Research Article
BibTex RIS Cite

Proteasome Inhibition by Biflavonoids from the Genus Araucaria: Insights from 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Simulation

Year 2026, Volume: 10 Issue: 1, 56 - 78

Fahruddin Hisanurrijal Purwantiningsih Sugita , Luthfan Irfana

Abstract

Biflavonoid compounds have demonstrated significant potential as anticancer agents, particularly as non-covalent proteasome inhibitors. However, the inhibitory mechanisms of these compounds remain underexplored. The 20S proteasome, a key target in cancer therapy, plays a crucial role in protein degradation and cell cycle regulation, making its inhibition a promising strategy for cancer treatment. This study employs an integrated computational approach, combining Three-Dimensional Quantitative Structure-Activity Relationships (3D-QSAR) modelling, molecular docking, molecular dynamics (MD) simulations, and Molecular Mechanics-Generalized Born and Surface Area Solvation (MM/GBSA) binding energy calculations, to evaluate the proteasome inhibitory potential of biflavonoids from the genus Araucaria. A 3D-QSAR model was developed using 62 flavonoid derivatives, with the Partial Least Squares (PLS) model highlighting electrostatic interactions and hydrogen bond donors as key determinants of proteasome inhibition. Concurrently, the Support Vector Machine (SVM) model exhibited superior predictive accuracy (with an R² of 0.98 and a predicted R² of 0.75) and was employed to screen 22 biflavonoid compounds, identifying five candidates with the highest predicted IC50 values: 7-O-methylagathisflavone (1), 7-O-methylcupressuflavone (15), ochnaflavone (22), 7''-O-methylamentoflavone (11), and 7''-O-methylagathisflavone (2). Molecular docking analysis confirmed strong binding affinities of all five compounds within the β5 active site of the 20S proteasome, with 22 exhibiting the highest docking score. However, MD simulations (100 ns) provided a more comprehensive assessment of binding stability, revealing that 1 showed the most stable behaviour, characterized by low RMSD fluctuations, minimal RMSF values, and a stable radius of gyration (Rg). Conversely, 15 and 22 demonstrated substantial conformational fluctuations, indicating diminished long-term stability. MM/GBSA binding energy calculations further substantiated the ranking observed in 3D-QSAR predictions, underscoring the preeminent potential of compound 1 as a promising inhibitor, as it demonstrated the best IC50 prediction, the strongest binding interactions, and the highest dynamic stability. The integration of 3D-QSAR modelling, docking, and MD simulations provides a comprehensive evaluation of biflavonoid-proteasome interactions, offering valuable insights for developing novel anticancer proteasome inhibitors.

Keywords

3D-QSAR anticancer Araucaria biflavonoid proteasome

Thanks

The authors are grateful to the Indonesian Ministry of Education, Culture, Research, and Technology through IPB University under Project No. 102/E5/PG.02.00.PL/2023, dated June 19, 2023.

References

  • [1] Y. Kim, E.K. Kim, Y. Chey, M.J. Song, H.H. Jang, Targeted protein degradation: principles and applications of the proteasome, Cells, 12 (2023) 1846.
  • [2] M. Benvenuto, S. Ciuffa, C. Focaccetti, D. Sbardella, S. Fazi, M. Scimeca, G.R. Tundo, G. Barillari, M. Segni, E. Bonanno, V. Manzari, A. Modesti, L. Masuelli, M. Coletta, R. Bei, Proteasome inhibition by bortezomib parallels a reduction in head and neck cancer cells growth, and an increase in tumor-infiltrating immune cells, Sci Rep, 11 (2021) 1–23.
  • [3] C.L. Soave, T. Guerin, J. Liu, Q.P. Dou, Targeting the ubiquitin-proteasome system for cancer treatment: discovering novel inhibitors from nature and drug repurposing, Cancer and Metastasis Reviews, 36 (2017) 717–736.
  • [4] Q.P. Dou, J.A. Zonder, Overview of proteasome inhibitor-based anti-cancer therapies: Perspective on bortezomib and second generation proteasome inhibitors versus future generation inhibitors of ubiquitin-proteasome system, Curr Cancer Drug Targets, 14 (2014) 517.
  • [5] N. Guo, Z. Peng, MG132, a proteasome inhibitor, induces apoptosis in tumour cells, Asia Pac J Clin Oncol, 9 (2013) 6–11.
  • [6] F. Obrist, G. Manic, G. Kroemer, I. Vitale, L. Galluzzi, Trial Watch: Proteasomal inhibitors for anticancer therapy, Mol Cell Oncol, 2 (2015).
  • [7] Y. Ren, D.D. Lantvit, E.J.C. De Blanco, L.B.S. Kardono, S. Riswan, H. Chai, C.E. Cottrell, N.R. Farnsworth, S.M. Swanson, Y. Ding, X.C. Li, J.P.J. Marais, D. Ferreira, A.D. Kinghorn, Proteasome-inhibitory and cytotoxic constituents of Garcinia lateriflora: absolute configuration of caged xanthones, Tetrahedron, 66 (2010) 5311–5320.
  • [8] J. Tsalikis, M. Abdel-Nour, A. Farahvash, M.T. Sorbara, S. Poon, D.J. Philpott, S.E. Girardin, Isoginkgetin, a natural biflavonoid proteasome inhibitor, sensitizes cancer cells to apoptosis via disruption of lysosomal homeostasis and impaired protein clearance, Mol Cell Biol, 39 (2019).
  • [9] P. Sugita, D.D. Agusta, H. Dianhar, I.H. Suparto, K. Kurniawanti, D.U.C. Rahayu, L. Irfana, The cytotoxicity and SAR analysis of biflavonoids isolated from Araucaria hunsteinii K Schum leaves against MCF-7 and HeLa cancer cells, J Appl Pharm Sci, 13 (2023) 199–209.
  • [10] M. Damale, S. Harke, F. Kalam Khan, D. Shinde, J. Sangshetti, Recent Advances in Multidimensional QSAR (4D-6D): A Critical Review, Mini-Reviews in Medicinal Chemistry, 14 (2014) 35–55.
  • [11] A.K. Halder, A.S. Moura, M.N.D.S. Cordeiro, QSAR modelling: a therapeutic patent review 2010-present, Expert Opin Ther Pat, 28 (2018) 467–476.
  • [12] A. Tropsha, O. Isayev, A. Varnek, G. Schneider, A. Cherkasov, Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR, Nat Rev Drug Discov, 23 (2024) 141–155.
  • [13] S. Nam, D.M. Smith, Q.P. Dou, Ester bond-containing tea polyphenols potently inhibit proteasome activity in vitro and in vivo, Journal of Biological Chemistry, 276 (2001) 13322–13330.
  • [14] D.M. Smith, Z. Wang, A. Kazi, L.H. Li, T.H. Chan, Q.P. Dou, Synthetic analogues of green tea polyphenols as proteasome inhibitors, Molecular Medicine, 8 (2002) 382–392.
  • [15] D. Chen, K.G. Daniel, M.S. Chen, D.J. Kuhn, K.R. Landis-Piwowar, Q.P. Dou, Dietary flavonoids as proteasome inhibitors and apoptosis inducers in human leukaemia cells, Biochem Pharmacol, 69 (2005) 1421–1432.
  • [16] F. Pajonk, J. Scholber, B. Fiebich, Hypericin-an inhibitor of proteasome function, Cancer Chemother Pharmacol, 55 (2005) 439–446.
  • [17] S.B. Wan, K.R. Landis-Piwowar, D.J. Kuhn, D. Chen, Q.P. Dou, T.H. Chan, Structure-activity study of epi-gallocatechin gallate (EGCG) analogues as proteasome inhibitors, Bioorg Med Chem, 13 (2005) 2177–2185.
  • [18] V. Milacic, S. Banerjee, K.R. Landis-Piwowar, F.H. Sarkar, A.P.N. Majumdar, Q.P. Dou, Curcumin inhibits the proteasome activity in human colon cancer cells in vitro and in vivo, Cancer Res, 68 (2008) 7283–7292.
  • [19] T.L. Chang, Inhibitory effect of flavonoids on 26S proteasome activity, J Agric Food Chem, 57 (2009) 9706–9715.
  • [20] C. Huo, H. Yang, Q.C. Cui, Q.P. Dou, T.H. Chan, Proteasome inhibition in human breast cancer cells with high catechol-O-methyltransferase activity by green tea polyphenol EGCG analogues, Bioorg Med Chem, 18 (2010) 1252–1258.
  • [21] H. Huang, N. Liu, K. Zhao, C. Zhu, X. Lu, S. Li, W. Lian, P. Zhou, X. Dong, C. Zhao, H. Guo, C. Zhang, C. Yang, G. Wen, L. Lu, X. Li, L. Guan, C. Liu, X. Wang, Q.P. Dou, et al., Sanggenon C decreases tumour cell viability associated with proteasome inhibition, Front Biosci, 3 (2011) 1315–1325.
  • [22] L. Pan, S. Matthew, D.D. Lantvit, X. Zhang, T.N. Ninh, H. Chai, E.J. Carcache De Blanco, D.D. Soejarto, S.M. Swanson, A.D. Kinghorn, Bioassay-guided isolation of constituents of Piper sarmentosum using a mitochondrial transmembrane potential assay, J Nat Prod, 74 (2011) 2193–2199.
  • [23] S.H. Shim, 20S proteasome inhibitory activity of flavonoids isolated from Spatholobus suberectus, Phytotherapy Research, 25 (2011) 615–618.
  • [24] M. Piedfer, S. Bouchet, R. Tang, C. Billard, D. Dauzonne, B. Bauvois, p70S6 kinase is a target of the novel proteasome inhibitor 3,3'-diamino-4'-methoxyflavone during apoptosis in human myeloid tumour cells, Biochim Biophys Acta, 1833 (2013) 1316–1328.
  • [25] Y. He, J. Huang, P. Wang, X. Shen, S. Li, L. Yang, W. Liu, A. Suksamrarn, G. Zhang, F. Wang, Y. He, J. Huang, P. Wang, X. Shen, S. Li, L. Yang, W. Liu, A. Suksamrarn, G. Zhang, F. Wang, Emodin potentiates the antiproliferative effect of interferon α/β by activation of JAK/STAT pathway signalling through inhibition of the 26S proteasome, Oncotarget, 7 (2015) 4664–4679.
  • [26] S.B. Wan, D. Chen, Q.P. Dou, T.H. Chan, Study of the green tea polyphenols catechin-3-gallate (CG) and epicatechin-3-gallate (ECG) as proteasome inhibitors, Bioorg Med Chem, 12 (2004) 3521–3527.
  • [27] A. Dreiseitel, P. Schreier, A. Oehme, S. Locher, G. Rogler, H. Piberger, G. Hajak, P.G. Sand, Inhibition of proteasome activity by anthocyanins and anthocyanidins, Biochem Biophys Res Commun, 372 (2008) 57–61.
  • [28] T.L. Chang, H.Y. Ding, K.N. Teng, Y.C. Fang, 5,6,3’,4’-Tetrahydroxy-7-methoxyflavone as a novel potential proteasome inhibitor, Planta Med, 76 (2010) 987–994.
  • [29] P. Tosco, T. Balle, F. Shiri, Open3DALIGN: an open-source software aimed at unsupervised ligand alignment, J Comput Aided Mol Des, 25 (2011) 777–783.
  • [30] D. Hayakawa, N. Sawada, Y. Watanabe, H. Gouda, A molecular interaction field describing nonconventional intermolecular interactions and its application to protein-ligand interaction prediction, J Mol Graph Model, 96 (2020) 107515.
  • [31] P. Tosco, T. Balle, Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields, J Mol Model, 17 (2011) 201–208.
  • [32] M. Pastor, G. Cruciani, S. Clementi, Smart Region Definition: A New Way To Improve the Predictive Ability and Interpretability of Three-Dimensional Quantitative Structure−Activity Relationships, J Med Chem, 40 (1997) 1455–1464.
  • [33] M. Baroni, G. Costantino, G. Cruciani, D. Riganelli, R. Valigi, S. Clementi, Generating Optimal Linear PLS Estimations (GOLPE): An Advanced Chemometric Tool for Handling 3D-QSAR Problems, Quantitative Structure-Activity Relationships, 12 (1993) 9–20.
  • [34] D. Chung, S. Keles, Sparse Partial Least Squares Classification for High Dimensional Data, Stat Appl Genet Mol Biol, 9 (2010) 17.
  • [35] H. Moriwaki, Y.S. Tian, N. Kawashita, T. Takagi, Mordred: a molecular descriptor calculator, J Cheminform, 10 (2018) 4.
  • [36] J. Demšar, T. Curk, A. Erjavec, Č. Group, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, B. Zupan, Orange: Data mining toolbox in python, Journal of Machine Learning Research, 14 (2013) 2349–2353.
  • [37] P. Sugita, S.D.P. Handayani, D.D. Agusta, L. Ambarsari, H. Dianhar, D.U.C. Rahayu, Combined in-silico and in-vitro approaches to evaluate the inhibitory potential of biflavonoids from araucaria plants against α-glucosidase as target protein, Rasayan Journal of Chemistry, 16 (2023) 361–375.
  • [38] V.S. Gontijo, M.H. dos Santos, C. Viegas Jr., Biological and chemical aspects of natural biflavonoids from plants: A brief review, Medicinal Chemistry, 17 (2016).
  • [39] M. V. Shapovalov, R.L. Dunbrack, A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions, Structure, 19 (2011) 844–858.
  • [40] W. Sun, C. Chang, Q. Long, Bayesian non-linear support vector machine for high-dimensional data with incorporation of graph information on features, Proceedings - 2019 IEEE International Conference on Big Data, (2019) 4874–4882.
  • [41] B. Pes, Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains, Neural Comput Appl, 32 (2020) 5951–5973.
  • [42] S. Sengupta, G. Mehta, Non-peptidic natural products as ubiquitin-proteasome inhibitors, Tetrahedron, 75 (2019) 817–853.
  • [43] K.R. Landis-Piwowar, V. Milacic, Q.P. Dou, Relationship between the methylation status of dietary flavonoids and their growth-inhibitory and apoptosis-inducing activities in human cancer cells, J Cell Biochem, 105 (2008) 514.
  • [44] A.K. Varma, R. Patil, S. Das, A. Stanley, L. Yadav, A. Sudhakar, Optimized Hydrophobic Interactions and Hydrogen Bonding at the Target-Ligand Interface Leads the Pathways of Drug-Designing, PLoS One, 5 (2010) e12029.
  • [45] N. Vigneron, V. Ferrari, V. Stroobant, J.A. Habib, B.J. Van Den Eynde, Peptide splicing by the proteasome, J Biol Chem, 292 (2017) 21170.
  • [46] I.A. Guedes, C.S. de Magalhães, L.E. Dardenne, Receptor–ligand molecular docking, Biophys Rev, 6 (2013) 75.
  • [47] A.S. Abouzied, S. Alqarni, K.M. Younes, S.M. Alanazi, D.M. Alrsheed, R.K. Alhathal, B. Huwaimel, A.M. Elkashlan, Structural and free energy landscape analysis for the discovery of antiviral compounds targeting the cap-binding domain of influenza polymerase PB2, Scientific Reports 2024 14:1, 14 (2024) 1–14.
  • [48] A.A. Alzain, F.A. Elbadwi, T.H. Shoaib, A.E. Sherif, W. Osman, A. Ashour, G.A. Mohamed, S.R.M. Ibrahim, E.J. Roh, A.H.E. Hassan, Integrating computational methods guided the discovery of phytochemicals as potential Pin1 inhibitors for cancer: pharmacophore modelling, molecular docking, MM-GBSA calculations and molecular dynamics studies, Front Chem, 12 (2024) 1339891.
  • [49] E. Wang, H. Liu, J. Wang, G. Weng, H. Sun, Z. Wang, Y. Kang, T. Hou, Development and Evaluation of MM/GBSA Based on a Variable Dielectric GB Model for Predicting Protein-Ligand Binding Affinities, J Chem Inf Model, 60 (2020) 5353–5365.
There are 49 citations in total.

Details

Primary Language English
Subjects Chemical Thermodynamics and Energetics
Journal Section Research Article
Authors

Fahruddin Hisanurrijal 0000-0005-4620-5188

Purwantiningsih Sugita 0000-0002-0305-8123

Luthfan Irfana 0000-0002-0642-3421

Early Pub Date May 7, 2025
Publication Date
Submission Date December 12, 2024
Acceptance Date March 4, 2025
Published in Issue Year 2026 Volume: 10 Issue: 1

Cite

APA Hisanurrijal, F., Sugita, P., & Irfana, L. (2025). Proteasome Inhibition by Biflavonoids from the Genus Araucaria: Insights from 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Simulation. Turkish Computational and Theoretical Chemistry, 10(1), 56-78.
AMA Hisanurrijal F, Sugita P, Irfana L. Proteasome Inhibition by Biflavonoids from the Genus Araucaria: Insights from 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Simulation. Turkish Comp Theo Chem (TC&TC). May 2025;10(1):56-78.
Chicago Hisanurrijal, Fahruddin, Purwantiningsih Sugita, and Luthfan Irfana. “Proteasome Inhibition by Biflavonoids from the Genus Araucaria: Insights from 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Simulation”. Turkish Computational and Theoretical Chemistry 10, no. 1 (May 2025): 56-78.
EndNote Hisanurrijal F, Sugita P, Irfana L (May 1, 2025) Proteasome Inhibition by Biflavonoids from the Genus Araucaria: Insights from 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Simulation. Turkish Computational and Theoretical Chemistry 10 1 56–78.
IEEE F. Hisanurrijal, P. Sugita, and L. Irfana, “Proteasome Inhibition by Biflavonoids from the Genus Araucaria: Insights from 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Simulation”, Turkish Comp Theo Chem (TC&TC), vol. 10, no. 1, pp. 56–78, 2025.
ISNAD Hisanurrijal, Fahruddin et al. “Proteasome Inhibition by Biflavonoids from the Genus Araucaria: Insights from 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Simulation”. Turkish Computational and Theoretical Chemistry 10/1 (May 2025), 56-78.
JAMA Hisanurrijal F, Sugita P, Irfana L. Proteasome Inhibition by Biflavonoids from the Genus Araucaria: Insights from 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Simulation. Turkish Comp Theo Chem (TC&TC). 2025;10:56–78.
MLA Hisanurrijal, Fahruddin et al. “Proteasome Inhibition by Biflavonoids from the Genus Araucaria: Insights from 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Simulation”. Turkish Computational and Theoretical Chemistry, vol. 10, no. 1, 2025, pp. 56-78.
Vancouver Hisanurrijal F, Sugita P, Irfana L. Proteasome Inhibition by Biflavonoids from the Genus Araucaria: Insights from 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Simulation. Turkish Comp Theo Chem (TC&TC). 2025;10(1):56-78.

Journal Full Title: Turkish Computational and Theoretical Chemistry


Journal Abbreviated Title: Turkish Comp Theo Chem (TC&TC)