Multi-Conformation Docking and Molecular Dynamics Study of Neolignan Compounds from Ocimum sanctum L. Targeting Estrogen Receptor Alpha

Fawwaz Muhammad Fauzi, Desti Kameliani

Abstract


Estrogen receptor alpha (ERalpha)-mediated breast cancer is the main target of hormone therapy. However, the long-term use of Selective Estrogen Receptor Modulators (SERMs) however, can lead to side effects and resistance. This study evaluated how well selected phenolic derivatives from Ocimum sanctum L. to various ER? conformations and to assess the initial stability of selected ligand-receptor complexes in silico. Five compounds, dominated by the neolignan group with one flavonoid derivative, were molecularly docked against four ERalpha structures representing the apo, agonist, and SERM states using AutoDock-GPU, with method validation through co-crystal ligand redocking. The binding affinity and key residue interactions were analyzed to assess cross-conformation consistency. The most stable ligand candidates were further analyzed using molecular dynamics simulations for 30 ns in the agonist and SERM conformations to evaluate the initial structural stabilities of the protein–ligand complexes. The docking results showed that most compounds had ERalpha conformation-dependent affinity; however, Tulsinol D exhibited the most consistent affinity profile and maintained interactions with key ERalpha residues across all tested conformations. Molecular dynamics simulations showed that the ERalpha–Tulsinol D complex had good initial stability, characterized by protein backbone stability, reasonable residue flexibility, maintained structural compactness, and stable ligand positioning within the binding pocket. Based on these results, Tulsinol D has potential as an in silico candidate inhibitor of ER? based on phenolic metabolites from O. sanctum and warrants further investigation through advanced computational studies and experimental validation.


Keywords


Estrogen receptor alpha; in silico study; molecular dynamics simulation; neolignan; Ocimum sanctum L.

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Abraham, M., Alekseenko, A., Andrews, B., Basov, V., Bauer, P., Bird, H., Briand, E., Brown, A., Doijade, M., Fiorin, G., Fleischmann, S., Gorelov, S., Gouaillardet, G., Gray, A., Irrgang, M. E., Jalalypour, F., Johansson, P., Kutzner, C., ?azarski, G., … Lindahl, E. (2025). GROMACS Documentation Release 2025.4 GROMACS development team.

Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindahl, E. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1–2, 19–25. https://doi.org/10.1016/j.softx.2015.06.001

Afendi, F. M., Okada, T., Yamazaki, M., Hirai-Morita, A., Nakamura, Y., Nakamura, K., Ikeda, S., Takahashi, H., Altaf-Ul-Amin, Md., Darusman, L. K., Saito, K., & Kanaya, S. (2012). KNApSAcK Family Databases: Integrated Metabolite–Plant Species Databases for Multifaceted Plant Research. Plant and Cell Physiology, 53(2), e1–e1. https://doi.org/10.1093/pcp/pcr165

Berman, H. M. (2000). The Protein Data Bank. Nucleic Acids Research, 28(1), 235–242. https://doi.org/10.1093/nar/28.1.235

Bhattarai, K., Bhattarai, R., Pandey, R. D., Paudel, B., & Bhattarai, H. D. (2024). A Comprehensive Review of the Phytochemical Constituents and Bioactivities of Ocimum tenuiflorum. TheScientificWorldJournal, 2024, 8895039. https://doi.org/10.1155/2024/8895039

BIOVIA. (2025). BIOVIA Discovery Studio Visualizer 2025. Dassault Systèmes.

Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I., & Jemal, A. (2024). Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3), 229–263. https://doi.org/10.3322/caac.21834

Buijs, S. M., Koolen, S. L. W., Mathijssen, R. H. J., & Jager, A. (2024). Tamoxifen Dose De-Escalation: An Effective Strategy for Reducing Adverse Effects? Drugs, 84(4), 385–401. https://doi.org/10.1007/s40265-024-02010-x

Dubey, A., Al-Lohedan, H. A., Ali, M. S., & Ragusa, A. (2025). Integrative in silico analysis to explore the potential of Zingiberaceae compounds to inhibit estrogen receptor alpha activity in breast cancer. Journal of Molecular Graphics and Modelling, 138, 109023. https://doi.org/10.1016/j.jmgm.2025.109023

Eiler, S., Gangloff, M., Duclaud, S., Moras, D., & Ruff, M. (2001). Overexpression, Purification, and Crystal Structure of Native ER? LBD. Protein Expression and Purification, 22(2), 165–173. https://doi.org/10.1006/prep.2001.1409

Fanning, S. W., Hodges-Gallagher, L., Myles, D. C., Sun, R., Fowler, C. E., Plant, I. N., Green, B. D., Harmon, C. L., Greene, G. L., & Kushner, P. J. (2018). Specific stereochemistry of OP-1074 disrupts estrogen receptor alpha helix 12 and confers pure antiestrogenic activity. Nature Communications, 9(1), 2368. https://doi.org/10.1038/s41467-018-04413-3

Fauzi, F. M., Wutsqa, Y. U., & Rohmatika, N. A. (2024). Studi Bioavailabilitas Dan Molecular Docking Senyawa Fenolik Ocimum sanctum L. sebagai Inhibitor Reseptor Estrogen Alfa pada Sel Kanker Payudara. Jurnal Kefarmasian Akfarindo, 09(01), 49–56. https://www.rcsb.org/structure/3ERT

Gautam, S., Maurya, R., Vikal, A., Patel, P., Thakur, S., Singh, A., Gupta, G. Das, & Kurmi, B. Das. (2025). Understanding drug resistance in breast cancer: Mechanisms and emerging therapeutic strategies. Medicine in Drug Discovery, 26, 100210. https://doi.org/10.1016/j.medidd.2025.100210

Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10(5), 449–461. https://doi.org/10.1517/17460441.2015.1032936

Hanwell, M. D., Curtis, D. E., Lonie, D. C., Vandermeersch, T., Zurek, E., & Hutchison, G. R. (2012). Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. Journal of Cheminformatics, 4(1), 17. https://doi.org/10.1186/1758-2946-4-17

Hasan, M. R., Alotaibi, B. S., Althafar, Z. M., Mujamammi, A. H., & Jameela, J. (2023). An Update on the Therapeutic Anticancer Potential of Ocimum sanctum L.: “Elixir of Life”. Molecules (Basel, Switzerland), 28(3). https://doi.org/10.3390/molecules28031193

Hassan, A. M., Gattan, H. S., Faizo, A. A., Alruhaili, M. H., Alharbi, A. S., Bajrai, L. H., AL-Zahrani, I. A., Dwivedi, V. D., & Azhar, E. I. (2024). Evaluating the Binding Potential and Stability of Drug-like Compounds with the Monkeypox Virus VP39 Protein Using Molecular Dynamics Simulations and Free Energy Analysis. Pharmaceuticals, 17(12), 1617. https://doi.org/10.3390/ph17121617

He, X., Man, V. H., Yang, W., Lee, T.-S., & Wang, J. (2020). A fast and high-quality charge model for the next generation general AMBER force field. The Journal of Chemical Physics, 153(11), 114502. https://doi.org/10.1063/5.0019056

Hollingsworth, S. A., & Dror, R. O. (2018). Molecular Dynamics Simulation for All. Neuron, 99(6), 1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011

Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55

Jablonský, M., Šteklá?, M., Majová, V., Gall, M., Matúška, J., Pito?ák, M., & Bu?inský, L. (2022). Molecular docking and machine learning affinity prediction of compounds identified upon softwood bark extraction to the main protease of the SARS-CoV-2 virus. Biophysical Chemistry, 288, 106854. https://doi.org/10.1016/j.bpc.2022.106854

Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983). Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics, 79(2), 926–935. https://doi.org/10.1063/1.445869

Lee, J., Hitzenberger, M., Rieger, M., Kern, N. R., Zacharias, M., & Im, W. (2020). CHARMM-GUI supports the Amber force fields. The Journal of Chemical Physics, 153(3). https://doi.org/10.1063/5.0012280

Linowiecka, K., Szpotan, J., Godlewska, M., Gawe?, D., Zarakowska, E., Gackowski, D., Bro?yna, A. A., & Foksi?ski, M. (2024). Selective Estrogen Receptor Modulators’ (SERMs) Influence on TET3 Expression in Breast Cancer Cell Lines with Distinct Biological Subtypes. International Journal of Molecular Sciences, 25(16), 8561. https://doi.org/10.3390/ijms25168561

Masand, V. H., Al-Hussain, S. A., Alzahrani, A. Y., Al-Mutairi, A. A., Hussien, R. A., Samad, A., & Zaki, M. E. A. (2024). Estrogen Receptor Alpha Binders for Hormone-Dependent Forms of Breast Cancer: e-QSAR and Molecular Docking Supported by X-ray Resolved Structures. ACS Omega, 9(14), 16759–16774. https://doi.org/10.1021/acsomega.4c00906

McDougal, D. P., Pederick, J. L., Novick, S. J., Jovcevski, B., Warrender, A. K., Pascal, B. D., Griffin, P. R., & Bruning, J. B. (2025). A ternary switch model governing ER? ligand binding domain conformation. Nature Communications, 16(1), 10363. https://doi.org/10.1038/s41467-025-65323-9

Mills, J. N., Rutkovsky, A. C., & Giordano, A. (2018). Mechanisms of resistance in estrogen receptor positive breast cancer: overcoming resistance to tamoxifen/aromatase inhibitors. Current Opinion in Pharmacology, 41, 59–65. https://doi.org/10.1016/j.coph.2018.04.009

Mora Lagares, L., Minovski, N., Caballero Alfonso, A. Y., Benfenati, E., Wellens, S., Culot, M., Gosselet, F., & Novi?, M. (2020). Homology Modeling of the Human P-glycoprotein (ABCB1) and Insights into Ligand Binding through Molecular Docking Studies. International Journal of Molecular Sciences, 21(11), 4058. https://doi.org/10.3390/ijms21114058

Salmaso, V., & Moro, S. (2018). Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview. Frontiers in Pharmacology, 9. https://doi.org/10.3389/fphar.2018.00923

Santos-Martins, D., Solis-Vasquez, L., Tillack, A. F., Sanner, M. F., Koch, A., & Forli, S. (2021). Accelerating AutoDock 4 with GPUs and Gradient-Based Local Search. Journal of Chemical Theory and Computation, 17(2), 1060–1073. https://doi.org/10.1021/acs.jctc.0c01006

Shiau, A. K., Barstad, D., Loria, P. M., Cheng, L., Kushner, P. J., Agard, D. A., & Greene, G. L. (1998). The Structural Basis of Estrogen Receptor/Coactivator Recognition and the Antagonism of This Interaction by Tamoxifen. Cell, 95(7), 927–937. https://doi.org/10.1016/S0092-8674(00)81717-1

Suzuki, A., Shirota, O., Mori, K., Sekita, S., Fuchino, H., Takano, A., & Kuroyanagi, M. (2009). Leishmanicidal Active Constituents from Nepalese Medicinal Plant Tulsi (Ocimum sanctum L.). Chemical and Pharmaceutical Bulletin, 57(3), 245–251. https://doi.org/10.1248/cpb.57.245

Tanenbaum, D. M., Wang, Y., Williams, S. P., & Sigler, P. B. (1998). Crystallographic comparison of the estrogen and progesterone receptor’s ligand binding domains. Proceedings of the National Academy of Sciences, 95(11), 5998–6003. https://doi.org/10.1073/pnas.95.11.5998

Tian, C., Kasavajhala, K., Belfon, K. A. A., Raguette, L., Huang, H., Migues, A. N., Bickel, J., Wang, Y., Pincay, J., Wu, Q., & Simmerling, C. (2020). ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. Journal of Chemical Theory and Computation, 16(1), 528–552. https://doi.org/10.1021/acs.jctc.9b00591

Vajdos, F. F., Hoth, L. R., Geoghegan, K. F., Simons, S. P., LeMotte, P. K., Danley, D. E., Ammirati, M. J., & Pandit, J. (2007). The 2.0 Å crystal structure of the ER? ligand?binding domain complexed with lasofoxifene. Protein Science, 16(5), 897–905. https://doi.org/10.1110/ps.062729207

Wu, B.-X., Wu, H.-T., Lan, Y.-Z., Chen, W.-J., Yu, X.-N., Liu, J.-W., & Liu, J. (2025). Targeting estrogen receptor alpha in breast cancer for novel therapies resistance mechanisms and future directions. Discover Oncology. https://doi.org/10.1007/s12672-025-04302-4

Yao, J., Tao, Y., Hu, Z., Li, J., Xue, Z., Zhang, Y., & Lei, Y. (2023). Optimization of small molecule degraders and antagonists for targeting estrogen receptor based on breast cancer: current status and future. Frontiers in Pharmacology, 14. https://doi.org/10.3389/fphar.2023.1225951

Yusuff, O. K., Omotosho, K., & Raji, A. T. (2023). COMPARATIVE CHAINS DYNAMICS OF TRIOSEPHOSPHATE ISOMERASE INVESTIGATED BY MOLECULAR DYNAMICS SIMULATION. Journal of Chemical Society of Nigeria, 48(1). https://doi.org/10.46602/jcsn.v48i1.856

Zhang, D., & Lazim, R. (2017). Application of conventional molecular dynamics simulation in evaluating the stability of apomyoglobin in urea solution. Scientific Reports, 7(1), 44651. https://doi.org/10.1038/srep44651




DOI: https://doi.org/10.14421/biomedich.2026.151.411-420

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Copyright (c) 2026 Fawwaz Muhammad Fauzi, Desti Kameliani



Biology, Medicine, & Natural Product Chemistry
ISSN 2089-6514 (paper) - ISSN 2540-9328 (online)
Published by Sunan Kalijaga State Islamic University & Society for Indonesian Biodiversity.

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