Marine Bastadins as Potent ACAT1 Inhibitors: Integrated Molecular Docking and ADMET Profiling for Anticancer Drug Discovery
DOI:
https://doi.org/10.22399/ijcesen.4378Keywords:
ACAT1, Bastadins, Cancer, Molecular Docking, ADMETAbstract
Acyl-CoA: cholesterol acyltransferase 1 (ACAT1) is a key enzyme in lipid homeostasis, catalyzing the esterification of cholesterol, a process closely associated with the metabolic reprogramming that supports tumor progression. In this study, twenty-two Bastadins, bromotyrosine-derived macrocyclic metabolites isolated from the marine sponge Ianthella basta, were evaluated as potential human ACAT1 inhibitors using molecular docking, ADME analysis, and toxicity predictions. These compounds were selected based on prior reports of their experimental ACAT1 inhibitory activity, underscoring their potential as lead scaffolds for enzyme modulation. Molecular docking was performed using PyRx (v0.8), and ADMET properties were evaluated with ADMETlab 3.0 and ProTox 3.0. Among the compounds, Bastadins 8, 10, 13, and 19 demonstrated the strongest affinities toward ACAT1 (-11.0 to -11.5 kcal/mol). Bastadin 13 exhibited the most stable complex formation (-11.5 kcal/mol), involving strong hydrogen bonds as well as π-π T-shaped and π-alkyl interactions with key residues, including His460 and Phe384. Similarly, Bastadin 19 displayed a high interaction energy (-11.4 kcal/mol), engaging in stable polar and hydrophobic contacts with residues such as His460, Trp420, Asn421, Phe254, and Tyr417. ADMET predictions indicated that both Bastadins 13 and 19 possess favorable pharmacokinetic properties, including enhanced intestinal absorption, metabolic stability, and low predicted toxicity. Overall, these compounds emerge as the most promising ACAT1 inhibitors, combining strong binding, robust interactions with critical residues, and favorable ADMET characteristics, offering a rational framework for the development of novel anticancer agents, and providing a solid basis for further experimental validation and optimization in targeted cancer therapy
References
[1] Tufail, M., Wang, H., Wang, Y. (2024). Hallmarks of cancer resistance. Cell Death & Disease, 15, 266. https://pmc.ncbi.nlm.nih.gov/articles/PMC11145355/ DOI: https://doi.org/10.1016/j.isci.2024.109979
[2] Schiliro, C., Firestein, B. L. (2021). Mechanisms of metabolic reprogramming in cancer cells supporting enhanced growth and proliferation. Cells, 10(5), 1056. https://doi.org/10.3390/cells10051056 DOI: https://doi.org/10.3390/cells10051056
[3] Cheng, C., Geng, F., Cheng, X., Guo, D. (2018). Lipid metabolism reprogramming and its potential targets in cancer. Cancer Communications, 38(1), 27. https://doi.org/10.1186/s40880-018-0301-4 DOI: https://doi.org/10.1186/s40880-018-0301-4
[4] Terry, A. R., Hay, N. (2024). Emerging targets in lipid metabolism for cancer therapy. Trends in Pharmacological Sciences, 45(6), 537-551. https://doi.org/10.1016/j.tips.2024.04.007 DOI: https://doi.org/10.1016/j.tips.2024.04.007
[5] Mordzińska-Rak, A., Verdeil, G., Hamon, Y., Błaszczak, E., Tromb, T. (2025). Dysregulation of cholesterol homeostasis in cancer pathogenesis. Cellular and Molecular Life Sciences, 82(1), 168. https://doi.org/10.1007/s00018-025-05617-9 DOI: https://doi.org/10.1007/s00018-025-05617-9
[6] Tu, T., Zhang, H., Xu, H. (2023). Targeting sterol‑O‑acyltransferase 1 to disrupt cholesterol metabolism for cancer therapy. Frontiers in Oncology, 13, Article 1197502. https://doi.org/10.3389/fonc.2023.1197502 DOI: https://doi.org/10.3389/fonc.2023.1197502
[7] Bairos, J. A., Chinedu, U. N., Zafar, M., Akl, M. G., Li, L., Parlakgul, G., Arruda, A. P., Widenmaier, S. B. (2024). Sterol O-acyltransferase (SOAT/ACAT) activity is required to form cholesterol crystals in hepatocyte lipid droplets. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids, 1869(4), 159512. https://doi.org/10.1016/j.bbalip.2024.159512 DOI: https://doi.org/10.1016/j.bbalip.2024.159512
[8] Ayyagari, V. N., Bandyopadhyay, A., Harris, R. E. (2020). Assessment of acyl‑CoA:cholesterol acyltransferase‑1 (ACAT‑1) mediated cholesterol ester accumulation in ovarian cancer progression. PLOS ONE, 15(8), e0228024. https://doi.org/10.1371/journal.pone.0228024 DOI: https://doi.org/10.1371/journal.pone.0228024
[9] Zixi, S., Liu, Y., Yang, Y. (2024). Targeting ACAT1 in cancer: From threat to treatment. Frontiers in Oncology, 14, 1395192. https://doi.org/10.3389/fonc.2024.1395192
[10] Li, J., Gu, D., Lee, S. Y., Wang, X., Yang, Y., Liu, Q. (2016). Abrogating cholesterol esterification suppresses growth and metastasis of pancreatic cancer. Oncogene, 35 (25), 3343‑3353. https://doi.org/10.1038/onc.2016.168 DOI: https://doi.org/10.1038/onc.2016.168
[11] He, W., Li, Y., Liu, S.-B., Chang, Y., Han, S., Han, X., Ma, Z., Amin, H. M., Song, Y.-H., Zhou, J. (2024). From mitochondria to tumor suppression: ACAT1's crucial role in gastric cancer. Frontiers in Immunology, 15, Article 1449525. https://doi.org/10.3389/fimmu.2024.1449525 DOI: https://doi.org/10.3389/fimmu.2024.1449525
[12] Sun, T., Xiao, X. (2024). Targeting ACAT1 in cancer: From threat to treatment. Frontiers in Oncology, 14, 1395192. https://doi.org/10.3389/fonc.2024.1395192 DOI: https://doi.org/10.3389/fonc.2024.1395192
[13] Zhang, J. (2024). Target fishing and mechanistic insights of the natural anticancer drug candidate chlorogenic acid. Acta Pharmaceutica Sinica B, 14(10), 4431‑4442. https://doi.org/10.1016/j.apsb.2024.07.005 DOI: https://doi.org/10.1016/j.apsb.2024.07.005
[14] Yang, Q.‑B., Liang, L.‑F. (2024). Spongia sponges: Unabated sources of novel secondary metabolites. Marine Drugs, 22(5), 213. https://doi.org/10.3390/md22050213 DOI: https://doi.org/10.3390/md22050213
[15] Raymond, M. J. F., Rakotondraibe, H. L. (2025). Recent updates on terpenoids and other bioactive constituents of marine sponges. Molecules, 30(5), 1112. https://doi.org/10.3390/molecules30051112 DOI: https://doi.org/10.3390/molecules30051112
[16] Kazlauskas, R., Lidgard, R. O., Murphy, P. T., Wells, R. J., Blount, J. F. (1981). Brominated tyrosine‑derived metabolites from the sponge Ianthella basta. Australian Journal of Chemistry, 34(4), 765‑786. https://doi.org/10.1071/CH9810765 DOI: https://doi.org/10.1071/CH9810765
[17] Eguchi, K., Kato, H., Fujiwara, Y., Losung, F., Mangindaan, R. E. P., de Voogd, N. J., Takeya, M., Tsukamoto, S. (2015). Bastadins, brominated-tyrosine derivatives, suppress accumulation of cholesterol ester in macrophages. Bioorganic & Medicinal Chemistry Letters, 25(22), 5389-5392. https://doi.org/10.1016/j.bmcl.2015.09.024 DOI: https://doi.org/10.1016/j.bmcl.2015.09.024
[18] Mathieu, V., Wauthoz, N., Lefranc, F., Niemann, H., Amighi, K., Kiss, R., Proksch, P. (2013). Cyclic versus hemi‑bastadins: Pleiotropic anti‑cancer effects from apoptosis to anti‑angiogenic and anti‑migratory effects. Molecules, 18(3), 3543‑3561. https://doi.org/10.3390/molecules18033543 DOI: https://doi.org/10.3390/molecules18033543
[19] Miao, S. C., Andersen, R. J., Allen, T. M. (1990). Cytotoxic metabolites from the sponge Ianthella basta collected in Papua New Guinea. Journal of Natural Products, 53(6), 1441‑1446. https://doi.org/10.1021/np50072a007 DOI: https://doi.org/10.1021/np50072a007
[20] Aoki, S., Cho, S.-H., Ono, M., Kuwano, T., Nakao, S., Kuwano, M., Nakagawa, S., Gao, J.-Q., Mayumi, T., Shibuya, M., Kobayashi, M. (2006). Bastadin 6, a spongean brominated tyrosine derivative, inhibits tumor angiogenesis by inducing selective apoptosis to endothelial cells. Anti‑Cancer Drugs, 17(3), 269‑278. https://doi.org/10.1097/00001813-200603000-00005 DOI: https://doi.org/10.1097/00001813-200603000-00005
[21] Greve, H., Kehraus, S., Krick, A., Kelter, G., Maier, A., Fiebig, H. H., Wright, A. D., König, G. M. (2008). Cytotoxic Bastadin 24 from the Australian Sponge Ianthella quadrangulata. Journal of Natural Products, 71(3), 309‑312. https://doi.org/10.1021/np070373e DOI: https://doi.org/10.1021/np070373e
[22] Xiao, M., Xu, J., Wang, W., Zhang, B., Liu, J., Li, J., Xu, H., Zhao, Y., Yu, X., Shi, S. (2023). Functional significance of cholesterol metabolism in cancer: From threat to treatment. Experimental & Molecular Medicine, 55(9), 1982‑1995. https://doi.org/10.1038/s12276-023-01079-w DOI: https://doi.org/10.1038/s12276-023-01079-w
[23] Taib, N., Tchouar, N. (2025). Computational screening and QSAR study of bastadins as ACAT1 inhibitors. International Journal of Computational and Experimental Science and Engineering, 11(4), 7270-7288. https://doi.org/10.22399/ijcesen.3994 DOI: https://doi.org/10.22399/ijcesen.3994
[24] Wu, F., Zhou, Y., Li, L., Shen, X., Chen, G., Wang, X., Liang, X., Tan, M., Huang, Z. (2020). Computational approaches in preclinical studies on drug discovery and development. Frontiers in Chemistry, 8, 726. https://doi.org/10.3389/fchem.2020.00726 DOI: https://doi.org/10.3389/fchem.2020.00726
[25] Laeeq, S., Dubey, V. (2022). Computational prediction of ADMET properties of ACAT inhibitors for synthesis and pharmacological screening. International Journal of Health Sciences, 6 (S7), 2377‑2388. https://doi.org/10.53730/ijhs.v6nS7.11866 DOI: https://doi.org/10.53730/ijhs.v6nS7.11866
[26] Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., Zaslavsky, L., Zhang, J., Bolton, E. E. (2021). PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Research, 49(D1), D1388-D1395. https://doi.org/10.1093/nar/gkaa971 DOI: https://doi.org/10.1093/nar/gkaa971
[27] Sterling, T., Irwin, J. J. (2015). ZINC 15 – ligand discovery for everyone. Journal of Chemical Information and Modeling, 55(11), 2324-2337. https://doi.org/10.1021/acs.jcim.5b00559 DOI: https://doi.org/10.1021/acs.jcim.5b00559
[28] Dallakyan, S., Olson, A. J. (2015). Small-Molecule Library Screening by Docking with PyRx. In J. E. Hempel (Ed.), Chemical Biology: Methods and Protocols (Methods in Molecular Biology, Vol. 1263, pp. 243-250). Springer. https://doi.org/10.1007/978-1-4939-2269-7_19 DOI: https://doi.org/10.1007/978-1-4939-2269-7_19
[29] O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., Hutchison, G. R. (2011). Open Babel: An open chemical toolbox. Journal of Cheminformatics, 3, 33. https://doi.org/10.1186/1758-2946-3-33 DOI: https://doi.org/10.1186/1758-2946-3-33
[30] Berman, H. M., Battistuz, T., Bhat, T. N., Bluhm, W. F., Bourne, P. E., Burkhardt, K., Zardecki, C. (2002). The Protein Data Bank. Acta Crystallographica Section D: Biological Crystallography, 58(6), 899-907. https://doi.org/10.1107/S0907444902003451 DOI: https://doi.org/10.1107/S0907444902003451
[31] BIOVIA. (2024). BIOVIA Discovery Studio (Version 2024). Dassault Systèmes. San Diego, CA, USA.
[32] Trott, O., Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455-461. https://doi.org/10.1002/jcc.21334 DOI: https://doi.org/10.1002/jcc.21334
[33] Yusuf, D., Davis, A. M., Kleywegt, G. J., Schmitt, S. (2008). An alternative method for the evaluation of docking performance: RSR vs RMSD. Journal of Chemical Information and Modeling, 48(7), 1411-1422. https://doi.org/10.1021/ci800084x DOI: https://doi.org/10.1021/ci800084x
[34] Schrödinger, LLC. (2023). The PyMOL Molecular Graphics System, Version 3.1
[35] Dassault Systèmes BIOVIA. (2024). BIOVIA Discovery Studio Visualizer, Version 24
[36] Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., Olson, A. J. (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 DOI: https://doi.org/10.1002/jcc.21256
[37] Xiong, G., Wu, Z., Yi, J., Fu, L., Yang, Z., Hsieh, C., Yin, M., Zeng, X., Wu, C., Lu, A., Chen, X., Hou, T. (2024). ADMETlab 3.0: Integrating AI and PBPK models with extended usability and reliability for ADMET predictions. Nucleic Acids Research, 52(W1), W422-W431. https://doi.org/10.1093/nar/gkae236 DOI: https://doi.org/10.1093/nar/gkae236
[38] Yang, H., Lou, C., Sun, L., Li, J., Cai, Y., Wang, Z., Li, W., Liu, G., Tang, Y. (2019). admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics, 35(6), 1067-1069. https://doi.org/10.1093/bioinformatics/bty707 DOI: https://doi.org/10.1093/bioinformatics/bty707
[39] Banerjee, P., Koutsoukas, A., Ecker, G. F. (2024). ProTox-3.0: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 52(W1), W513-W519. https://doi.org/10.1093/nar/gkae303 DOI: https://doi.org/10.1093/nar/gkae303
[40] Li, Q., Cheng, T., Wang, Y., Bryant, S. H. (2010). PubChem as a public resource for drug discovery. Drug Discovery Today, 15(23-24), 1052-1057. https://doi.org/10.1016/j.drudis.2010.10.003 DOI: https://doi.org/10.1016/j.drudis.2010.10.003
[41] Long, T., Sun, Y., Hassan, A., Qi, X., Li, X. (2020). Structure of nevanimibe-bound tetrameric human ACAT1. Nature, 581(7808), 339-343. https://doi.org/10.1038/s41586-020-2295-8 DOI: https://doi.org/10.1038/s41586-020-2295-8
[42] Lu, Y., Wang, Y., Zhu, W. (2010). Non-bonding interactions of organic halogens in biological systems: Implications for drug discovery and biomolecular design. Physical Chemistry Chemical Physics, 12(18), 4543-4551. https://doi.org/10.1039/B926326H DOI: https://doi.org/10.1039/b926326h
[43] Pace, C. N., Fu, H., Fryar, K. L., Landua, J., Trevino, S. R., Schell, D., Thurlkill, R. L., Imura, S., Scholtz, J. M., Gajiwala, K., Sevcik, J., Urbanikova, L., Myers, J. K., Takano, K., Hebert, E. J., Shirley, B. A., Grimsley, G. R. (2014). Contribution of hydrogen bonds to protein stability. Protein Science, 23(5), 652-661. https://doi.org/10.1002/pro.2449 DOI: https://doi.org/10.1002/pro.2449
[44] Ippolito, J. A., Christianson, D. W. (1992). The contribution of halogen atoms to protein–ligand interactions. International Journal of Biological Macromolecules,14(4),193-197. https://doi.org/10.1016/S0141-8130(05)80026-1 DOI: https://doi.org/10.1016/S0141-8130(05)80026-1
[45] Perrin, C. L. (2010). Are short, low-barrier hydrogen bonds unusually strong? Accounts of Chemical Research, 43(12), 1550-1557. https://doi.org/10.1021/ar100097j DOI: https://doi.org/10.1021/ar100097j
[46] Cheng, F., Li, W., Liu, G., Tang, Y. (2013). In silico ADMET prediction: Recent advances, current challenges and future trends. Current Topics in Medicinal Chemistry, 13(10), 1273-1289. https://doi.org/10.2174/15680266113139990033 DOI: https://doi.org/10.2174/15680266113139990033
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
