Skip to main content
SpringerLink
Log in

An insight for the inhibition of anxiolytic and anti-convulsant effects in zebrafish using the curcumins via exploring molecular docking and molecular dynamics simulations

  • Original Article
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

In the contemporary landscape, anxiety and seizures stand as major areas of concern, prompting researchers to explore potential drugs against them. While numerous drugs have shown the potential to treat these two neurological conditions, certain adverse effects emphasize the need for development of safer alternatives. This study seeks to employ an in silico approach to evaluate natural compounds, particularly curcumins, as potential inhibitors of GABA-AT to mitigate anxiety and seizures. The proposed methodology includes generating a compound library, minimizing energy, conducting molecular docking using AutoDock, molecular dynamics simulations using Amber, and MM-GBSA calculations. Remarkably, CMPD50 and CMPD88 exhibited promising binding affinities of − 9.0 kcal/mol and − 9.1 kcal/mol with chains A and C of GABA-AT, respectively. Further, MM-GBSA calculations revealed binding free energies of − 10.88 kcal/mol and − 10.72 kcal/mol in CMPD50 and CMPD88, respectively. ADME analysis showed that these compounds contain drug-likeness properties and might be considered as potential drug candidates. The findings from this study will have practical applications in the field of drug discovery for the development of safer and effective drugs for treatment of anxiety and seizures. Overall, this study will lay the groundwork for providing valuable insights into the potential therapeutic effects of curcumins in alleviating anxiety and seizures, establishing a computational framework for future experimental validation.

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

Similar content being viewed by others

Data availability

Data will be made applicable on request of reader.

References

  1. Testa SM, Brandt J (2010) Do patients with psychogenic nonepileptic seizures have positive covert attitudes toward sickness? Epilepsy Behav 19(3):323–327. https://doi.org/10.1016/J.YEBEH.2010.07.014

    Article  PubMed  Google Scholar 

  2. Gurgu R, Ciobanu A, Danasel R, Panea C (2021) Psychiatric comorbidities in adult patients with epilepsy (asystematic review). Exp Ther Med 22:909. https://doi.org/10.3892/etm.2021.10341

    Article  PubMed  PubMed Central  Google Scholar 

  3. da Xavier JC et al (2021) Anxiolytic-like and anticonvulsant effect in adult zebrafish (Danio rerio) through GABAergic system and molecular docking study of chalcone derived from natural products. Biointerface Res Appl Chem 11(6):14021–14031. https://doi.org/10.33263/BRIAC116.1402114031

    Article  Google Scholar 

  4. Ochoa-De La Paz LD et al (2021) The role of GABA neurotransmitter in the human central nervous system, physiology, and pathophysiology. Rev Mex Neurocienc 22(2):67-76. https://doi.org/10.24875/RMN.20000050

    Google Scholar 

  5. de Leon AS, Tadi P (2023) Biochemistry, gamma aminobutyric acid. StatPearl. https://www.ncbi.nlm.nih.gov/books/NBK551683/.

  6. Treiman DM (2001) GABAergic mechanisms in epilepsy. Epilepsia 42(S3):8–12. https://doi.org/10.1046/J.1528-1157.2001.042SUPPL.3008.X

    Article  PubMed  Google Scholar 

  7. Perucca E, Bialer M, White HS (2023) New GABA-targeting therapies for the treatment of seizures and epilepsy: I Role of GABA as a modulator of seizure activity and recently approved medications acting on the GABA system. CNS Drugs 37(9):755–779. https://doi.org/10.1007/S40263-023-01027-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Nuss P (2015) Anxiety disorders and GABA neurotransmission: a disturbance of modulation. Neuropsychiatr Dis Treat 11:165–175. https://doi.org/10.2147/NDT.S58841

    Article  PubMed  PubMed Central  Google Scholar 

  9. Storici P et al (2004) Structures of γ-aminobutyric acid (GABA) aminotransferase, a pyridoxal 5′-phosphate, and [2Fe–2S] cluster-containing enzyme, complexed with γ-ethynyl-GABA and with the antiepilepsy drug vigabatrin. J Biol Chem 279(1):363–373. https://doi.org/10.1074/jbc.M305884200

    Article  CAS  PubMed  Google Scholar 

  10. Allen MJ, Sabir S, Sharma S (2023) GABA receptor. Trends Pharmacol Sci 2(C):62–64. https://doi.org/10.1016/0165-6147(81)90264-9

    Article  Google Scholar 

  11. Silverman RB (2018) Design and mechanism of GABA aminotransferase inactivators. Treatments for epilepsies and addictions. Chem Rev 118(7):4037–4070. https://doi.org/10.1021/acs.chemrev.8b00009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. da Silva AW et al (2020) Anxiolytic-like effect of Azadirachta indica A. Juss. (Neem, Meliaceae) bark on adult zebrafish (Danio rerio): participation of the serotoninergic and GABAergic systems. Pharm Pharmacol Int J 8(4):256–263. https://doi.org/10.15406/PPIJ.2020.08.00303

    Article  Google Scholar 

  13. Kundap UP, Kumari Y, Othman I, Shaikh MF (2017) Zebrafis as a model for epilepsy-induced cognitive dysfunction: a pharmacological, biochemical and behavioral approach. Front Pharmacol 8:515. https://doi.org/10.3389/FPHAR.2017.00515/BIBTEX

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ferreira MKA et al (2021) Chalcones reverse the anxiety and convulsive behavior of adult zebrafish. Epilepsy Behav 117:107881. https://doi.org/10.1016/J.YEBEH.2021.107881

    Article  PubMed  Google Scholar 

  15. Jung MJ, Lippert B, Metcalf BW, Böhlen P, Schechter PJ (1977) γ-Vinyl GABA (4-amino-hex-5-enoic acid), a new selective irreversible inhibitor of GABA-T: effects on brain GABA metabolism in mice1. J Neurochem 29(5):797–802. https://doi.org/10.1111/J.1471-4159.1977.TB10721.X

    Article  CAS  PubMed  Google Scholar 

  16. Waterhouse EJ, Mims KN, Gowda SN (2009) Treatment of refractory complex partial seizures: role of vigabatrin. Neuropsychiatr Dis Treat 5:505–505. https://doi.org/10.2147/ndt.s5236

  17. Shrivastava SK et al (2022) Synthesis, characterization, and biological evaluation of some novel ϒ-aminobutyric acid aminotransferase (GABA-AT) inhibitors. Med Chem Res 31(9):1594–1610. https://doi.org/10.1007/s00044-022-02935-6

    Article  CAS  Google Scholar 

  18. Lee H et al (2015) Mechanism of inactivation of γ-aminobutyric acid aminotransferase by (1S, 3S)-3-amino-4-difluoromethylene-1-cyclopentanoic acid (CPP-115). J Am Chem Soc 137(7):2628–2640. https://doi.org/10.1021/JA512299N

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Feja M et al (2021) OV329, a novel highly potent γ-aminobutyric acid aminotransferase inactivator, induces pronounced anticonvulsant effects in the pentylenetetrazole seizure threshold test and in amygdala-kindled rats. Epilepsia 62(12):3091–3104. https://doi.org/10.1111/EPI.17090

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Vijayakumar S, Kasthuri G, Prabhu S, Manogar P, Parameswari N (2018) Screening and identification of novel inhibitors against human 4-aminobutyrate-aminotransferase: a computational approach. Egypt J Basic Appl Sci 5(3):210–219. https://doi.org/10.1016/j.ejbas.2018.05.008

    Article  Google Scholar 

  21. Garodia P, Hegde M, Kunnumakkara AB, Aggarwal BB (2023) Curcumin, inflammation, and neurological disorders: how are they linked? Integr Med Res. 12(3):100968. https://doi.org/10.1016/j.imr.2023.100968

    Article  PubMed  PubMed Central  Google Scholar 

  22. Jain M et al (2024) In silico and in vitro profiling of curcumin and its derivatives as a potent acetylcholinesterase inhibitor. Biocatal Agric Biotechnol 56:1878–8181. https://doi.org/10.1016/j.bcab.2024.103022

    Article  CAS  Google Scholar 

  23. Hussain H et al (2021) Neuroprotective potential of synthetic mono-carbonyl curcumin analogs assessed by molecular docking studies. Molecules 26(23):7168. https://doi.org/10.3390/MOLECULES26237168

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Contreras-Puente N, Pérez-Orozco D, Camacho-Día F (2022) Curcumin analogues as promissory compounds for inhibition of β-secretase, γ-secretase and GSK-3β implicated at Alzheimer disease: in silico study. Biomed Pharmacol J 15(1):445–452. https://doi.org/10.13005/BPJ/2384

    Article  Google Scholar 

  25. Mills N (2009) ChemDraw Ultra 10.0 CambridgeSoft, 100 CambridgePark Drive, Cambridge, MA 02140. www.cambridgesoft.com. Commercial Price: $1910 for download, $2150 for CD-ROM; Academic Price: $710 for download, $800 for CD-ROM. J Am Chem Soc 128(41):13649–13650. https://doi.org/10.1021/JA0697875

  26. Ferreira MKA et al (2021) Chalcones reverse the anxiety and convulsive behavior of adult zebrafish. Epilepsy Behav. https://doi.org/10.1016/j.yebeh.2021.107881

    Article  PubMed  Google Scholar 

  27. El-Hachem N, Haibe-Kains B, Khalil A, Kobeissy FH, Nemer G (N.D.) Chapter 20 AutoDock and AutoDockTools for protein–ligand docking: beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) as a case study. In: Methods in molecular biology, 1598: p 391-403. https://doi.org/10.1007/978-1-4939-6952-4_20

  28. Das M (2023) Molecular docking study: targeting sickle cell anemia using active phytochemical compounds from zanthoxylum zanthoxyloides. Innovare J Med Sci 11:2023. https://doi.org/10.22159/ijms.2023v11i3.47983

    Article  Google Scholar 

  29. Morris GM et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791. https://doi.org/10.1002/JCC.21256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 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(2):455–461. https://doi.org/10.1002/JCC.21334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Eberhardt J, Santos-Martins D, Tillack AF, Forli S (2021) AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings. J Chem Inf Model 61(8):3891–3898. https://doi.org/10.1021/ACS.JCIM.1C00203/SUPPL_FILE/CI1C00203_SI_002.ZIP

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 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(2):455. https://doi.org/10.1002/JCC.21334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Singh S, Bajpai U, Lynn AM (2014) Structure based virtual screening to identify inhibitors against MurE Enzyme of Mycobacterium tuberculosis using AutoDock Vina. Bioinformation 10(11):697–702. https://doi.org/10.6026/97320630010697

  34. Khan SA, Wu Y, Li ASM, Fu XQ, Yu ZL (2022) Network pharmacology and molecular docking-based prediction of active compounds and mechanisms of action of Cnidii Fructus in treating atopic dermatitis. BMC Complement Med Ther 22(1):275. https://doi.org/10.1186/S12906-022-03734-7

    Article  PubMed  PubMed Central  Google Scholar 

  35. Raghav M et al (2023) In silico molecular prediction of de-novo pteridophytic ligands targeting fungal Sec-14p: a CADD based analysis. Mater Today Proc. https://doi.org/10.1016/J.MATPR.2023.09.210

    Article  Google Scholar 

  36. Baroroh U, Si S, Biotek M, Muscifa ZS, Destiarani W, Rohmatullah FG, Yusuf M (2023) Molecular interaction analysis and visualization of protein–ligand docking using Biovia Discovery Studio Visualizer. Indones J Comput Biol 2(1):22. https://doi.org/10.24198/ijcb.v2i1.46322

    Article  Google Scholar 

  37. Pieroni M et al (2023) MD–ligand–receptor: a high-performance computing tool for characterizing ligand–receptor binding interactions in molecular dynamics trajectories. Int J Mol Sci 24(14):11671. https://doi.org/10.3390/IJMS241411671

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Case DA, Walker RC, Cheatham TE, Simmering C, Roitberg A, Merz KM, Li P (N.D.) Amber 2020 Reference Manual Principal contributors to the current codes. http://ambermd.org/contributors.html. Accessed 26 March 2024

  39. Kumar D, Meena MK, Kumari K, Patel R, Jayaraj A, Singh P (2020) In silico prediction of novel drug–target complex of nsp3 of CHIKV through molecular dynamic simulation. Heliyon 6(8): e04720. https://doi.org/10.1016/j.heliyon.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Abdullahi SH et al. (N.D.) Molecular docking studies of some benzoxazole and benzothiazole derivatives as VEGFR-2 target inhibitors: in silico design, MD simulation, pharmacokinetics and DFT studies. Intel Pharm 2(2):232:250. https://doi.org/10.1016/j.ipha.2023.11.0100

  41. Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10(5):449. https://doi.org/10.1517/17460441.2015.1032936

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Cannady E, Katyayan K, Patel N (2022) ADME principles in small molecule drug discovery and development: an industrial perspective. In: Haschek and Rousseaux’s handbook of toxicologic pathology: principles and practice of toxicologic pathology, vol 1, pp 51–76. https://doi.org/10.1016/B978-0-12-821044-4.00003-0

  43. Singh MB, Vishvakarma VK, Lal AA, Chandra R, Jain P, Singh P (2022) A comparative study of 5-fluorouracil, doxorubicin, methotrexate, paclitaxel for their inhibition ability for Mpro of nCoV: molecular docking and molecular dynamics simulations. J Indian Chem Soc 99(12):100790. https://doi.org/10.1016/J.JICS.2022.100790

    Article  CAS  Google Scholar 

  44. Kumar D et al (2020) Selective docking of pyranooxazoles against nsP2 of CHIKV eluted through isothermally and non-isothermally MD simulations. ChemistrySelect 5(14):4210–4220. https://doi.org/10.1002/SLCT.202000768

    Article  CAS  Google Scholar 

  45. Maurer M, Oostenbrink C (2019) Water in protein hydration and ligand recognition. J Mol Recognit 32(12):e2810. https://doi.org/10.1002/JMR.2810

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Babu Singh M, Himani YS, Singh P (2024) A theoretical study to understand the impact of Mpro of nCoV on the hormones. ChemistrySelect 9(8):e202304767. https://doi.org/10.1002/SLCT.202304767

    Article  CAS  Google Scholar 

  47. Jain P et al (2023) Bioactive thiosemicarbazone coordination metal complexes: synthesis, characterization, theoretical analysis, biological activity, molecular docking and ADME analysis**. Chem Biodivers 20(8):e202300760. https://doi.org/10.1002/CBDV.202300760

    Article  CAS  PubMed  Google Scholar 

  48. Ghahremanian S, Rashidi MM, Raeisi K, Toghraie D (2022) Molecular dynamics simulation approach for discovering potential inhibitors against SARS-CoV-2: a structural review J Mol Liq 354:118901. https://doi.org/10.1016/j.molliq.2022.118901

  49. Roe DR, Cheatham TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084–3095. https://doi.org/10.1021/CT400341P

    Article  CAS  PubMed  Google Scholar 

  50. Abdalla M, Eltayb WA, El-Arabey AA, Singh K, Jiang X (2022) Molecular dynamic study of SARS-CoV-2 with various S protein mutations and their effect on thermodynamic properties. Comput Biol Med 141:105025. https://doi.org/10.1016/J.COMPBIOMED.2021.105025

    Article  CAS  PubMed  Google Scholar 

  51. Dong YW, Liao ML, Meng XL, Somero GN (2018) Structural flexibility and protein adaptation to temperature: molecular dynamics analysis of malate dehydrogenases of marine molluscs. Proc Natl Acad Sci USA 115(6):1274–1279. https://doi.org/10.1073/PNAS.1718910115/-/DCSUPPLEMENTAL

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Raman APS et al (2023) An investigation for the interaction of gamma oryzanol with the Mpro of SARS-CoV-2 to combat COVID-19: DFT, molecular docking, ADME and molecular dynamics simulations. J Biomol Struct Dyn 41(5):1919–1929. https://doi.org/10.1080/07391102.2022.2029770

    Article  CAS  PubMed  Google Scholar 

  53. Raman APS et al (2022) DFT calculations, molecular docking and SAR investigation for the formation of eutectic mixture using thiourea and salicylic acid. J Mol Liq 362:119650. https://doi.org/10.1016/J.MOLLIQ.2022.119650

    Article  CAS  Google Scholar 

  54. Raman APS et al (2022) In silico evaluation of binding of 2-deoxy-d-glucose with Mpro of nCoV to combat COVID-19. Pharmaceutics 14(1):135. https://doi.org/10.3390/PHARMACEUTICS14010135

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Meena MK et al (2022) Designed thiazolidines: an arsenal for the inhibition of nsP3 of CHIKV using molecular docking and MD simulations. J Biomol Struct Dyn 40(4):1607–1616. https://doi.org/10.1080/07391102.2020.1832918

    Article  CAS  PubMed  Google Scholar 

  56. Systemes D (2008) Discovery studio life science modeling and simulations. Dassault Systemes, Paris

    Google Scholar 

  57. Singh E et al (2022) A computational essential dynamics approach to investigate structural influences of ligand binding on Papain like protease from SARS-CoV-2. Comput Biol Chem 99:107721. https://doi.org/10.1016/J.COMPBIOLCHEM.2022.107721

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. González-González A et al (2023) Molecular docking and dynamic simulations of quinoxaline 1, 4-di-N-oxide as inhibitors for targets from Trypanosoma cruzi, Trichomonas vaginalis, and Fasciola hepatica. J Mol Model 29:180. https://doi.org/10.1007/s00894-023-05579-4

    Article  CAS  PubMed  Google Scholar 

  59. Bronowska AK (2011) Thermodynamics of ligand–protein interactions: implications for molecular design. In: Thermodynamics—interaction studies—solids, liquids and gases. https://doi.org/10.5772/19447

  60. Du X et al (N.D.) Molecular sciences insights into protein–ligand interactions:mechanisms, models, and methods Mol Sci 17(2):144. https://doi.org/10.3390/ijms17020144

  61. Özkan H, Adem Ş (2020) Synthesis, spectroscopic characterizations of novel norcantharimides, their ADME properties and docking studies against COVID-19 Mpr. ChemistrySelect 5(18):5422–5428. https://doi.org/10.1002/SLCT.202001123

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Authors are thankful to Professor B. Jayaram for utilizing the facilities of SCFBio, Indian Institute of Technology, Delhi, India.

Funding

Not Applicable.

Author information

Author notes

  1. Iona Massey and Sandeep Yadav share equal authorship for the study.

Authors and Affiliations

  1. Department of Chemistry, Atma Ram Sanatan Dharma College, University of Delhi, Delhi, India

    Iona Massey, Sandeep Yadav, Ram Swaroop Maharia & Prashant Singh

  2. Amity Institute of Biotechnology, Amity University, Noida, India

    Iona Massey

  3. Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, NCR Campus, Ghaziabad, Uttar Pradesh, India

    Sandeep Yadav

  4. Department of Chemistry, Maitreyi College, University of Delhi, Delhi, India

    Durgesh Kumar

  5. Department of Zoology, University of Delhi, Delhi, India

    Kamlesh Kumari

Authors
  1. Iona Massey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sandeep Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Durgesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ram Swaroop Maharia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kamlesh Kumari
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Prashant Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Iona Massey, Sandeep Yadav, Ram Swaroop Maharia and Durgesh Kumar—Performed calculations draft writing, analysis and writing; Prashant Singh and Kamlesh Kumari—Conceptualization and finalization of the manuscript.

Corresponding authors

Correspondence to Durgesh Kumar, Kamlesh Kumari or Prashant Singh.

Ethics declarations

Conflict of interest

We, Kamlesh Kumari, Durgesh Kumar and Prashant Singh (the Corresponding authors) on behalf of all authors declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere. I further confirm that the order of authors listed in the manuscript has been approved by all of us. There is also no conflict of interest in any way.

Ethical approval

Not Applicable.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Massey, I., Yadav, S., Kumar, D. et al. An insight for the inhibition of anxiolytic and anti-convulsant effects in zebrafish using the curcumins via exploring molecular docking and molecular dynamics simulations. Mol Divers (2024). https://doi.org/10.1007/s11030-024-10865-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11030-024-10865-1

Keywords

Search

Navigation