BAZI HDAC8 İNHİBİTÖRLERİNİN İNHİBİSYON MEKANİZMASININ HESAPLAMALI YÖNTEMLERLE ARAŞTIRILMASI

Öz

Amaç: Kanser dünya çapında önde gelen ölüm nedenlerinden biri olmaya devam etmektedir. Günümüzde uygulanan tedavi yöntemlerinin yan etkileri ve direnç geliştirmeleri bilinmektedir.  Bu nedenle daha etkili ve düşük toksisiteye sahip yeni tedavi stratejilerine olan ihtiyaç artmaktadır. Histon deasetilaz 8 (HDAC8) aşırı ekspresyonunun kanserlerde kötü prognozla bağlantılı olduğu görülmüştür. Bu nedenle HDAC8 yeni kanser önleyici ajanların geliştirilmesinde cazip bir hedeftir. Bu çalışmanın amacı, bazı seçilmiş HDAC8 inhibitörlerinin etki mekanizmalarını hesaplamalı yöntemlerle açıklamaktır.

Gereç ve Yöntem: Bu çalışmada, seçilmiş HDAC8 inhibitörlerinin enzim yapısına bağlanma şekilleri LibDock aracılığıyla doking ve GROMACS 2024 aracılığıyla moleküler dinamik (MD) simülasyon yaparak araştırılmıştır.

Sonuç ve Tartışma: Yapılan doking çalışması bileşik d'nin HDAC8 yapısına en yüksek bağlanma potansiyeli gösterdiğini ortaya koymuştur. HDAC8 inhibisyonunda hidroksamik asidin, amid grubun

ve aromatik halkaların rolleri doking çalışmasıyla teyit edilmiştir. His180 ve Tyr306 kalıntılarının bağlanmada kritik kalıntılar oldukları bulunmuştur. MD simülasyon çalışması doking ile elde edilen HDAC8-inhibitör komplekslerin 200 ns simülasyon sırasında kararlı olduğunu ortaya koymuştur.

 

Anahtar Kelimeler

• HDAC8
• kanser
• LibDock
• MD simülasyonu
• moleküler doking

INVESTIGATION OF THE INHIBITION MECHANISM OF SOME HDAC8 INHIBITORS THROUGH COMPUTATIONAL METHODS

Öz

Objective: Cancer remains one of the leading causes of death around the globe. Current treatment methods are known for their side effects and resistance development. Therefore, there is a growing need for novel therapeutic strategies that are more effective with lower toxicity. Histone deacetylase 8 (HDAC8) overexpression has been linked to poor prognosis in cancer. Hence, HDAC8 is an attractive target in developing new anticancer agents. The objective of this study has been to elucidate mechanisms of action for some selected HDAC8 inhibitors through computational methods.

Material and Method: In this study, binding modes of selected HDAC8 inhibitors to the enzyme structure were explored through docking via LibDock and molecular dynamics (MD) simulation via GROMACS 2024.

Result and Discussion: The docking study disclosed that compound d exhibited the highest binding potential to the HDAC8 structure. The roles of hydroxamic acid, amide group, and aromatic rings in the HDAC8 inhibition were affirmed by the docking study. His180 and Tyr306 residues were found to be critical residues in the binding. The MD simulation study disclosed that HDAC8-inhibitor complexes procured from the docking were stable during the 200 ns simulation.

 

Anahtar Kelimeler

• Cancer
• HDAC8
• LibDock
• MD simulation
• molecular docking

Kaynakça

1. 1. Mima, K., Hamada, T., Inamura, K., et al. (2023). The microbiome and rise of early-onset cancers: Knowledge gaps and research opportunities. Gut Microbes, 15(2), 2269623. [CrossRef]
2. 2. Renzi, C., Odelli, S., Morani, F., et al. (2023). Delays in cancer diagnosis: Challenges and opportunities in Europe. Acta Bio-Medica, 94(S3), e2023161. [CrossRef]
3. 3. Debela, D.T., Muzazu, S.G., Heraro, K.D., et al. (2021). New approaches and procedures for cancer treatment: Current perspectives. SAGE Open Medicine, 9, 20503121211034366. [CrossRef]
4. 4. Holohan, C., Van Schaeybroeck, S., Longley, D.B., et al. (2013). Cancer drug resistance: An evolving paradigm. Nature Reviews Cancer, 13(10), 714-726. [CrossRef]
5. 5. Poonpanichakul, T., Shiao, M.S., Jiravejchakul, N., et al. (2021). Capturing tumour heterogeneity in pre- and post-chemotherapy colorectal cancer ascites-derived cells using single-cell RNA-sequencing. Bioscience Reports, 41(12), BSR20212093. [CrossRef]
6. 6. He, X., Hui, Z., Xu, L., et al. (2022). Medicinal chemistry updates of novel HDACs inhibitors (2020 to present). European Journal of Medicinal Chemistry, 227, 113946. [CrossRef]
7. 7. Ho, T.C.S., Chan, A.H.Y., Ganesan, A. (2020). Thirty years of HDAC inhibitors: 2020 insight and hindsight. Journal of Medicinal Chemistry, 63(21), 12460-12484. [CrossRef]
8. 8. Shanmugam, G., Rakshit, S., Sarkar, K. (2022). HDAC inhibitors: Targets for tumor therapy, immune modulation and lung diseases. Translational Oncology, 16, 101312. [CrossRef]

9. 9. Witt, O., Deubzer, H.E., Milde, T., et al. (2009). HDAC family: What are the cancer relevant targets? Cancer Letters, 277(1), 8-21. [CrossRef]
10. 10. Adhikari, N., Jha, T., Ghosh, B. (2021). Dissecting histone deacetylase 3 in multiple disease conditions: selective inhibition as a promising therapeutic strategy. Journal of Medicinal Chemistry, 64(13), 8827-8869. [CrossRef]
11. 11. Asmamaw, M.D., He, A., Zhang, L.R., et al. (2024). Histone deacetylase complexes: Structure, regulation and function. Biochimica Et Biophysica Acta Reviews on Cancer, 1879(5), 189150. [CrossRef]
12. 12. Kim, J.Y., Cho, H., Yoo, J., et al. (2022). Pathological role of HDAC8: Cancer and beyond. Cells, 11(19), 3161. [CrossRef]
13. 13. Marek, M., Shaik, T.B., Heimburg, T., et al. (2018). Characterization of histone deacetylase 8 (HDAC8) selective ınhibition reveals specific active site structural and functional determinants. Journal of Medicinal Chemistry, 61(22), 10000-10016. [CrossRef]
14. 14. Chakrabarti, A., Oehme, I., Witt, O., et al. (2015). HDAC8: A multifaceted target for therapeutic interventions. Trends in Pharmacological Sciences, 36(7), 481-492. [CrossRef]
15. 15. Heimburg, T., Kolbinger, F.R., Zeyen, P., et al. (2017). Structure-based design and biological characterization of selective histone deacetylase 8 (HDAC8) inhibitors with anti-neuroblastoma activity. Journal of Medicinal Chemistry, 60(24), 10188-10204. [CrossRef]
16. 16. Oehme, I., Deubzer, H.E., Wegener, D., et al. (2009). Histone deacetylase 8 in neuroblastoma tumorigenesis. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 15(1), 91-99. [CrossRef]
17. 17. Watters, J.M., Wright, G., Smith, M.A., et al. (2021). Histone deacetylase 8 inhibition suppresses mantle cell lymphoma viability while preserving natural killer cell function. Biochemical and Biophysical Research Communications, 534, 773-779. [CrossRef]
18. 18. Yan, W., Liu, S., Xu, E., et al. (2013). Histone deacetylase inhibitors suppress mutant p53 transcription via histone deacetylase 8. Oncogene, 32(5), 599-609. [CrossRef]
19. 19. Pantelaiou-Prokaki, G., Mieczkowska, I., Schmidt, G.E., et al. (2022). HDAC8 suppresses the epithelial phenotype and promotes EMT in chemotherapy-treated basal-like breast cancer. Clinical Epigenetics, 14(1), 7. [CrossRef]
20. 20. Yu, D., Wang, L., Wang, Y. (2022). Recent advances in application of computer-aided drug design in anti-influenza a virus drug discovery. International Journal of Molecular Sciences, 23(9), 4738. [CrossRef]
21. 21. Ripphausen, P., Nisius, B., Bajorath, J. (2011). State-of-the-art in ligand-based virtual screening. Drug Discovery Today, 16(9-10), 372-376. [CrossRef]
22. 22. Tari, L.W. (Ed.). (2012). Structure-Based Drug Discovery (C. 841). Humana Press, New York, 1-27. [CrossRef]
23. 23. Morris, G.M., Lim-Wilby, M. (2008) Molecular Docking. Ed.: Kukol, A. Methods in Molecular Biology, Humana Press, Clifton, 365-382. [CrossRef]
24. 24. De Magalhaes, C.S., Dias, V., Dardenne, L.E. (2015). Comparison of differential evolution variants for the molecular ligand-receptor docking problem. 2015 Latin America Congress on Computational Intelligence (LA-CCI), 1-6. [CrossRef]
25. 25. Berman, H., Henrick, K., Nakamura, H. (2003). Announcing the worldwide protein data bank. Nature Structural & Molecular Biology, 10(12), 980-980. [CrossRef]
26. 26. Muhammed, M.T., Aki-Yalcin, E. (2024). Molecular docking: Principles, advances, and its applications in drug discovery. Letters in Drug Design & Discovery, 21(3), 480-495. [CrossRef]
27. 27. Vanommeslaeghe, K., MacKerell, A.D. (2012). Automation of the CHARMM general force field (CGenFF) I: Bond perception and atom typing. Journal of Chemical Information and Modeling, 52(12), 3144-3154. [CrossRef]
28. 28. Abraham, M.J., Murtola, T., Schulz, R., et al. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1-2, 19-25. [CrossRef]
29. 29. Akkoc, S., Muhammed, M.T. (2024). Synthesis, biological application, and computational study of a thymol-based molecule. Journal of Biologically Active Products from Nature, 14(1), 35-50. [CrossRef]
30. 30. Somoza, J.R., Skene, R.J., Katz, B.A., et al. (2004). Structural snapshots of human HDAC8 provide insights into the class I histone deacetylases. Structure, 12(7), 1325-1334. [CrossRef]
31. 31. Guzmán Ramírez, J.E., Mancilla Percino, T. (2023). Synthesis of N-aminophalimides derived from α-amino acids: Theoretical study to find them as HDAC8 inhibitors by docking simulations and in vitro assays. Chemical Biology & Drug Design, 102(6), 1367-1386. [CrossRef]
32. 32. Li, Y., Liu, S., Xu, X., et al. (2024). Integrated molecular modeling and dynamics approaches revealed the mechanism of selective inhibition of HDAC6/8. Journal of Biomolecular Structure and Dynamics, 42(23), 12689-12702. [CrossRef]
33. 33. Khatun, S., Dasgupta, I., Islam, R., et al. (2024). Unveiling critical structural features for effective HDAC8 inhibition: A comprehensive study using quantitative read-across structure-activity relationship (q-RASAR) and pharmacophore modeling. Molecular Diversity, 28(4), 2197-2215. [CrossRef]
34. 34. Sarkar, K., Debnath, S., Ghosh, R., et al. (2024). Repurposing of DrugBank molecules as dual non-hydroxamate HDAC8 and HDAC2 inhibitors by pharmacophore modeling, molecular docking, and molecular dynamics studies. Journal of Biomolecular Structure and Dynamics, 0(0), 1-23. [CrossRef]
35. 35. Chowdhury, D.R., Sen, D., Roy, S.J., et al. (2025). Discovery of potential selective HDAC8 binders: Using combination of pharmacophore-based-structure-based virtual screening, adme filtration, and molecular dynamics studies. ChemistrySelect, 10(15), e202500646. [CrossRef]
36. 36. Al-Madhagi, H., Muhammed, M.T. (2024). Targeting COVID-19 and varicocele by blocking inflammasome: Ligand-based virtual screening. Archives of Biochemistry and Biophysics, 759, 110107. [CrossRef]
37. 37. Erzurumlu, Y., Muhammed, M.T. (2023). Triiodothyronine positively regulates endoplasmic reticulum-associated degradation (ERAD) and promotes androgenic signaling in androgen-dependent prostate cancer cells. Cellular Signalling, 109, 110745. [CrossRef]
38. 38. Kumari, R., Kumar, R., Lynn, A. (2014). G-mmpbsa -A GROMACS tool for high-throughput MM-PBSA calculations. Journal of Chemical Information and Modeling, 54, 1951-1962. [CrossRef]