In Silico Molecular Docking of Murraya koenigii (L.) Spreng. Metabolites against the Androgen Receptor

Nayla Shaffa  Mardhiya; Muhammad Nahrawi  Udharaja; Viviana  Idris; Iswahyudi Iswahyudi

doi:10.70392/jpns.v3i2.53

Authors

Nayla Shaffa Mardhiya Faculty of Pharmacy, Universitas Mulawarman, Samarinda, East Kalimantan, Indonesia Author
Muhammad Nahrawi Udharaja Research and Development Laboratory, PT. Borneo Riseta Naturafarm, Kutai Kertanegara, East Kalimantan, Indonesia Author
Viviana Idris UPTD Pusat Kesehatan Masyarakat (Puskesmas) Sidomulyo, Dinas Kesehatan Kota Samarinda, Samarinda, Kalimantan Timur, Indonesia Author
Iswahyudi Iswahyudi PT Borneo Riseta Naturafarm Author

DOI:

https://doi.org/10.70392/jpns.v3i2.53

Keywords:

Murraya koenigii, Molecular docking, Androgen receptor, Carbazole alkaloids, AutoDock4, SwissADME, Prostate cancer

Abstract

Prostate cancer is strongly driven by androgen receptor (AR) signaling, making the AR ligand-binding domain an important therapeutic target. Murraya koenigii (L.) Spreng. contains diverse secondary metabolites with reported pharmacological activities, but their interaction with AR remains insufficiently characterized. This study evaluated 20 secondary metabolites of M. koenigii using molecular docking with AutoDock4, using SARM C-23 as the reference ligand. The docking protocol was validated by redocking the native ligand, yielding an RMSD of 0.77 Å. Nine metabolites showed more favorable predicted binding energies and lower inhibition constants than SARM C-23: mahanine, mahanimbine, isomahanine, pyrayafoline D, murrayazoline, O-methylmurrayamine, mahanimbinine, murrayacinine, and mahanimboline. Their predicted binding energies ranged from −10.03 to −11.48 kcal/mol, with Ki values of 3.84–44.60 nM. Interaction analysis indicated that these compounds occupied the AR binding site through predominantly hydrophobic and hydrogen-bond interactions. SwissADME analysis identified O-methylmurrayamine as having the most favorable overall predicted oral drug-likeness profile, although several high-affinity compounds showed excessive lipophilicity and/or poor predicted solubility. These findings identify promising AR-binding candidates from M. koenigii, particularly O-methylmurrayamine, which warrants further biochemical, cellular, pharmacokinetic, and in vivo validation.

References

Balakrishnan, R., Vijayraja, D., Jo, S.H., Ganesan, P., Su-Kim, I., Choi, D.K. Medicinal profile, phytochemistry, and phar-macological activities of Murraya koenigii and its primary bioactive compounds. Antioxidants 2020, 9(2), 101.

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A. Global cancer statistics 2018: GLOBOCAN esti-mates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018, 68, 394–424.

Lawrenti, H. Perkembangan Terapi Kanker Prostat. Cermin Dunia Kedokteran 2019, 46(8), 521–528.

Messner, E.A., Steele, T.M., Tsamouri, M.M., Hejazi, N., Gao, A.C., Mudryj, M., Ghosh, P.M. The androgen receptor in prostate cancer: Effect of structure, ligands and spliced variants on therapy. Biomedicines 2020, 8(10), 422.

Lucas-Herald, A.K., Touyz, R.M. Androgens and androgen receptors as determinants of vascular sex differences across the lifespan. Canadian Journal of Cardiology 2022, 38, 1854–1864.

Chauhan, B., Dedania, J., Mashru, R.C. Review on Murraya koenigii: Versatile role in management of human health. World Journal of Pharmaceutical and Pharmaceutical Sciences 2017, 6(3), 476–493.

Goel, A., Sharma, A., Kulshrestha, S. A phytopharmacological review on Murraya koenigii: an important medicinal plant. In-ternational Journal of Pharmaceutical Sciences Review and Research 2020, 62(2), 113–119.

Kumar, S.R., Loveleena, D., Godwin, S. Medicinal property of Murraya koenigii—a review. International Research Journal of Biological Sciences 2013, 2(9), 80–83.

Khaerunnisa, S.K., Suhartati, S., Awaluddin, R.A. Penelitian In Silico untuk Pemula. Airlangga University Press: Surabaya, In-donesia, 2023.

Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., Olson, A.J. AutoDock4 and Auto-DockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry 2009, 30(16), 2785-2791.

Rose, P.W., Prlić, A., Altunkaya, A., Bi, C., Bradley, A.R., Christie, C.H., Di Costanzo, L., Duarte, J.M., Dutta, S., Feng, Z., Green, R.K., Goodsell, D.S., Hudson, B., Kalro, T., Lowe, R., Peisach, E., Randle, C., Rose, A.S., Shao, C., Tao, Y.P., Valasatava, Y., Voight, M., Westbrook, J.D., Woo, J., Yang, H., Young, J.Y., Zardecki, C., Berman, H.M., Burley, S.K. The RCSB Protein Data Bank: Integrative view of protein, gene and 3D structural information. Nucleic Acids Research 2017, 45, D271–D281.

Baroroh, U., Biotek, M., Muscifa, Z.S., Destiarani, W., Rohmatullah, F.G., Yusuf, M. Molecular interaction analysis and vis-ualization of protein-ligand docking using Biovia Discovery Studio Visualizer. Indonesian Journal of Computational Biology 2023, 2(1), 22–30.

Li, Z., Wan, H., Shi, Y., Ouyang, P. Personal experience with four kinds of chemical structure drawing software: review on ChemDraw, ChemWindow, ISIS/Draw, and ChemSketch. Journal of chemical information and computer sciences 2004, 44(5), 1886-1890.

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(1), 42717.

Jia, C.Y., Li, J.Y., Hao, G.F., Yang, G.F. A drug-likeness toolbox facilitates ADMET study in drug discovery. Drug discovery today 2020, 25(1), 248-258.

Lipinski, C.A., Lombardo, F., Dominy, B.W., Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews 2001, 46(1–3):3–26.

Reisch, J., Goj, O., Wickramasinghe, A., Bandara Herath, H.M.T., Henkel, G. Carbazole alkaloids from seeds of Murraya koenigii. Phytochemistry 1992, 31(8), 2877–2879.

Ramsewak, R.S., Nair, M.G., Strasburg, G.M., DeWitt, D.L., Nitiss, J.L. Biologically active carbazole alkaloids from Murraya koenigii. Journal of Agricultural and Food Chemistry 1999, 47(2), 444–447.

Veber, D.F., Johnson, S.R., Cheng, H.Y., Smith, B.R., Ward, K.W., Kopple, K.D. Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry 2002, 45(12), 2615–2623.

Delaney, J.S. ESOL: Estimating aqueous solubility directly from molecular structure. Journal of Chemical Information and Computer Sciences 2004, 44(3), 1000–1005.

Egan, W.J., Merz, K.M., Baldwin, J.J. Prediction of drug absorption using multivariate statistics. Journal of Medicinal Chemis-try 2000, 43(21), 3867–3877.

Guzman-Pando, A., Ramirez-Alonso, G., Arzate-Quintana, C., Camarillo-Cisneros, J. Deep learning algorithms applied to computational chemistry. Molecular Diversity 2024, 28(4), 2375-2410.

Castellino, N.J., Montgomery, A.P., Danon, J.J., Kassiou, M. Late-stage functionalization for improving drug-like molecular properties. Chemical reviews 2023, 123(13), 8127-8153.

Jacob, A., Raj, R., Allison, D.B., Myint, Z.W. Androgen receptor signaling in prostate cancer and therapeutic strategies. Cancers 2021, 13(21), 5417

Westaby, D., Fenor de La Maza, M.D., Paschalis, A., Jimenez-Vacas, J.M., Welti, J., de Bono, J., Sharp, A. A new old target: androgen receptor signaling and advanced prostate cancer. Annual Review of Pharmacology and Toxicology 2022, 62, 131–153.