper cells in vitro. Biomolecules. 2020;10(1):116.
11. Morris GM, Lim-Wilby M. Molecular docking. In: Kukol A, editor. Molecular Modeling of Proteins. Humana Press; 2008.
12. Eguale T, Tilahun G, Debella A, Feleke A, Makonnen E. In vitro and in vivo anthelmintic activity of crude extracts of Coriandrum sativum against Haemonchus contortus. Journal of Ethnopharmacology. 2007;110(3):428-433.
13. Kumar BK, Kalyani DK, Machawal LM, Singh SS. Invitro anthelmintic activity of ethanolic and aqueous fruit extract of Terminalia bellerica. Indian Journal of Pharmaceutical Sciences. 2010:1061-1062.
14. Yadav V, Verma N, Singh A, Jaiswal R, Srivastava A. In-vitro anthelmintic activity of Ocimum sanctum leaf extract. International Journal of Advanced Pharmaceutical Research. 2021;5:89-91.
15. Murshed M, Alzaylaee H, Mares MM, et al. Evaluation of the anthelmintic effectiveness of Cinnamomum verum bark extract in mice infected with Aspiculuris tetraptera: in vitro and in vivo. Helminthologia. 2025;62(2):102.
16. Waterhouse A, Bertoni M, Bienert S, et al. SWISSMODEL: homology modelling of protein structures and complexes. Nucleic Acids Research. 2018;46(W1):W296-W303.
17. Heo L, Park H, Seok C. GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic Acids Research. 2013;41(W1):W384-W388.
18. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. Journal of Applied Crystallography. 1993;26(2):283-291.
19. Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. Journal of Computational Chemistry. 2009;30(16):2785-2791.
20. Asgarpanah J, Kazemivash N. Phytochemistry, pharmacology and medicinal properties of Coriandrum sativum L. African Journal of Pharmacy and Pharmacology. 2012;6(31):2340-2345.
21. Rao PV, Gan SH. Cinnamon: a multifaceted medicinal plant. Evidence-Based Complementary and Alternative Medicine. 2014;2014:642942.
22. Kumari S, Krishna MJ, Joshi AB, et al. A pharmacognostic, phytochemical and pharmacological review of Terminalia bellerica. Journal of Pharmacognosy and Phytochemistry. 2017;6(5):368376.
23. Sobhani Z, Mohtashami L, Amiri MS, et al. Ethnobotanical and phytochemical aspects of Coriandrum sativum L. Journal of Food Science. 2022;87(4):1386-1422.
24. Rao VMM, Shanti Prabha Y, Gend P, et al. A comprehensive review of phytochemistry and pharmacological activities of Ocimum sanctum Linn. International Journal of Zoology and Applied Biosciences. 2025;10(6):181-191.
25. Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Research. 2018;46(W1):W257-W263.
26. O'Boyle NM, Banck M, James CA, et al. Open Babel: an open chemical toolbox. Journal of Cheminformatics. 2011;3:33.
27. Halgren TA. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. Journal of Computational Chemistry. 1996;17(5-6):490-519.
28. BIOVIA, Dassault Systèmes. Discovery Studio Visualizer. San Diego, CA; 2019.
29. Schake P, Bolz SN, Linnemann K, Schroeder M. PLIP 2025: introducing protein-protein interactions to the protein-ligand interaction profiler. Nucleic Acids Research. 2025;53(W1):W463-W465.
30. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Science. 1993;2(9):1511-1519.
31. Eisenberg D, Lüthy R, Bowie JU. VERIFY3D: assessment of protein models with three-dimensional profiles. In: Methods in Enzymology. Vol 277. Academic Press; 1997:396-404.
32. Ramachandran GN, Ramakrishnan C, Sasisekharan V. Stereochemistry of polypeptide chain configurations. Journal of Molecular Biology. 1963;7(1):95-99.
33. Lipinski CA. Rule of five in 2021 and beyond: current status and future directions in drug-like property prediction. Drug Discovery Today. 2021;26(3):681688.
34. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports. 2017;7:42717.
35. Martin YC. A bioavailability score. Journal of Medicinal Chemistry. 2005;48(9):3164-3170.
36. Bosshard HR, Marti DN, Jelesarov I. Protein stabilization by salt bridges: concepts, experimental approaches and clarification of misunderstandings. Journal of Molecular Recognition. 2004;17(1):1-16.
37. Donald JE, Kulp DW, DeGrado WF. Salt bridges: geometrically specific, designable interactions. Proteins. 2011;79(3):898-915.