Molecular Dynamics Study of Thymoquinone and Madecassoside as Potential Anti-Parkinson’s Agents via Dopamine D2 Receptor Modulation

Main Article Content

Shinta Kusumawati
Agustina T. Endharti
Farhad Balafif
Shahdevi N. Kurniawan
Rabjhany Anaqah
Husnul Khotimah

Abstract

Parkinson's disease is a neurodegenerative disease characterized by degeneration of dopaminergic neurons. There is no drug can inhibit its progression. The dopamine receptor D2 (DRD2) subtype is pivotal in Parkinson's disease for decreasing locomotor activity. Thymoquinone is an active component of N. sativa that prevents dopamine degradation. Madecassoside is a major chemical component of C.asiatica that contributes to anti-apoptosis in Parkinson's disease. There is currently no research on the two chemicals working together as an anti-Parkinsonian. This study investigated the potential of thymoquinone and madecassoside in regulating the dopamine DRD2 through an in silico approach. The PubChem database was used to retrieve SMILES for each bioactive chemical. The 3D structure of the target protein was obtained from the RSCB PDB database, while that of the PDB ligand control rotigotine (CID: 59227) and the wet-lab control pramipexole (CID: 119570) were obtained from the PubChem database. Molecular dynamics simulations were conducted with Biosciences. The results showed the compounds had a high binding affinity potential, particularly when compared to the wet-lab control, pramipexole. The overlay visualization findings were consistent with the results of H-bond, SASA, and RMSD, indicating that the control drug ligand rotigotine and thymoquinone could better preserve the conformational stability of the DRD2 protein than other ligands. Thymoquinone, a bioactive chemical, acted as the most similar activator to rotigotine. The madecassoside formed the greatest protein-ligand hydrogen bonds. Thymoquinone showed promise as a DRD2 modulator with neuroprotective potential, while madecassoside requires optimization. Findings support natural compounds for Parkinson’s disease via DRD2 modulation.

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How to Cite
Kusumawati, S., Endharti, A. T., Balafif, F., Kurniawan, S. N., Anaqah, R., & Khotimah, H. (2025). Molecular Dynamics Study of Thymoquinone and Madecassoside as Potential Anti-Parkinson’s Agents via Dopamine D2 Receptor Modulation. Tropical Journal of Natural Product Research (TJNPR), 9(4), 1625 – 1630. https://doi.org/10.26538/tjnpr/v9i4.35
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Articles
Author Biography

Shinta Kusumawati, Departmen of Medicine, Doctoral Program in Medical Science, Faculty of Medicine, Universitas Brawijaya, 65145, Malang, Indonesia

Department of Neurology, Faculty of Medicine, Universitas Islam Malang,65144, Malang, Indonesia

References

1. Wang X, Chi J, Huang D, Ding L, Zhao X, Jiang L, Yu Y, Gao F. α-synuclein promotes progression of Parkinson’s disease by upregulating autophagy signaling pathway to activate NLRP3 inflammasome. Exp Ther Med. 2020;19:931–938. Doi: 10.3892/etm.2019.8297

2. Church FC. Treatment Options for Motor and Non-Motor Symptoms of Parkinson’s Disease. Biomolecules. 2021;11(4):1-17. Doi: 0.3390/biom11040612

3. Latif S, Jahangeer M, Maknoon RD, Ashiq M, Ghaffar A, Akram M, El allam A, Bouyahya A, Garipova L, Ali SM, Thiruvengadam M, Azam AM. Dopamine in Parkinson’s disease. Clinica Chimica Acta. 2021;522:114–126. Doi: 10.1016/j.cca.2021.08.009

4. Oertel W, Schulz JB. Current and experimental treatments of Parkinson disease: A guide for neuroscientists. Journal of Neurochemistry. 2016;139:325–337. Doi: 10.1111/jnc.13750

5. Jingwen L, Xi L, Jichuan H, Juan B, Ting Z, Xingfang G, Chao H, Jinsha H, Tao W, Nian X, Zhicheng L.Multiple pathways for natural product treatment of Parkinson’s disease:A mini review. Phytomedicine. 2019;60:1–8.Doi: 10.1016 /j.phymed.2019.152954

6. Hannan A, Rahman A, Sohaq AA., Uddin J, Dash R, Sikder MH, Rahman S,Timalsina B, Munni YA, Sarker PP,Alam M, Mohibbullah, Haque N, Jahan I, Hossain T, Afrin T, Rahman M, Arif TU, Mitra S, Oktaviana DF, Khan K, Choi HJ, Moon IS, Kim B. Black Cumin (Nigella sativa L.): A Comprehensive Review on Phytochemistry, Health Benefits, Molecular Pharmacology, and Safety. Nutrients. 2021;13:1-60. Doi: 10.3390/nu13061784 .

7. Ardah MT, Merghani MM, Haque ME. Thymoquinone prevents neurodegeneration against MPTP in vivo and modulates α-synuclein aggregation in vitro. Neurochem Inter. 2019;128:115–126. Doi: 10.1016/j.neuint.2019.04.014

8. Khotimah H, Ali M, Sumitro SB, Widodo MA. Decreasing α-synuclein aggregation by methanolic extract of Centella asiatica in zebrafish Parkinson’s model. Asian Pac. J Trop Biomed. 2015;5(11):948–954. Available from: http://dx.doi.org/10.1016/j.apjtb.2015.07.024

9. Sun B, Wu L, Wu Y, Zhang C, Qin L, Hayashi M, Kudo M, Gao M, Liu T. Therapeutic Potential of Centella asiatica and Its Triterpenes: A Review. Frontier Pharmacol. 2020;11:1–24. Doi: 10.3389/fphar.2020.568032

10. Bandopadhyay S, Mandal S, Ghorai M, Jha NK, Kumar M, Radha, Ghosh A, Proćków J, Pérez de la Lastra JM, Dey A. Therapeutic properties and pharmacological activities of asiaticoside and madecassoside: A review. J Cell Mol Med . 2023;27(5):593–608. Doi: 10.1111/jcmm.17635

11. Babazadeh A, Vahed FM, Liu Q, Siddiqui SA, Kharazmi MS, Jafari SM. Natural Bioactive Molecules as Neuromedicines for the Treatment/Prevention of Neurodegenerative Diseases. ACS Omega. 2023;8(4):3667–3683. Doi: 10.1021/acsomega.2c06098

12. Durairaj DR, Shanmughavel P. In Silico Drug Design of Thiolactomycin Derivatives Against Mtb-KasA Enzyme to Inhibit Multidrug Resistance of Mycobacterium tuberculosis. Interdiscip Sci Comput Life Sci. 2019;11(2):215–225. Doi: 10.1007/s12539-017-0257-0

13. Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Brit. J Pharmacol. 2007 29;152(1):9–20. Doi: 10.1038/sj.bjp.0707305

14. Agu PC, Afiukwa CA, Orji OU, Ezeh EM, Ofoke IH, Ogbu CO, Ugwuja EI, Aja PM,. Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Scientific Reports. 2023;13(1):1–18. Doi: 10.1038/s41598-023-40160-2

15. Sriramulu DK, Lee SG. Effect of molecular properties of the protein-ligand complex on the prediction accuracy of AutoDock. J Mol Graphics Model. 2021;106:1-7. Doi: 10.1016/j.jmgm.2021.107921

16. Saurabh S, Sivakumar PM, Perumal V, Khosravi A, Sugumaran A, Prabhawathi V. Molecular Dynamics Simulations in Drug Discovery and Drug Delivery. Engin. Mat. 2020;9:275–301. Doi: 10.1007/978-3-030-36260-7_10

17. Afif Z, Santoso MIE, Khotimah H, Satriotomo I, Widjajanto E, Rahayu M, Rianawati SB, Kurniawan SN, Rakhmatiar R, Iskandar DS, Hakimah A, Azizah S, Andriani N, Agustina K. Centella asiatica improved Insomnia through MAPK/ERK Signaling Pathway: In Silico Study. Res J Pharm Technol. 2023;16(2):587–592. Doi: 10.52711/0974-360X.2023.00100

18. Dinengsih S, Winarsih S, Raharjo B, Endharti AT. In silico approaches of gandarusa ( Justicia gendarussa Burm. f ) potential for osteoporosis prevention via EGF pathway. J Pharm Pharmacog. Res. 2024;12(3):439–52. Doi: https://doi.org/10.56499/jppres23.1781_13.3.439 Original

19. Dong J, Wang NN, Yao ZJ, Zhang L, Cheng Y, Ouyang D, Lu AP. ADMETlab: A platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. J Cheminfo. 2018;10(1):1–11. Doi: 10.1186/s13321-018-0283-x

20. 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. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. [Internet]. 2021;49:W5–14. Doi: 10.1093/nar/gkab255

21. Ijoma I, Okafor C, Ajiwe V. Computational Studies of 5-methoxypsolaren as Potential Deoxyhemoglobin S Polymerization Inhibitor. Trop J Nat Prod Res. 2024;8:8835–8841. Doi: 10.26538/tjnpr/v8i10.28

22. Prasasty VD, Istyastono EP. Structure-based design and molecular dynamics simulations of pentapeptide AEYTR as a potential acetylcholinesterase inhibitor. Indones J Chem. 2020;20(4):953–959. Doi: 10.22146/ijc.46329

23. Borjian BM, Shahbazi DM, Shokrgozar MA, Rahimi H, Omidinia E. Computational driven molecular dynamics simulation of keratinocyte growth factor behavior at different pH conditions. Informatics Med Unlocked [Internet]. 2021;23:1-9. Available from: https://doi.org/10.1016/j.imu.2021.100514

24. Musfiroh I, Kartasasmita RE, Ibrahim S, Muchtaridi M, Hidayat S, Ikram NKK. Stability Analysis of the Asiatic Acid-COX-2 Complex Using 100 ns Molecular Dynamic Simulations and Its Selectivity against COX-2 as a Potential Anti-Inflammatory Candidate. Molecules. 2023;28(9):1–12. Doi: 10.3390/molecules28093762

25. Jakaria M, Cho DY, Haque ME, Karthivashan G, Kim IS, Ganesan P, Choi DK. Neuropharmacological Potential and Delivery Prospects of Thymoquinone for Neurological Disorders. Bertolin K, editor. Oxidative Medicine and Cellular Longevity. 2018;2018:1-17. Doi: 10.1155/2018/1209801

26. 26. Xu P, Huang S, Krumm BE, Zhuang Y, Mao C, Zhang Y, Wang Y, Huang XP, Liu YF, He X, Li H, Yin W, Jiang Y, Zang Y, Roth BL, Xu HE. Structural genomics of the human dopamine receptor system. Cell Research. 2023;33(8):604–616. Doi: 10.1038/s41422-023-00808-0

27. Eberhardt, J.,Martins, DS., Tillack, A.F.,Forli, S. AutoDock Vina 1.2. 0: New docking methods, expanded force field, and python bindings. Journal of chemical information and modeling. 2021; 61(8):3891-3898. https://doi.org/10.1021/acs.jcim.1c00203

28. Aini NS, Kharisma VD, Widyananda MH, Murtadlo AAA, Probojati RT, Turista DDR, Tamam MB, Jakhmola V, Yuniarti E, Al Aziz S, Ghifari MR, Albari MT, Mandeli RS, Ghifari MA, Purnamasari D, Oktavia B, Lubis AP, Azra F, Fitri F, Ansori ANM, Rebezov M, Zainul R. In Silico Screening of Bioactive Compounds from Garcinia mangostana L. Against SARS-CoV-2 via Tetra Inhibitors. Pharmacognosy Journal. 2022;14(5):575–579. Doi: 10.5530/pj.2022.14.138

29. Sahakyan, H. Improving virtual screening results with MM/GBSA and MM/PBSA rescoring. Journal of Computer-Aided Molecular Design. 2021; 35(6):731-736.Doi: https://doi.org/10.1007/s10822-021-00389-3

30. Legiawati L, Fadilah F, Bramono K, Setiati S, Yunir E. Molecular dynamic simulation of Centella asiatica compound as an inhibitor of advanced glycation end products. J Appl Pharm Sci. 2020;10(8):001-007. Doi: 10.7324/JAPS.2020.10801

31. Wu Z, Yang L, Wang R, Yang J, Liang P, Ren W, Yu H. Exploring the Mechanism of Asiatic Acid against Atherosclerosis Based on Molecular Docking, Molecular Dynamics, and Experimental Verification. Pharmaceuticals. 2024;17(7):1-17. Doi: 10.3390/ph17070969

32. Sharma J, Kumar V, Singh R, Rajendran V. An in-silico evaluation of different bioactive molecules of tea for their inhibition potency against non structural protein-15 of SARS-CoV-2.Food Chem. 2020;346:1-8.Doi: 10.1016/j.foodchem.2020.128933

33. Ali S, Hassan M, Islam A, Ahmad F. A Review of Methods Available to Estimate Solvent-Accessible Surface Areas of Soluble Proteins in the Folded and Unfolded States. Cur Prot. Pept. Sci. 2014;15(5):456–476. Doi: 10.2174/1389203715666140327114232

34. Bagewadi ZK, Yunus Khan TM, Gangadharappa B, Kamalapurkar A, Shamsudeen SM, Yaraguppi DA. Molecular dynamics and simulation analysis against superoxide dismutase (SOD) target of Micrococcus luteus with secondary metabolites from Bacillus licheniformis recognized by genome mining approach. Saudi J Biol. Sci. 2023;30(9):1-11. Doi: 10.1016/j.sjbs.2023.103753 A