Main Article Content

Abstract

Alzheimer’s disease is one of the neurodegenerative diseases that afflict the elderly. One of the symptoms is a loss cognitive ability due to neuronal death caused by amyloid plaque accumulation. Alzheimer’s disease is one of the most expensive diseases to treat. Drugs for Alzheimer’s treatment only treat the symptoms, not the disease itself. Several pathways, including the mitochondrial cascade, can be used to develop drugs for Alzheimer’s disease, according to NIH guidelines. Caspase3 is a protein that involved in the mitochondrial cascade, specifically in apoptosis. Alzheimer’s therapy may be more effective if caspase3 is targeted. Indonesia is a rich country, particularly in medicinal plants. We used the Structure-Based Drug Design approaches to screen bioactive compounds in Indonesian medicinal plant to find the best compound candidate. In addition, we performed ADMETOX prediction, molecular docking, and molecular dynamic simulation on forty 3D structures of bioactive compounds and donepezil as an FDA approved Alzheimer’s drug. We discovered Miraxanthin-V had a higher binding affinity than donepezil using molecular simulation. As a result, we can conclude that Miraxanthin-V has a high potential of neuroprotective by inhibiting apoptosis.

Keywords

Alzheimer's Disease Bioactive compound Bioinformatics Molecular Simulation

Article Details

How to Cite
1.
Kezia I, Erlina L, Mudjihartini N, Fadilah F. Molecular Simulation for Screening Bioactive Compounds as Potential Candidate for Alzheimer’s Disease. EKSAKTA [Internet]. 2023Apr.27 [cited 2023Jun.6];24(02):179-92. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/401

References

  1. Breijyeh, Z., & Karaman, R. (2020). Comprehensive Review on Alzheimer ’ s Disease : Molecules, 25(24), 5789.
  2. Centers for Disease Control and Prevention. (2018). What is Alzheimer’s Disease? | CDC. https://www.cdc.gov/aging/aginginfo/alzheimers.htm%0Ahttps://www.cdc.gov/aging/aginginfo/alzheimers.htm%0Ahttps://www.cdc.gov/aging/aginginfo/alzheimers.htm#known%0Ahttps://www.cdc.gov/aging/aginginfo/alzheimers.htm%0Ahttps://www.cdc.gov/aging/aginginfo/a
  3. Chi, H., Chang, H. Y., & Sang, T. K. (2018). Neuronal cell death mechanisms in major neurodegenerative diseases. International Journal of Molecular Sciences, 19(10)
  4. Lei, P., Ayton, S., & Bush, A. I. (2021). The essential elements of Alzheimer’s disease. Journal of Biological Chemistry, 296, 100105
  5. Sehar, U., Rawat, P., Reddy, A. P., Kopel, J., & Reddy, P. H. (2022). Amyloid Beta in Aging and Alzheimer’s Disease. International Journal of Molecular Sciences, 23(21).
  6. Erin R. HascupErnesto Solis Jr.Kevin N. Hascup. (2020). Alzheimer’s Disease: The Link Between Amyloid-β and Neurovascular Dysfunction. Journal of Alzheimer’s Disease, 76(4), 1179–1198.
  7. Liu, J., Chang, L., Song, Y., Li, H., & Wu, Y. (2019). The role of NMDA receptors in Alzheimer’s disease. Frontiers in Neuroscience, 13(FEB), 1–22
  8. Singh, R., Letai, A., & Sarosiek, K. (2020). Regulation of apoptosis in health and disease: the balancing act of BCL-2 family proteins. Definitions, 20(3), 175–193.
  9. Espinosa-Oliva, A. M., García-Revilla, J., Alonso-Bellido, I. M., & Burguillos, M. A. (2019). Brainiac Caspases: Beyond the Wall of Apoptosis. Frontiers in Cellular Neuroscience, 13(November), 1–9.
  10. Ułamek-Kozioł, M., Czuczwar, S. J., Kocki, J., Januszewski, S., Bogucki, J., Bogucka-Kocka, A., & Pluta, R. (2019). Dysregulation of Autophagy, Mitophagy, and Apoptosis Genes in the CA3 Region of the Hippocampus in the Ischemic Model of Alzheimer’s Disease in the Rat. Journal of Alzheimer’s Disease, 72(4), 1279–1286.
  11. Ghoneum, M. H., & El Sayed, N. S. (2021). Protective Effect of Biobran/MGN-3 against Sporadic Alzheimer’s Disease Mouse Model: Possible Role of Oxidative Stress and Apoptotic Pathways. Oxidative Medicine and Cellular Longevity, 2021
  12. Wang, R., & Reddy, P. H. (2018). Role of Glutamate Receptor. J. Alzheimer Dis., 57(4), 1041–1048.
  13. Cummings, J., Feldman, H. H., & Scheltens, P. (2019). The “ rights ” of precision drug development for Alzheimer ’ s disease. 5, 1–14.
  14. Li;, H. W. Y. Z. J. W. Y. (2018). Combining in vitro and in silico Approaches to Find New Candidate Drugs Targeting the Pathological Proteins Related to the Alzheimer’s Disease. Current Neuropharmacology, 16, 758–768.
  15. Song, Z., He, C., Yu, W., Yang, M., Li, Z., Li, P., Zhu, X., Xiao, C., & Cheng, S. (2022). Baicalin Attenuated A β 1-42-Induced Apoptosis in SH-SY5Y Cells by Inhibiting the Ras-ERK Signaling Pathway. BioMed Research International, 2022.
  16. Weller, J., & Budson, A. (2018). Current understanding of Alzheimer’s disease diagnosis and treatment. F1000Research, 7(0), 1–9.
  17. Li, J., Sun, M., Cui, X., & Li, C. (2022). Protective Effects of Flavonoids against Alzheimer’s Disease: Pathological Hypothesis, Potential Targets, and Structure–Activity Relationship. International Journal of Molecular Sciences, 23(17).
  18. Cummings, J. L., Tong, G., & Ballard, C. (2019). Treatment Combinations for Alzheimer’s Disease: Current and Future Pharmacotherapy Options. Journal of Alzheimer’s Disease, 67(3), 779–794.
  19. Olivares, D., Deshpande, V. K., Shi, Y., Lahiri, D. K., Greig, N. H., Rogers, J. T., & Huang, X. (2012). N-Methyl D-Aspartate (NMDA) Receptor Antagonists and Memantine Treatment for Alzheimer’s Disease, Vascular Dementia and Parkinson’s Disease HHS Public Access. Curr Alzheimer Res, 9(6), 746–758.
  20. Tiwari, S., Venkata, A., Kaushik, A., Adriana, Y., & Nair, M. (2019). Alzheimer ’ s Disease Diagnostics And Therapeutics Market. Int J Nanomedicine ., Jul 2019(14), 5541–5554.
  21. Batool, M., Ahmad, B., & Choi, S. (2019). A structure-based drug discovery paradigm. International Journal of Molecular Sciences, 20(11).
  22. Pengpid, S., & Peltzer, K. (2019). Use of traditional medicines and traditional practitioners by children in Indonesia: Findings from a national population survey in 2014-2015. Journal of Multidisciplinary Healthcare, 12, 291–298.
  23. You, J. S., Li, C. Y., Chen, W., Wu, X. L., Huang, L. J., Li, R. K., Gao, F., Zhang, M. Y., Liu, H. L., & Qu, W. L. (2020). A network pharmacology-based study on Alzheimer disease prevention and treatment of Qiong Yu Gao. BioData Mining, 13(1), 1–12.
  24. Widyowati, R., & Agil, M. (2018). Chemical constituents and bioactivities of several Indonesian plants typically used in jamu. Chemical and Pharmaceutical Bulletin, 66(5), 506–518.
  25. Budiarti, M., Maruzy, A., Mujahid, R., Sari, A. N., Jokopriyambodo, W., Widayat, T., & Wahyono, S. (2020). The use of antimalarial plants as traditional treatment in Papua Island, Indonesia. Heliyon, 6(12), e05562.
  26. Zhang, R., Zhu, X., Bai, H., & Ning, K. (2019). Network pharmacology databases for traditional Chinese medicine: Review and assessment. Frontiers in Pharmacology, 10(February), 1–14.
  27. Azminah, A., Erlina, L., Radji, M., Mun’im, A., Syahdi, R. R., & Yanuar, A. (2019). In silico and in vitro identification of candidate SIRT1 activators from Indonesian medicinal plants compounds database. Computational Biology and Chemistry, 83(June), 107096.
  28. Rifaai, R. A., Mokhemer, S. A., Saber, E. A., El-Aleem, S. A. A., & El-Tahawy, N. F. G. (2020). Neuroprotective effect of quercetin nanoparticles: A possible prophylactic and therapeutic role in alzheimer’s disease. Journal of Chemical Neuroanatomy, 107(April).
  29. Gregory, J., Vengalasetti, Y. V., Bredesen, D. E., & Rao, R. V. (2021). Neuroprotective herbs for the management of alzheimer’s disease. Biomolecules, 11(4), 1–19.
  30. Erlina, L., Paramita, R. I., Kusuma, W. A., Fadilah, F., Tedjo, A., Pratomo, I. P., Ramadhanti, N. S., Nasution, A. K., Surado, F. K., Fitriawan, A., Istiadi, K. A., & Yanuar, A. (2022). Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches. BMC Complementary Medicine and Therapies, 22(1), 1–19.
  31. Wu, C., Liu, Y., Yang, Y., Zhang, P., Zhong, W., Wang, Y., Wang, Q., Xu, Y., Li, M., Li, X., Zheng, M., Chen, L., & Li, H. (2020). Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharmaceutica Sinica B, 10(5), 766–788.
  32. Pinzi, L., & Rastelli, G. (2019). Molecular docking: Shifting paradigms in drug discovery. International Journal of Molecular Sciences, 20(18).
  33. Hemalatha, G., Sivakumari, K., Rajesh, S., & K, S. D. (2020). In silico molecular docking studies of muricin J, muricin K and muricin L compound from A. muricata againts apoptotic proteins (caspase-3, caspase-9 and β-actin). Innoriginal Originating Innovation, 7(5), 1–4.
  34. Lin, X., Li, X., & Lin, X. (2020). A review on applications of computational methods in drug screening and design. Molecules, 25(6), 1–17.
  35. Patel, H., & Kukol, A. (2021). Integrating molecular modelling methods to advance influenza A virus drug discovery. Drug Discovery Today, 26(2), 503–510.
  36. Ojo, O. A., Ojo, A. B., Okolie, C., Nwakama, M. A. C., Iyobhebhe, M., Evbuomwan, I. O., Nwonuma, C. O., Maimako, R. F., Adegboyega, A. E., Taiwo, O. A., Alsharif, K. F., & Batiha, G. E. S. (2021). Deciphering the interactions of bioactive compounds in selected traditional medicinal plants against alzheimer’s diseases via pharmacophore modeling, auto-QSAR, and molecular docking approaches. Molecules, 26(7).
  37. Yan, R., Zhang, Y., Li, Y., Xia, L., Guo, Y., & Zhou, Q. (2020). Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science, 367(6485), 1444–1448.
  38. Boittier, E. D., Tang, Y. Y., Buckley, M. E., Schuurs, Z. P., Richard, D. J., & Gandhi, N. S. (2020). Assessing molecular docking tools to guide targeted drug discovery of cd38 inhibitors. International Journal of Molecular Sciences, 21(15), 1–19.
  39. Opo, F. A. D. M., Rahman, M. M., Ahammad, F., Ahmed, I., Bhuiyan, M. A., & Asiri, A. M. (2021). Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein. Scientific Reports, 11(1), 1–18.
  40. Jabir, N. R., Rehman, M. T., Alsolami, K., Shakil, S., Zughaibi, T. A., Alserihi, R. F., Khan, M. S., AlAjmi, M. F., & Tabrez, S. (2021). Concatenation of molecular docking and molecular simulation of BACE-1, γ-secretase targeted ligands: in pursuit of Alzheimer’s treatment. Annals of Medicine, 53(1), 2332–2344.
  41. Torres, P. H. M., Sodero, A. C. R., Jofily, P., & Silva-Jr, F. P. (2019). Key topics in molecular docking for drug design. International Journal of Molecular Sciences, 20(18), 1–29.
  42. Vyse, S., & Huang, P. H. (2019). Targeting EGFR exon 20 insertion mutations in non-small cell lung cancer. Signal Transduction and Targeted Therapy, 4(1), 1–10.
  43. Muhammed, M. T., & Aki-Yalcin, E. (2019). Homology modeling in drug discovery: Overview, current applications, and future perspectives. Chemical Biology and Drug Design, 93(1), 12–20.
  44. RCSB PDB - 2XYH_ Caspase-3_CAS60254719.
  45. Syahdi, R. R., Iqbal, J. T., Munim, A., & Yanuar, A. (2019). HerbalDB 2.0: Optimization of construction of three-dimensional chemical compound structures to update Indonesian medicinal plant database. Pharmacognosy Journal, 11(6), 1189–1194.
  46. Erlina, L., Paramita, R. I., Kusuma, W. A., Fadilah, F., Tedjo, A., Pratomo, I. P., Ramadhanti, N. S., Nasution, A. K., Surado, F. K., Fitriawan, A., Istiadi, K. A., & Yanuar, A. (2020). Virtual Screening on Indonesian Herbal Compounds as COVID-19 SupportiveTherapy: Machine Learning and Pharmacophore Modeling Approaches.
  47. Maia, E. H. B., Assis, L. C., de Oliveira, T. A., da Silva, A. M., & Taranto, A. G. (2020). Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Frontiers in Chemistry, 8(April).
  48. Garrett M. Morris. (2010). AutoDock Version 4.2 - User Guide. Guide, 1–49.
  49. ChemAxon - Software Solutions and Services for Chemistry & Biology (Vol. 2019, Issue February 4). (2019).
  50. Valencia, P. L., Sepúlveda, B., Gajardo, D., & Astudillo-Castro, C. (2020). Estimating the product inhibition constant from enzyme kinetic equations using the direct linear plot method in one-stage treatment. Catalysts, 10(8), 1–10.
  51. Zhou, F., He, K., Guan, Y., Yang, X., Chen, Y., Sun, M., Qiu, X., Yan, F., Huang, H., Yao, L., Liu, B., & Huang, L. (2020). Network pharmacology-based strategy to investigate pharmacological mechanisms of Tinospora sinensis for treatment of Alzheimer’s disease. Journal of Ethnopharmacology, 259(May), 112940.
  52. de Ávila, M. B., & de Azevedo, W. F. (2018). Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chemical Biology and Drug Design, 92(2), 1468–1474.
  53. Xiong, G., Wu, Z., Yi, J., Fu, L., Yang, Z., Hsieh, C., Yin, M., Zeng, X., Wu, C., Lu, A., Chen, X., Hou, T., & Cao, D. (2021). ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research, 49(W1), W5–W14.
  54. Lemkul, J. (2019). From Proteins to Perturbed Hamiltonians: A Suite of Tutorials for the GROMACS-2018 Molecular Simulation Package [Article v1.0]. Living Journal of Computational Molecular Science, 1(1), 1–53.
  55. Cui, Q., Zhang, Y. liang, Ma, Y. hui, Yu, H. yu, Zhao, X. zhe, Zhang, L. hui, Ge, S. qin, Zhang, G. wei, & Qin, X. de. (2020). A network pharmacology approach to investigate the mechanism of Shuxuening injection in the treatment of ischemic stroke. Journal of Ethnopharmacology, 257, 112891.
  56. Zu, G., Sun, K., Li, L., Zu, X., Han, T., & Huang, H. (2021). Mechanism of quercetin therapeutic targets for Alzheimer disease and type 2 diabetes mellitus. Scientific Reports, 11(1), 1–11.