Integrated in Silico Analyses Reveal Diterpenoids from Erythrophleum fordii as Potential Inhibitors of Anti-Apoptotic Targets in Ovarian Cancer
DOI:
https://doi.org/10.53560/PPASA(63-2)713Keywords:
Apoptosis, DFT, Diterpenoid, Erythrophleum fordii, Mcl-1, Molecular ModelingAbstract
Ovarian cancer remains a major clinical challenge, particularly in advanced-stage disease, where relapse and resistance to platinum-taxane chemotherapy continue to limit therapeutic efficacy. Because anti-apoptotic Bcl-2 family proteins contribute to chemoresistance, myeloid cell leukemia 1 (Mcl-1) represents a relevant molecular target. This study evaluated five cassane-type diterpenoids from Erythrophleum fordii (CPD1-CPD5), previously reported to inhibit A2780 ovarian cancer cell growth, against Mcl-1 (PDB: 6UD2) using molecular docking, molecular dynamics simulation, Molecular Mechanics Generalised Born Surface Area (MM/GBSA), and Density Functional Theory (DFT) analysis, with paclitaxel as a reference. Docking placed all compounds within the BH3-binding groove and identified CPD2 (erythrophlesin F) as the top-ranked diterpenoid, with a docking score of -9.63 kcal/mol and favorable interactions with Asn223, Arg263, and Thr266. Molecular dynamics analysis indicated stable pocket occupancy for CPD2 and paclitaxel, while hydrogen-bond monitoring suggested greater persistence of polar contacts for CPD2. MM/GBSA supported favorable binding for both ligands, although paclitaxel displayed a more favorable mean binding free energy under the applied protocol. DFT descriptors further differentiated both compounds, with CPD2 showing a larger HOMO-LUMO gap and lower electrophilicity. These findings support CPD2 as a promising Mcl-1-binding candidate that warrants experimental validation.
References
1. M. Arcieri, V. Tius, S. Filippin, G. Aletti, D. Lorusso, A. Fagotti, J. Sehouli, I. Zapardiel, P. De Iaco, P. Scollo, and et al. Management of patients with epithelial ovarian cancer: A systematic comparison of international guidelines from scientific societies (AIOM-BGCS-ESGO-ESMO-JGSO-NCCN-NICE). Cancers 17(24): 3915 (2025). DOI: 10.3390/cancers17243915
2. M. Nunes, C. Bartosch, M.H. Abreu, A. Richardson, R. Almeida, and S. Ricardo. Deciphering the molecular mechanisms behind drug resistance in ovarian cancer to unlock efficient treatment options. Cells 13(9): 786 (2024). DOI: 10.3390/cells13090786
3. R. Goel, P. Upadhyay, R. Garg, D. Gulwani, and T.D. Singh. Overcoming paclitaxel resistance in ovarian cancer cells using DN200434, an inverse agonist of estrogen-related receptor gamma (ERRγ). Biochemical and Biophysical Research Communications 778: 152351 (2025). DOI: 10.1016/j.bbrc.2025.152351
4. J. Kale, E.J. Osterlund, and D.W. Andrews. BCL-2 family proteins: Changing partners in the dance towards death. Cell Death & Differentiation 25(1): 65-80 (2018). DOI: 10.1038/cdd.2017.186
5. I. Jaya, F. Safriadi, I. Wijaya, P. Pitaloka, E. Afifah, F.S. Medina, and M.H. Bashari. Role of Bcl-2 family anti-apoptosis inhibition in overcoming therapeutic resistance in prostate cancer: A systematic review. Critical Reviews in Oncology/Hematology 215: 104895 (2025). DOI: 10.1016/j.critrevonc.2025.104895
6. M. Mohiuddin. Targeting MCL-1 to overcome therapeutic resistance and improve cancer mortality. Health Science Reports 8(10): e71390 (2025). DOI: 10.1002/hsr2.71390
7. W. Xiang, C.Y. Yang, and L. Bai. MCL-1 inhibition in cancer treatment. OncoTargets and therapy 11: 7301-7314 (2018). DOI: 10.2147/OTT.S146228
8. S.T. Asma, U. Acaroz, K. Imre, A. Morar, S.R. Shah, S.Z. Hussain, D. Arslan-Acaroz, H. Demirbas, Z. Hajrulai-Musliu, F.R. Istanbullugil, A. Soleimanzadeh, D. Morozov, K. Zhu, V. Herman, A. Ayad, C. Athanassiou, and S. Ince. Natural products/bioactive compounds as a source of anticancer drugs. Cancers 14(24): 6203 (2022). DOI: 10.3390/cancers14246203
9. A. Naeem, P. Hu, M. Yang, J. Zhang, Y. Liu, W. Zhu, and Q. Zheng. Natural Products as Anticancer Agents: Current Status and Future Perspectives. Molecules 27(23): 8367 (2022). DOI: 10.3390/molecules27238367
10. Z. Deng, I. Bakunina, H. Yu, J. Han, A. Dömling, M.J.U. Ferreira, and J. Zhang. Research progress on natural diterpenoids in reversing multidrug resistance. Frontiers in Pharmacology 13: 815603 (2022). DOI: 10.3389/fphar.2022.815603
11. A. Sharmila, P. Bhadra, C. Kishore, C.I. Selvaraj, J. Kavalakatt, and A. Bishayee. Nanoformulated Terpenoids in cancer: A review of therapeutic applications, mechanisms, and challenges. Cancers 17(18): 3013 (2025). DOI: 10.3390/cancers17183013
12. D. Du, J. Qu, J.M. Wang, S.S. Yu, X.G. Chen, S. Xu, S.G. Ma, Y. Li, G.Z. Ding, and L. Fang. Cytotoxic cassaine diterpenoid-diterpenoid amide dimers and diterpenoid amides from the leaves of Erythrophleum fordii. Phytochemistry 71(14): 1749-1755 (2010). DOI: 10.1016/j.phytochem.2010.07.004
13. A. Vidal-Limon, J.E. Aguilar-Toalá, and A.M. Liceaga, Integration of molecular docking analysis and molecular dynamics simulations for studying food proteins and bioactive peptides. Journal of Agricultural and Food Chemistry 70(4): 934-943 (2022). DOI: 10.1021/acs.jafc.1c06110
14. S. Liu. The evolving quest for chemical understanding in the quantum age. Journal of Chemical Theory and Computation 21(20): 10068-10079 (2025). DOI: 10.1021/acs.jctc.5c01299
15. G. Rescourio, A.Z. Gonzalez, S. Jabri, B. Belmontes, G. Moody, D. Whittington, X. Huang, S. Caenepeel, M. Cardozo, A.C. Cheng, D. Chow, and et al. Discovery and in vivo evaluation of macrocyclic Mcl-1 inhibitors featuring an α-hydroxy phenylacetic acid pharmacophore or bioisostere. Journal of Medicinal Chemistry 62(22): 10258-10271 (2019). DOI: 10.1021/acs.jmedchem.9b01310
16. D.V.D. Spoel, E. Lindahl, B. Hess, G. Groenhof, A.E. Mark, and H.J.C. Berendsen. GROMACS: Fast, flexible, and free. Journal of Computational Chemistry 26(16): 1701-1718 (2005). DOI: 10.1002/jcc.20291
17. N. Guex and M.C. Peitsch. SWISS-MODEL and the Swiss-Pdb Viewer: An environment for comparative protein modeling. ELECTROPHORESIS 18(15): 2714-2723 (1997). DOI: 10.1002/elps.1150181505
18. V. Zoete, M.A. Cuendet, A. Grosdidier, and O. Michielin. SwissParam: A fast force field generation tool for small organic molecules. Journal of Computational Chemistry 32(11): 2359-2368 (2011). DOI: 10.1002/jcc.21816
19. M.S. Valdés-Tresanco, M.E. Valdés-Tresanco, P.A. Valiente, and E. Moreno. gmx_MMPBSA: A new tool to perform end-state free energy calculations with GROMACS. Journal of Chemical Theory and Computation 17(10): 6281-6291 (2021). DOI: 10.1021/acs.jctc.1c00645
20. H.D. Nguyen. Unveiling the anti-apoptotic mechanism of magnolialide as a colorectal cancer inhibitor via molecular modeling, ADMET, and MMGBSA analysis. Physical Chemistry Research 13(4): 783-796 (2025). DOI: 10.22036/pcr.2025.535946.2708
21. F. Neese. Software update: The ORCA program system-version 6.0. WIREs Computational Molecular Science 15(2): e70019 (2025). DOI: 10.1002/wcms.70019
22. G. Knizia and J.E. Klein. Electron flow in reaction mechanisms-revealed from first principles. Angewandte Chemie International Edition 54(18): 5518-5522 (2015). DOI: 10.1002/anie.201410637
23. G. Knizia. Intrinsic atomic orbitals: An unbiased bridge between quantum theory and chemical concepts. Journal of Chemical Theory and Computation 9(11): 4834-4843 (2013). DOI: 10.1021/ct400687b
24. M.D. Hanwell, D.E. Curtis, D.C. Lonie, T. Vandermeersch, E. Zurek, and G.R. Hutchison. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. Journal of Cheminformatics 4(1): 17 (2012). DOI: 10.1186/1758-2946-4-17
25. J. Luo, Z.Q. Xue, W.M. Liu, J.L. Wu, and Z.Q. Yang. Koopmans’ Theorem for Large Molecular Systems within Density Functional Theory. The Journal of Physical Chemistry A 110(43): 12005-12009 (2006). DOI: 10.1021/jp063669m
26. R. Das, J.L. Vigneresse, and P.K. Chattaraj. Chemical reactivity through structure-stability landscape. International Journal of Quantum Chemistry 114(21): 1421-1429 (2014). DOI: 10.1002/qua.24706
27. Z. Ma, A. Ajibade, and X. Zou. Docking strategies for predicting protein-ligand interactions and their application to structure-based drug design. Communications in Information and Systems 24(3): 199 (2024). DOI: 10.4310/cis.241021221101
28. S.I. Tantawy, N. Timofeeva, A. Sarkar, and V. Gandhi. Targeting MCL-1 protein to treat cancer: opportunities and challenges. Frontiers in Oncology 13: 1226289 (2023). DOI: 10.3389/fonc.2023.1226289
29. D.A. Schaller, C.D. Christ, J.D. Chodera, and A. Volkamer. Benchmarking Cross-Docking Strategies in Kinase Drug Discovery. Journal of Chemical Information and Modeling 64(23): 8848-8858 (2024). DOI: 10.1021/acs.jcim.4c00905
30. A.O.H. Zayed. Optimizing protein-ligand docking through machine learning: algorithm selection with AutoDock Vina. Discover Chemistry 2(1): 164 (2025). DOI: 10.1007/s44371-025-00246-4
31. Q. Sun. The hydrophobic effects: Our current understanding. Molecules 27(20): 7009 (2022). DOI: 10.3390/molecules27207009
32. T.V. Szalai, D. Bajusz, R. Börzsei, B.Z. Zsidó, J. Ilaš, G.G. Ferenczy, C. Hetényi, and G.M. Keserű. Effect of water networks on ligand binding: Computational predictions vs experiments. Journal of Chemical Information and Modeling 64(23): 8980-8998 (2024). DOI: 10.1021/acs.jcim.4c01291
33. A. McGriff and W.J. Placzek. Phylogenetic analysis of the MCL1 BH3 binding groove and rBH3 sequence motifs in the p53 and INK4 protein families. Plos One 18(1): e0277726 (2023). DOI: 10.1371/journal.pone.0277726
34. J. Sharifi-Rad, C. Quispe, J.K. Patra, Y.D. Singh, M.K. Panda, G. Das, C.O. Adetunji, O.S. Michael, O. Sytar, L. Polito, J. Živković, N. Cruz-Martins, M. Klimek-Szczykutowicz, H. Ekiert, M.I. Choudhary, S.A. Ayatollahi, B. Tynybekov, F. Kobarfard, A.C. Muntean, I. Grozea, S.D. Dastan, M. Butnariu, A. Szopa, and D. Calina. Paclitaxel: Application in modern oncology and nanomedicine-based cancer therapy. Oxidative Medicine and Cellular Longevity 2021(1): 3687700 (2021). DOI: 10.1155/2021/3687700
35. G.G. Ferenczy and M. Kellermayer. Contribution of hydrophobic interactions to protein mechanical stability. Computational and Structural Biotechnology Journal 20: 1946-1956 (2022). DOi: 10.1016/j.csbj.2022.04.025
36. V.A. Adhav and K. Saikrishnan. The realm of unconventional noncovalent interactions in proteins: Their significance in structure and function. ACS Omega 8(25): 22268-22284 (2023). DOI: 10.1021/acsomega.3c00205
37. O.M.H. Salo-Ahen, I. Alanko, R. Bhadane, A.M.J.J. Bonvin, R.V. Honorato, S. Hossain, A.H. Juffer, A. Kabedev, M. Lahtela-Kakkonen, A.S. Larsen, E. Lescrinier, P. Marimuthu, M.U. Mirza, G. Mustafa, A. Nunes-Alves, T. Pantsar, A. Saadabadi, K. Singaravelu, and M. Vanmeert. Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes 9(1): 71 (2021). DOI: 10.3390/pr9010071
38. R.F. de Freitas and M. Schapira. A systematic analysis of atomic protein-ligand interactions in the PDB. MedChemComm 8(10): 1970-1981 (2017). DOI: 10.1039/C7MD00381A
39. E. Wang, H. Sun, J. Wang, Z. Wang, H. Liu, J.Z.H. Zhang, and T. Hou. End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design. Chemical Reviews 119(16): 9478-9508 (2019). DOI: 10.1021/acs.chemrev.9b00055
40. L. Dong, P. Li, and B. Wang. Enhancing MM/P(G)BSA methods: Integration of formulaic entropy for improved binding free energy calculations. Journal of Computational Chemistry 46(10): e70093 (2025). DOI: 10.1002/jcc.70093
41. I. Ahmad, V. Jagatap, and H. Patel. Application of density functional theory (DFT) and response surface methodology (RSM) in drug discovery. In: Phytochemistry, computational tools and databases in drug discovery. C. Egbuna, M. Rudrapal, and H. Tijjani (Eds.). Elsevier, Amsterdam, Netherlands pp. 371-392 (2023). DOI: 10.1016/B978-0-323-90593-0.00004-6
42. V. Hadigheh Rezvan, Molecular structure. HOMO-LUMO, and NLO studies of some quinoxaline 1,4-dioxide derivatives: Computational (HF and DFT) analysis. Results in Chemistry 7: 101437 (2024). DOI: 10.1016/j.rechem.2024.101437
43. Y. Xu, Q. Chu, D. Chen, and A. Fuentes. HOMO-LUMO Gaps and Molecular Structures of Polycyclic Aromatic Hydrocarbons in Soot Formation. Frontiers in Mechanical Engineering 7: 744001 (2021). DOI: 10.3389/fmech.2021.744001
44. P. Geerlings. From density functional theory to conceptual density functional theory and biosystems. Pharmaceuticals 15(9): 1112 (2022). DOI: 10.3390/ph15091112
45. R. Pal and P.K. Chattaraj. Electrophilicity index revisited. Journal of Computational Chemistry 44(3): 278-297 (2023). DOI: 10.1002/jcc.26886
