Research Article Investigation of soyasapogenols from soybean seed germs as Anti-apoptotic agents in colon cancer using an In silico approach
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https://doi.org/10.15625/vjbt-23376Keywords:
Anti-cancer, apoptosis, docking, Glycine max, in silic, soyasapogenol.Abstract
Colorectal cancer is one of the most common malignancies worldwide, with 1.9 million new cases and 903,859 deaths recorded in 2022. Current treatments remain constrained by limited efficacy and safety concerns, emphasizing the need for new therapeutic options. Soybean [Glycine max (L.) Merr.] seed germs are rich in triterpenoids and represent a promising source of anticancer compounds. Preliminary phytochemical analysis confirmed the presence of alkaloids, flavonoids, terpenoids, cardiac glycosides, coumarins, saponins, and tannins, while steroids were absent, reflecting the metabolic diversity of this material. This study investigated the anticancer activity of soyasapogenol A and B, triterpenoids derived from soybean seed germs, by inhibiting the anti-apoptotic protein Bcl-2. In silico docking analysis revealed that soyasapogenol B exhibited a higher binding affinity and more stable interaction with the Bcl-2 protein (PDB: 6GL8) compared to soyasapogenol A and the reference drug paclitaxel. Molecular dynamics simulations over 100 nanoseconds supported the persistence of these interactions. ADMET profiling using pkCSM predicted that soyasapogenol B exhibits favorable pharmacokinetic properties, including high intestinal absorption, moderate distribution, and the absence of significant toxicities. Collectively, these findings identify soyasapogenol B as a potential Bcl-2 inhibitor with therapeutic relevance to colorectal cancer. The results provide a basis for further experimental validation and the development of soybean seed germs-derived compounds as novel anticancer agents.
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Abdelwahab A. A., Elattar M. A., and Fawzi S. A. (2025). Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions. BioMedical Engineering OnLine, 24(1), 93. https://doi.org/10.1186/s12938-025-01412-6
Agu P. C., Afiukwa C. A., Orji O. U., Ezeh E. M., Ofoke I. H., Ogbu C. O., et al. (2023). Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Scientific Reports, 13(1), 13398. https://doi.org/10.1038/s41598-023-40160-2
Alqethami A., and Aldhebiani A. Y. (2021). Medicinal plants used in Jeddah, Saudi Arabia: Phytochemical screening. Saudi Journal of Biological Sciences, 28(1), 805–812. https://doi.org/10.1016/j.sjbs.2020.11.013
Alsedfy M. Y., Ebnalwaled A. A., Moustafa M., and Said A. H. (2024). Investigating the binding affinity, molecular dynamics, and ADMET properties of curcumin-IONPs as a mucoadhesive bioavailable oral treatment for iron deficiency anemia. Scientific Reports, 14(1), 22027. https://doi.org/10.1038/s41598-024-72577-8
Amorim A. M. B., Piochi L. F., Gaspar A. T., Preto A. J., Rosário-Ferreira N., and Moreira I. S. (2024). Advancing drug safety in drug development: Bridging computational predictions for enhanced toxicity prediction. Chemical Research in Toxicology, 37(6), 827–849. https://doi.org/10.1021/acs.chemrestox.3c00352
Arango J. P. B., Rodriguez D. Y. M., Cruz S. L., and Ocampo G. T. (2026). In silico evaluation of pharmacokinetic properties and molecular docking for the identification of potential anticancer compounds. Computational Biology and Chemistry, 120, 108626. https://doi.org/10.1016/j.compbiolchem.2025.108626
Aulifa D. L., Al Shofwan A. A., Megantara S., Fakih T. M., and Budiman, A. (2024). Elucidation of molecular interactions between drug–polymer in amorphous solid dispersion by a computational approach using molecular dynamics simulations. Advances and Applica- tions in Bioinformatics and Chemistry, 17, 1–19. https://doi.org/10.2147/AABC.S441628
Berezhkovskiy L. M. (2004). Determination of volume of distribution at steady state with complete consideration of the kinetics of protein and tissue binding in linear pharmacokinetics. Journal of Pharmaceutical Sciences, 93(2), 364–374. https://doi.org/10.1002/jps.10539
Bharate S. B., and Lindsley C. W. (2024). Natural products driven medicinal chemistry. Journal of Medicinal Chemistry, 67(23), 20723–20730. https://doi.org/10.1021/acs.jmedchem.4c02736
Bray F., Laversanne M., Sung H., Ferlay J., Siegel R. L., Soerjomataram I., et al. (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
Cao Q., Wu, X., Zhang Q., Gong J., Chen Y., You Y., et al. (2023). Mechanisms of action of the BCL-2 inhibitor venetoclax in multiple myeloma: a literature review. Frontiers in Pharmacology, 14, 1291920. https://doi.org/10.3389/fphar.2023.1291920
Casara P., Davidson J., Claperon A., Toumelin-Braizat G. L., Vogler M., Bruno A., et al. (2018). S55746 is a novel orally active BCL-2 selective and potent inhibitor that impairs hematological tumor growth. Oncotarget, 9(28), 20075. https://doi.org/10.18632/oncotarget.24744
Du X., Li Y., Xia Y. L., Ai S. M., Liang J., Sang P., et al. (2016). Insights into protein–ligand interactions: Mechanisms, models, and methods. In International Journal of Molecular Sciences, 17(2), 144. https://doi.org/10.3390/ijms17020144
Guex N., and Peitsch M. C. (1997). SWISS-MODEL and the Swiss-Pdb Viewer: An environment for comparative protein modeling. Electrophoresis, 18(15), 2714–2723. https://doi.org/10.1002/elps.1150181505
Gurfinkel D. M., and Rao A. V. (2003). Soyasaponins: The relationship between chemical structure and colon anticarcinogenic activity. Nutrition and Cancer, 47(1), 24–33. https://doi.org/10.1207/s15327914nc4701_3
Hamidi M., Azadi A., Rafiei P., and Ashrafi H. (2013). A pharmacokinetic overview of nanotechnology-based drug delivery systems: an ADME-oriented approach. Critical ReviewsTM in Therapeutic Drug Carrier Systems, 30(5), 435-467. https://doi.org/10.1615/CritRevTherDrugCarrierSyst.2013007419
Hanwell M. D., Curtis D. E., Lonie D. C., Vandermeersch T., Zurek E., and 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
Hoang C. V., Tu T. Q., Nguyen H. D., and Chu M. H. (2025). In silico studies of saponins from Hoya verticillata var. verticillate with Important apoptosis potency. Letters in Organic Chemistry, 22(11), 1–9). https://doi.org/10.2174/0115701786363779250528013022
Hoang C. V., Tu T. Q., Nguyen L. T. N., Nguyen H. D., Nguyen Q. H., and Chu M. H. (2023). Two New C21 steroidal glycosides from the leaves of Hoya parasitica. Records of Natural Products, 17(6), 1046–1051. https://doi.org/10.25135/rnp.419.2307.2831
Lisanti E., and Arwin A. (2019). Phytochemical screening and proximate analysis of soybeans (Glycine max) variety Gamasugen 1 and Gamasugen 2 derived from gamma rays irradiation. Journal of Physics: Conference Series, 1402(5), 55023. https://doi.org/10.1088/1742-6596/1402/5/055023
Lopes-Coelho F., Martins F., Pereira S. A., and Serpa J. (2021). Anti-angiogenic therapy: Current challenges and future perspectives. In International Journal of Molecular Sciences, 22(7), 3765. https://doi.org/10.3390/ijms22073765
Maiorov V. N., and Crippen G. M. (1994). Significance of root-mean-square deviation in comparing three-dimensional structures of globular proteins. Journal of Molecular Biology, 235(2), 625–634. https://doi.org/10.1006/jmbi.1994.1017
Meng X. Y., Zhang H. X., Mezei M., and Cui M. (2011). Molecular docking: A powerful approach for structure-based drug discovery. Current Computer-Aided Drug Design, 7(2), 146-157. https://dx.doi.org/10.2174/157340911795677602
Morris G. M., Huey R., Lindstrom W., Sanner M. F., Belew R. K., Goodsell D. S., et al. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/jcc.21256
Mortier J., Rakers C., Bermudez M., Murgueitio M. S., Riniker S., and Wolber G. (2015). The impact of molecular dynamics on drug design: applications for the characterization of ligand–macromolecule complexes. Drug Discovery Today, 20(6), 686–702. https://doi.org/10.1016/j.drudis.2015.01.003
Palabiyik Alperen A. (2025). The role of Bcl‑2 in controlling the transition between autophagy and apoptosis (Review). Molecular Medicine Reports. 32(1), 172. https://doi.org/10.3892/mmr.2025.13537
Patil R., Das S., Stanley A., Yadav L., Sudhakar A., and Varma A. K. (2010). Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug-designing. Plos One, 5(8), e12029. https://doi.org/10.1371/journal.pone.0012029
Pettersen E. F., Goddard T. D., Huang C. C., Couch G. S., Greenblatt D. M., Meng E. C., et al. (2004). UCSF Chimera—A visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612. https://doi.org/10.1002/jcc.20084
Pires D. E. V Blundell T. L., and Ascher D. B. (2015). pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry, 58(9), 4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104
Ramesh P., and Medema, J. P. (2020). BCL-2 family deregulation in colorectal cancer: potential for BH3 mimetics in therapy. Apoptosis: an international journal on programmed cell death, 25(5-6), 305–320. https://doi.org/10.1007/s10495-020-01601-9
Schreiner W., Karch R., Knapp B., and Ilieva N. (2012). Relaxation estimation of RMSD in molecular dynamics immunosimulations. Computational and Mathematical Methods in Medicine, 2012(1), 173521. https://doi.org/10.1155/2012/173521
Silverman I., Gerber M., Shaykevich A., Stein Y., Siegman A., Goel S., et al. (2024). Structural modifications and kinetic effects of KRAS interactions with HRAS and NRAS: an in silico comparative analysis of KRAS mutants. Frontiers in Molecular Biosciences, 11, 1436976. https://doi.org/10.3389/fmolb.2024.1436976
Song X., Bao L., Feng C., Huang Q., Zhang F., Gao X., et al. (2024). Accurate prediction of protein structural flexibility by deep learning integrating intricate atomic structures and cryo-em density information. Nature Communications, 15(1), 5538. https://doi.org/10.1038/s41467-024-49858-x
Van Der Spoel D., Lindahl E., Hess B., Groenhof G., Mark A. E., and Berendsen H. J. C. (2005). GROMACS: Fast, flexible, and free. Journal of Computational Chemistry, 26(16), 1701–1718. https://doi.org/10.1002/jcc.20291
Xu J., Dong X., Huang D. C., Xu P., Zhao Q., and Chen B. (2023). Current advances and future strategies for bcl-2 inhibitors: potent weapons against cancers. Cancers, 15(20), 4957. https://doi.org/10.3390/cancers15204957
Zafar A., Khatoon S., Khan M. J., Abu J., and Naeem A. (2025). Advancements and limitations in traditional anti-cancer therapies: A comprehensive review of surgery, chemotherapy, radiation therapy, and hormonal therapy. Discover Oncology, 16(1), 607. https://doi.org/10.1007/s12672-025-02198-8
Zeng T., Li J., and Wu R. (2024). Natural product databases for drug discovery: Features and applications. Pharmaceutical Science Advances, 2, 100050. https://doi.org/10.1016/j.pscia.2024.100050
Zoete V., Cuendet M. A., Grosdidier A., and Michielin O. (2011). SwissParam: A fast force field generation tool for small organic molecules. Journal of Computational Chemistry, 32(11), 2359–2368. https://doi.org/10.1002/jcc.21816
Zumu F. S., Akbor M. S., Amin A. Al Haque M. F., Sultana I., Faruq A. Al et al. (2024). Phytochemical screening and evaluation of antibacterial, antipyretic, hypoglycemic, and anxiolytic effects of Adiantum philippense leaf extracts. Pharmacological Research - Natural Products, 5, 100108. https://doi.org/10.1016/j.prenap.2024.100108
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National Foundation for Science and Technology Development
Grant numbers 106.02-2023.05
