Plausible compounds drawn from plants as curative agents for neurodegeneration: An in-silico approach

Abstract

Classification of chemical compounds of plants as a source of medicaments for neurodegenerative diseases through computer screening is an efficient process in drug discovery, in advance of laboratory testing and clinical trials. The onset of neurodegenerative disorders incarcerates both sufferers and their families mentally and financially. This investigation emphasises the search for potent compounds via a computational approach, as an initial path towards the treatment of the neurodegenerative diseases Alzheimer’s (AD), Parkinson’s (PD), prion, and Huntington’s (HD) diseases. The therapeutic strategy considered here is chelation therapy, emanated from the heightened levels of metal ions, which play an imperative role in the pathogenesis of all four neurodegenerative disorders mentioned. Hence, potent compounds from Sri Lankan plants to function as lead compounds have been identified for Cu(II), Fe(III), Zn(II), and Al(III) ions, from a library of around 200 chemical compounds, using an umbrella sampling molecular dynamics computational approach where the chelating ability of compounds for the metal ion is assessed in terms of binding free energy. Calculations reveal that 12 Sri Lankan plants possess compounds that could be considered as starting points of leads for AD, PD and prion disease. However, no compound was potentially useful for the HD category, according to the study.

Graphic abstract

Potential of mean force of Al3+ binding to (–)-5-methylmellin found in Semecarpus walkeri with two representative configurations.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 1.

    Przedborski S, Vila M, Jackson-Lewis V (2003) Series introduction: neurodegeneration: what is it and where are we? J Clin Invest 111(1):3–10. https://doi.org/10.1172/JCI17522

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Brown RC, Lockwood AH, Sonawane BR (2005) Neurodegenerative diseases: an overview of environmental risk factors. Environ Health Perspect 113(9):1250–1256. https://doi.org/10.1289/ehp.7567

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    World Health Organization (2006) Neurological disorders: public health challenges. World Health Organization, Geneva

    Google Scholar 

  4. 4.

    Sheikh S, Haque E, Mir SS (2013) Neurodegenerative diseases: multifactorial conformational diseases and their therapeutic interventions. J Neurodegener Dis 2013:1–8. https://doi.org/10.1155/2013/563481

    CAS  Article  Google Scholar 

  5. 5.

    Tutar Y, Ozgür A, Tutar L (2013) Role of protein aggregation in neurodegenerative diseases. In: Kishore U (ed) neurodegenerative diseases. IntechOpen, London

    Google Scholar 

  6. 6.

    Treusch S, Cyr DM, Lindquist S (2009) Amyloid deposits: protection against toxic protein species? Cell Cycle 8(11):1668–1674. https://doi.org/10.4161/cc.8.11.8503

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Bhatia KP, Schneider S (2013) Metal related neurodegenerative disease. Academic Press, Cambridge

    Google Scholar 

  8. 8.

    Crichton R, Ward R (2013) Metal-based neurodegeneration: from molecular mechanisms to therapeutic strategies. Wiley, Hoboken

    Google Scholar 

  9. 9.

    Viles JH (2012) Metal ions and amyloid fiber formation in neurodegenerative diseases. Copper, zinc and iron in Alzheimer's, Parkinson's and prion diseases. Coord Chem Rev 256(19–20):2271–2284. https://doi.org/10.1016/j.ccr.2012.05.003

    CAS  Article  Google Scholar 

  10. 10.

    Butterfield DA, Kanski J (2001) Brain protein oxidation in age-related neurodegenerative disorders that are associated with aggregated proteins. Mech Ageing Dev 122(9):945–962. https://doi.org/10.1016/S0047-6374(01)00249-4

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Gaeta A, Hider RC (2005) The crucial role of metal ions in neurodegeneration: the basis for a promising therapeutic strategy. Br J Pharmacol 146(8):1041–1059. https://doi.org/10.1038/sj.bjp.0706416

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Rathnayake S, Weerasinghe S (2018) Development of an information system of structures and force field parameters of chemical compounds from Sri Lankan Flora. Comb Chem High Throughput Screen 21(8):550–556. https://doi.org/10.2174/1386207321666181010113533

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Kästner J (2011) Umbrella sampling. Wiley Interdiscip Rev Comput Mol Sci 1(6):932–942. https://doi.org/10.1002/wcms.66

    CAS  Article  Google Scholar 

  14. 14.

    Tejchman W, Żesławska E, Zborowski K, Nitek W, Żylewski M (2015) The synthesis, molecular structure and spectra properties of sulfur and selenium deferiprone analogues. Arkivoc 2015(7):216–230. https://doi.org/10.3998/ark.5550190.p009.262

    CAS  Article  Google Scholar 

  15. 15.

    Schüttelkopf AW, van Aalten DM (2004) PRODRG: a tool for high-throughput crystallography of protein–ligand complexes. Acta Crystallogr Sect D Biol Crystallogr 60(8):1355–1363. https://doi.org/10.1107/S0907444904011679

    CAS  Article  Google Scholar 

  16. 16.

    Faro TM, Thim GP, Skaf MS (2010) A Lennard-Jones plus Coulomb potential for Al3+ ions in aqueous solutions. J Chem Phys 132(11):114509. https://doi.org/10.1063/1.3364110

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Oostenbrink C, Villa A, Mark AE, van Gunsteren WF (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. J Comput Chem 25(13):1656–1676. https://doi.org/10.1002/jcc.20090

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    van der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJ (2005) GROMACS: fast, flexible, and free. J Comput Chem 26(16):1701–1718. https://doi.org/10.1002/jcc.20291

    CAS  Article  Google Scholar 

  19. 19.

    Curtiss LA, Halley JW, Hautman J, Rahman A (1987) Nonadditivity of ab initio pair potentials for molecular dynamics of multivalent transition metal ions in water. J Chem Phys 86(4):2319–2327. https://doi.org/10.1063/1.452130

    CAS  Article  Google Scholar 

  20. 20.

    Berendsen HJ, Grigera JR, Straatsma TP (1987) The missing term in effective pair potentials. J Phys Chem 91(24):6269–6271. https://doi.org/10.1021/j100308a038

    CAS  Article  Google Scholar 

  21. 21.

    Hub JS, De Groot BL, van der Spoel D (2010) g_wham—A free weighted histogram analysis implementation including robust error and autocorrelation estimates. J Chem Theory Comput 6(12):3713–3720. https://doi.org/10.1021/ct100494z

    CAS  Article  Google Scholar 

  22. 22.

    Berendsen HJ, Postma JV, van Gunsteren WF, DiNola AR, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81(8):3684–3690. https://doi.org/10.1063/1.448118

    CAS  Article  Google Scholar 

  23. 23.

    Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh Ewald method. J Chem Phys 103(19):8577–8593. https://doi.org/10.1063/1.470117

    CAS  Article  Google Scholar 

  24. 24.

    Evans DJ, Holian BL (1985) The Nose-Hoover thermostat. J Chem Phys 83(8):4069–4074. https://doi.org/10.1063/1.449071

    CAS  Article  Google Scholar 

  25. 25.

    Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52(12):7182–7190. https://doi.org/10.1063/1.328693

    CAS  Article  Google Scholar 

  26. 26.

    Meng Y, Roux B (2015) Efficient determination of free energy landscapes in multiple dimensions from biased umbrella sampling simulations using linear regression. J Chem Theory Comput 11(8):3523–3529. https://doi.org/10.1021/ct501130r

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Chipot C, Pohorille A (2007) Free energy calculations. Springer-Verlag, Berlin

    Google Scholar 

  28. 28.

    Shell M (2009) Histograms and free energies. https://sites.engineering.ucsb.edu › ~shell › Histograms

Download references

Acknowledgements

The authors are grateful to the Department of Chemistry, University of Colombo and the AHEAD-Innovation commercialization enhancement (ICE) grant of the Faculty of Science, University of Colombo, Sri Lanka for providing necessary computer facilities.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Samantha Weerasinghe.

Ethics declarations

Conflict of interest

There are no conflicts to declare.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Thiruchittampalam, S., Weerasinghe, S. Plausible compounds drawn from plants as curative agents for neurodegeneration: An in-silico approach. J Comput Aided Mol Des (2020). https://doi.org/10.1007/s10822-020-00322-0

Download citation

Keywords

  • Chelation therapy
  • Metal ions
  • Neurodegeneration
  • Free energy
  • Umbrella sampling