Skip to main content

Advertisement

SpringerLink
Log in

In silico molecular docking and dynamic simulation of eugenol compounds against breast cancer

  • Original Paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

A Correction to this article was published on 05 March 2022

This article has been updated

Abstract

Breast cancer is one of the most severe problems, and it is the primary cause of cancer-related death in females worldwide. The adverse effects and therapeutic resistance development are among the most potent clinical issues for potent medications for breast cancer treatment. The eugenol molecules have a significant affinity for breast cancer receptors. The aim of the study has been on the eugenol compounds, which has potent actions on Erα, PR, EGFR, CDK2, mTOR, ERBB2, c-Src, HSP90, and chemokines receptors inhibition. Initially, the drug-likeness property was examined to evaluate the anti-breast cancer activity by applying Lipinski’s rule of five on 120 eugenol molecules. Further, structure-based virtual screening was performed via molecular docking, as protein-like interactions play a vital role in drug development. The 3D structure of the receptors has been acquired from the protein data bank and is docked with 87 3D PubChem and ZINC structures of eugenol compounds, and five FDA-approved anti-cancer drugs using AutoDock Vina. Then, the compounds were subjected to three replica molecular dynamic simulations run of 100 ns per system. The results were evaluated using root mean square deviation (RMSD), root mean square fluctuation (RMSF), and protein–ligand interactions to indicate protein–ligand complex stability. The results confirm that Eugenol cinnamaldehyde has the best docking score for breast cancer, followed by Aspirin eugenol ester and 4-Allyl-2-methoxyphenyl cinnamate. From the results obtained from in silico studies, we propose that the selected eugenols can be further investigated and evaluated for further lead optimization and drug development.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data availability

All data generated during this study are included in the supplementary information files.

Code availability

Not applicable.

Change history

References

  1. Lokhande KB, Nagar S, Swamy KV (2019) Molecular interaction studies of Deguelin and its derivatives with Cyclin D1 and Cyclin E in cancer cell signaling pathway: the computational approach. Sci Rep 9:1–13

    Article  CAS  Google Scholar 

  2. Rampun A, Morrow PJ, Scotney BW, Wang H (2020) Breast density classification in mammograms: an investigation of encoding techniques in binary-based local patterns. Comput Biol Med 122:103842

    Article  PubMed  Google Scholar 

  3. Akram M, Iqbal M, Daniyal M, Khan AU (2017) Awareness and current knowledge of breast cancer. Biol Res 50:1–23

    Article  CAS  Google Scholar 

  4. Kuhl H, Schneider HPG (2013) Progesterone–promoter or inhibitor of breast cancer. Climacteric 16:54–68

    Article  CAS  PubMed  Google Scholar 

  5. Saha Roy S, Vadlamudi RK (2012) Role of estrogen receptor signaling in breast cancer metastasis. Int J Breast Cancer 2012

  6. Thomas C, Gustafsson J-Å (2011) The different roles of ER subtypes in cancer biology and therapy. Nat Rev Cancer 11:597–608

    Article  CAS  PubMed  Google Scholar 

  7. Hayashi SI, Eguchi H, Tanimoto K et al (2003) The expression and function of estrogen receptor alpha and beta in human breast cancer and its clinical application. Endocr Relat Cancer 10:193–202

    Article  CAS  PubMed  Google Scholar 

  8. Salih AK, Fentiman IS (2001) Breast cancer prevention: present and future. Cancer Treat Rev 27:261–273

    Article  CAS  PubMed  Google Scholar 

  9. Wang Z-Y, Yin L (2015) Estrogen receptor alpha-36 (ER-α36): a new player in human breast cancer. Mol Cell Endocrinol 418:193–206

    Article  CAS  PubMed  Google Scholar 

  10. Acharya R, Chacko S, Bose P et al (2019) Structure based multitargeted molecular docking analysis of selected furanocoumarins against breast cancer. Sci Rep 9:1–13

    Article  Google Scholar 

  11. Kiani J, Khan A, Khawar H et al (2006) Estrogen receptor α-negative and progesterone receptor-positive breast cancer: Lab error or real entity? Pathol Oncol Res 12:223–227

    Article  PubMed  Google Scholar 

  12. Costa R, Shah AN, Santa-Maria CA et al (2017) Targeting epidermal growth factor receptor in triple negative breast cancer: new discoveries and practical insights for drug development. Cancer Treat Rev 53:111–119

    Article  CAS  PubMed  Google Scholar 

  13. Palma G, Frasci G, Chirico A et al (2015) Triple negative breast cancer: looking for the missing link between biology and treatments. Oncotarget 6:26560

    Article  PubMed  PubMed Central  Google Scholar 

  14. Reddy PS, Lokhande KB, Nagar S et al (2018) Molecular modeling, docking, dynamics and simulation of gefitinib and its derivatives with EGFR in non-small cell lung cancer. Curr Comput Aided Drug Des 14:246–252

    Article  CAS  PubMed  Google Scholar 

  15. Ding L, Cao J, Lin W et al (2020) The roles of cyclin-dependent kinases in cell-cycle progression and therapeutic strategies in human breast cancer. Int J Mol Sci 21:1960

    Article  CAS  PubMed Central  Google Scholar 

  16. Carraway H, Hidalgo M (2004) New targets for therapy in breast cancer: mammalian target of rapamycin (mTOR) antagonists. Breast Cancer Res 6:1–6

    Article  CAS  Google Scholar 

  17. Gee JMW, Robertson JFR, Ellis IO, Nicholson RI (2001) Phosphorylation of ERK1/2 mitogen-activated protein kinase is associated with poor response to anti-hormonal therapy and decreased patient survival in clinical breast cancer. Int J cancer 95:247–254

    Article  CAS  PubMed  Google Scholar 

  18. Ferrando IM, Chaerkady R, Zhong J et al (2012) Identification of targets of c-Src tyrosine kinase by chemical complementation and phosphoproteomics. Mol Cell Proteomics 11:355–369

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Zagouri F, Bournakis E, Koutsoukos K, Papadimitriou CA (2012) Heat shock protein 90 (hsp90) expression and breast cancer. Pharmaceuticals 5:1008–1020

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Liu H, Yang Z, Lu W et al (2020) Chemokines and chemokine receptors: a new strategy for breast cancer therapy. Cancer Med 9:3786–3799

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Vela M, Aris M, Llorente M et al (2015) Chemokine receptor-specific antibodies in cancer immunotherapy: achievements and challenges. Front Immunol 6:12

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Al-Sharif I, Remmal A, Aboussekhra A (2013) Eugenol triggers apoptosis in breast cancer cells through E2F1/survivin down-regulation. BMC Cancer 13:1–10

    Article  Google Scholar 

  23. Abdullah ML, Hafez MM, Al-Hoshani A, Al-Shabanah O (2018) Anti-metastatic and anti-proliferative activity of eugenol against triple negative and HER2 positive breast cancer cells. BMC Complement Altern Med 18:1–11

    Article  CAS  Google Scholar 

  24. Islam SS, Al-Sharif I, Sultan A et al (2018) Eugenol potentiates cisplatin anti-cancer activity through inhibition of ALDH-positive breast cancer stem cells and the NF-κB signaling pathway. Mol Carcinog 57:333–346

    Article  CAS  PubMed  Google Scholar 

  25. Bezerra DP, Militão GCG, De Morais MC, De Sousa DP (2017) The dual antioxidant/prooxidant effect of eugenol and its action in cancer development and treatment. Nutrients 9:1367

    Article  CAS  PubMed Central  Google Scholar 

  26. Ma M, Ma Y, Zhang G-J et al (2017) Eugenol alleviated breast precancerous lesions through HER2/PI3K-AKT pathway-induced cell apoptosis and S-phase arrest. Oncotarget 8:56296

    Article  PubMed  PubMed Central  Google Scholar 

  27. Yan X, Zhang G, Bie F et al (2017) Eugenol inhibits oxidative phosphorylation and fatty acid oxidation via downregulation of c-Myc/PGC-1β/ERRα signaling pathway in MCF10A-ras cells. Sci Rep 7:1–13

    Article  CAS  Google Scholar 

  28. Fangjun L, Zhijia Y (2018) Tumor suppressive roles of eugenol in human lung cancer cells. Thorac Cancer 9:25–29

    Article  PubMed  CAS  Google Scholar 

  29. Hussain A, Brahmbhatt K, Priyani A et al (2011) Eugenol enhances the chemotherapeutic potential of gemcitabine and induces anticarcinogenic and anti-inflammatory activity in human cervical cancer cells. Cancer Biother Radiopharm 26:519–527

    CAS  PubMed  Google Scholar 

  30. Carrasco AH, Espinoza CL, Cardile V et al (2008) Eugenol and its synthetic analogues inhibit cell growth of human cancer cells (Part I). J Braz Chem Soc 19:543–548

    Article  Google Scholar 

  31. Choudhury P, Barua A, Roy A et al (2021) Eugenol emerges as an elixir by targeting β-catenin, the central cancer stem cell regulator in lung carcinogenesis: an in vivo and in vitro rationale. Food Funct 12:1063–1078

    Article  CAS  PubMed  Google Scholar 

  32. Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 2IOK, Dykstra KD, Guo L, Birzin ET et al (2007) Estrogen receptor ligands. Part 16: 2-Aryl indoles as highly subtype selective ligands for ERα. Bioorg Med Chem Lett 17:2322–2328

    Article  CAS  Google Scholar 

  34. 4OAR, Petit-Topin I, Fay M, Resche-Rigon M et al (2014) Molecular determinants of the recognition of ulipristal acetate by oxo-steroid receptors. J Steroid Biochem Mol Biol 144:427–435

    Article  CAS  Google Scholar 

  35. 2J6M, Yun C-H, Boggon TJ, Li Y et al (2007) Structures of lung cancer-derived EGFR mutants and inhibitor complexes: mechanism of activation and insights into differential inhibitor sensitivity. Cancer Cell 11:217–227

    Article  CAS  Google Scholar 

  36. 4RJ3, Hanan EJ, Eigenbrot C, Bryan MC et al (2014) Discovery of selective and noncovalent diaminopyrimidine-based inhibitors of epidermal growth factor receptor containing the T790M resistance mutation. J Med Chem 57:10176–10191

    Article  CAS  Google Scholar 

  37. 4DRH: März AM, Fabian A-K, Kozany C et al (2013) Large FK506-binding proteins shape the pharmacology of rapamycin. Mol Cell Biol 33:1357–1367

  38. 2A9I: Lasker M V, Gajjar MM, Nair SK (2005) Cutting edge: molecular structure of the IL-1R-associated kinase-4 death domain and its implications for TLR signaling. J Immunol 175:4175–4179

  39. 2SRC: Xu W, Doshi A, Lei M et al (1999) Crystal structures of c-Src reveal features of its autoinhibitory mechanism. Mol Cell 3:629–638

  40. 2VCJ: Brough PA, Aherne W, Barril X et al (2008) 4, 5-diarylisoxazole Hsp90 chaperone inhibitors: potential therapeutic agents for the treatment of cancer. J Med Chem 51:196–218

  41. 4JL7: Lu G, Wu Y, Jiang Y et al (2013) Structural insights into neutrophilic migration revealed by the crystal structure of the chemokine receptor CXCR2 in complex with the first PDZ domain of NHERF1. PLoS One 8:e76219

  42. 3OE6: Wu B, Chien EYT, Mol CD, et al (2010) Structures of the CXCR4 chemokine GPCR with small-molecule and cyclic peptide antagonists. Science (80- ) 330:1066–1071

  43. 4MBS: Tan Q, Zhu Y, Li J, et al (2013) Structure of the CCR5 chemokine receptor–HIV entry inhibitor maraviroc complex. Science (80- ) 341:1387–1390

  44. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Kim S, Chen J, Cheng T et al (2021) PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res 49:D1388–D1395

    Article  CAS  PubMed  Google Scholar 

  46. Sterling T, Irwin JJ (2015) ZINC 15–ligand discovery for everyone. J Chem Inf Model 55:2324–2337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. O’Boyle NM, Banck M, James CA et al (2011) Open Babel: an open chemical toolbox. J Cheminform 3:1–14

    CAS  Google Scholar 

  48. Wishart DS, Feunang YD, Guo AC et al (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1074–D1082

    Article  CAS  PubMed  Google Scholar 

  49. Molinspiration Cheminformatics. In: Nov. ulica. https://www.molinspiration.com/

  50. Lipinski CA (2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 44:235–249

    Article  CAS  PubMed  Google Scholar 

  51. Chagas CM, Moss S, Alisaraie L (2018) Drug metabolites and their effects on the development of adverse reactions: revisiting Lipinski’s Rule of Five. Int J Pharm 549:133–149

    Article  CAS  PubMed  Google Scholar 

  52. Release S (2017) 3: Desmond molecular dynamics system, DE Shaw research, New York, NY, 2017. Maest Interoperability Tools, Schrödinger, New York, NY

  53. Pushpalatha R, Selvamuthukumar S, Kilimozhi D (2017) Comparative insilico docking analysis of curcumin and resveratrol on breast cancer proteins and their synergistic effect on MCF-7 cell line. J Young Pharm 9:480

    Article  CAS  Google Scholar 

  54. DeBono A, Thomas DR, Lundberg L et al (2019) Novel RU486 (mifepristone) analogues with increased activity against Venezuelan Equine Encephalitis Virus but reduced progesterone receptor antagonistic activity. Sci Rep 9:1–19

    Article  CAS  Google Scholar 

  55. Barzegar M, Ma S, Zhang C et al (2017) SKLB188 inhibits the growth of head and neck squamous cell carcinoma by suppressing EGFR signalling. Br J Cancer 117:1154–1163

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Liu Z, Wang F, Zhou Z-W et al (2017) Alisertib induces G2/M arrest, apoptosis, and autophagy via PI3K/Akt/mTOR-and p38 MAPK-mediated pathways in human glioblastoma cells. Am J Transl Res 9:845

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Ding Y, Fang Y, Moreno J et al (2016) Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem 64:403–413

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. García-Godoy MJ, López-Camacho E, García-Nieto J et al (2016) Molecular docking optimization in the context of multi-drug resistant and sensitive EGFR mutants. Molecules 21:1575

    Article  CAS  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Charmo University and Komar University of Science and Technology for their continued help and facilities in conducting this research.

Author information

Authors and Affiliations

  1. Department of Pharmaceutical Chemistry, College of Medicals and Applied Sciences, Charmo University, Peshawa Street, Chamchamal, 46023, Sulaimani, Iraq

    Hezha O. Rasul

  2. Department of Nanoscience and Applied Chemistry, College of Medicals and Applied Sciences, Charmo University, Peshawa Street, Chamchamal, 46023, Sulaimani, Iraq

    Bakhtyar K. Aziz

  3. Department of Medical Laboratory Science, College of Science, Komar University of Science and Technology, 46001, Sulaimani, Iraq

    Dlzar D. Ghafour

  4. Department of Chemistry, College of Science, University of Sulaimani, 46001, Sulaimani, Iraq

    Dlzar D. Ghafour

  5. Department of Chemistry, Faculty of Sciences and Arts, Eskisehir Osmangazi University, Eskişehir, 26040, Turkey

    Arif Kivrak

Authors
  1. Hezha O. Rasul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bakhtyar K. Aziz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dlzar D. Ghafour
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arif Kivrak
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Hezha O. Rasul: Conceptualization, Methodology, Software, Data Curation.

Bakhtyar K. Aziz: Supervision, Software, Validation.

Dlzar D. Ghafour: Writing—Original Draft, Visualization, Resources.

Arif Kivrak: Validation, Writing—Review and Editing.

Corresponding author

Correspondence to Hezha O. Rasul.

Ethics declarations

Ethics approval

This article does not contain any studied with human participants or animals performed by any of the authors.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised due to changes in authors' affiliation.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rasul, H.O., Aziz, B.K., Ghafour, D.D. et al. In silico molecular docking and dynamic simulation of eugenol compounds against breast cancer. J Mol Model 28, 17 (2022). https://doi.org/10.1007/s00894-021-05010-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-021-05010-w

Keywords

Search

Navigation