Evaluation and validation of synergistic effects of amyloid-beta inhibitor–gold nanoparticles complex on Alzheimer’s disease using deep neural network approach

Abstract

Numerous studies have reported that amyloid-beta 42 (Aβ-42) protein is a high-profile risk factor associated with the onset and progression of Alzheimer’s disease (AD). Accumulation of extracellular senile plaques, synaptic degeneration, and intracellular neurofibrillary tangles were recorded as essential features that facilitate the onset of Aβ-42, resulting in AD. Hence, we attempted a new screening technique to discover potential inhibitors against Aβ-42 using an in silico deep neural network approach. We screened PubChem compounds library and found wgx-50 as a potential inhibitor of Aβ-42. Also, synergistic effects of wgx-50–gold nanoparticles (AuNPs) complex induced significant inhibition of Aβ-42, compared with those of wgx-50 alone. Further, molecular docking analysis, systems biology approach, and time course simulation confirmed that synergistic effects of wgx-50–AuNPs complex have potential application in the treatment for AD. Additionally, we proposed the biological circuit for AD induced by Aβ-42 that can be used to monitor the effect of drugs on AD.

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References

  1. 1.

    A.C. Kaushik, A. Kumar, V.D. Dwivedi, S. Bharadwaj, S. Kumar, K. Bharti, P. Kumar, R.K. Chaudhary, S.K. Mishra: Deciphering the biochemical pathway and pharmacokinetic study of amyloid β-42 with superparamagnetic iron oxide nanoparticles (SPIONs) using systems biology approach. Mol. Neurobiol. 55, 3224–3236 (2018).

    CAS  Article  Google Scholar 

  2. 2.

    B.M. Austen, E.R. Frears, and H. Davies: The use of Seldi ProteinChip™ arrays to monitor production of Alzheimer’s β-amyloid in transfected cells. J. Pept. Sci. 6, 459–469 (2000).

    CAS  Article  Google Scholar 

  3. 3.

    D. Beher, J.D. Wrigley, A.P. Owens, and M.S. Shearman: Generation of C-terminally truncated amyloid-β peptides is dependent on γ-secretase activity. J. Neurochem. 82, 563–575 (2002).

    CAS  Article  Google Scholar 

  4. 4.

    J. Marksteiner, H. Hinterhuber, and C. Humpel: Cerebrospinal fluid biomarkers for diagnosis of Alzheimer’s disease: Beta-amyloid (1-42), tau, phospho-tau-181 and total protein. Drugs Today 43, 423 (2007).

    CAS  Article  Google Scholar 

  5. 5.

    R. Brookmeyer, E. Johnson, K. Ziegler-Graham, H.M. Arrighi: Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement. 3, 186–191 (2007).

    Article  Google Scholar 

  6. 6.

    M. Goedert and M.G. Spillantini: A century of Alzheimer’s disease. Science 314, 777–781 (2006).

    CAS  Article  Google Scholar 

  7. 7.

    T.C. Saido, T. Iwatsubo, D.M. Mann, H. Shimada, Y. Ihara, S. Kawashima: Dominant and differential deposition of distinct β-amyloid peptide species, AβN3(pE), in senile plaques. Neuron 14, 457–466 (1995).

    CAS  Article  Google Scholar 

  8. 8.

    J.A. Hardy and G.A. Higgins: Alzheimer’s disease: The amyloid cascade hypothesis. Science 256, 184–185 (1992).

    CAS  Article  Google Scholar 

  9. 9.

    R. Kayed, E. Head, J.L. Thompson, T.M. McIntire, S.C. Milton, C.W. Cotman, C.G. Glabe: Common structure of soluble amyloid oligomers implies common mechanism of pathogenesis. Science 300, 486–489 (2003).

    CAS  Article  Google Scholar 

  10. 10.

    M. Kawahara and Y. Kuroda: Molecular mechanism of neurodegeneration induced by Alzheimer’s beta-amyloid protein: Channel formation and disruption of calcium homeostasis. Brain Res. Bull. 53, 389–397 (2000).

    CAS  Article  Google Scholar 

  11. 11.

    A. Takashima, K. Noguchi, K. Sato, T. Hoshino, K. Imahori: Tau protein kinase I is essential for amyloid beta-protein-induced neurotoxicity. Proc. Natl. Acad. Sci. U. S. A. 90, 7789–7793 (1993).

    CAS  Article  Google Scholar 

  12. 12.

    S.M. Yatin, M. Aksenova, M. Aksenov, W.R. Markesbery, T. Aulick, and D.A. Butterfield: Temporal relations among amyloid beta-peptide-induced free-radical oxidative stress, neuronal toxicity, and neuronal defensive responses. J. Mol. Neurosci. 11, 183–197 (1998).

    CAS  Article  Google Scholar 

  13. 13.

    H.W. Querfurth, J. Jiang, J.D. Geiger, D.J. Selkoe: Caffeine stimulates amyloid beta-peptide release from beta-amyloid precursor protein-transfected HEK293 cells. J. Neurochem. 69, 1580–1591 (1997).

    CAS  Article  Google Scholar 

  14. 14.

    R. Capone, H. Jang, S.A. Kotler, L. Connelly, F. Teran Arce, S. Ramachandran, B.L. Kagan, R. Nussinov, R. Lal: All-D-enantiomer of beta-amyloid peptide forms ion channels in lipid bilayers. J. Chem. Theory Comput. 8, 1143–1152 (2012).

    CAS  Article  Google Scholar 

  15. 15.

    A. Quist, I. Doudevski, H. Lin, R. Azimova, D. Ng, B. Frangione, B. Kagan, J. Ghiso, R. Lal: Amyloid ion channels: A common structural link for protein-misfolding disease. Proc. Natl. Acad. Sci. U. S. A. 102, 10427–10432 (2005).

    CAS  Article  Google Scholar 

  16. 16.

    R. Bhatia, H. Lin, and R. Lal: Fresh and globular amyloid β protein (1-42) induces rapid cellular degeneration: Evidence for AβP channel-mediated cellular toxicity. FASEB J. 14, 1233–1243 (2000).

    CAS  Article  Google Scholar 

  17. 17.

    M. Lu, M.K. Jolly, and E. Ben-Jacob: Toward decoding the principles of cancer metastasis circuits. Cancer Res. 74, 4574–4587 (2014).

    CAS  Article  Google Scholar 

  18. 18.

    M.K. Jolly, B. Huang, M. Lu, S.A. Mani, H. Levine, and E. Ben-Jacob: Towards elucidating the connection between epithelial–mesenchymal transitions and stemness. J. R. Soc. Interface 11, 20140962 (2014).

    Article  Google Scholar 

  19. 19.

    C. Kiel, E. Yus, and L. Serrano: Engineering signal transduction pathways. Cell 140, 33–47 (2010).

    CAS  Article  Google Scholar 

  20. 20.

    D. McMillen, N. Kopell, J. Hasty, and J. Collins: Synchronizing genetic relaxation oscillators by intercell signaling. Proc. Natl. Acad. Sci. U.S.A. 99, 679–684 (2002).

    CAS  Article  Google Scholar 

  21. 21.

    D. Densmore and S. Hassoun: Design automation for synthetic biological systems. IEEE Des. Test Comput. 29, 7–20 (2012).

    Article  Google Scholar 

  22. 22.

    A.S. Khalil and J.J. Collins: Synthetic biology: Applications come of age. Nat. Rev. Genet. 11, 367–379 (2010).

    CAS  Article  Google Scholar 

  23. 23.

    E.M. Ozbudak, M. Thattai, I. Kurtser, A.D. Grossman, and A. Van Oudenaarden: Regulation of noise in the expression of a single gene. Nat. Genet. 31, 69–73 (2002).

    CAS  Article  Google Scholar 

  24. 24.

    M. Tang, Z. Wang, Y. Zhou, W. Xu, S. Li, L. Wang, D. Wei, Z. Qiao: A novel drug candidate for Alzheimer’s disease treatment: wgx-50 derived from zanthoxylum bungeanum. J. Alzheim. Dis. 34, 203–213 (2013).

    CAS  Article  Google Scholar 

  25. 25.

    S. Hou, R.X. Gu, and D.Q. Wei: Inhibition of beta-amyloid channels with a drug candidate wgx-50 revealed by molecular dynamics simulations. J. Chem. Inf. Model. 57, 2811–2821 (2017).

    CAS  Article  Google Scholar 

  26. 26.

    H.M. Fan, R.X. Gu, Y.J Wang, Y.L. Pi, Y.H. Zhang, Q. Xu, D.Q. Wei: Destabilization of Alzheimer’s Aβ42 protofibrils with a novel drug candidate wgx-50 by molecular dynamics simulations. J. Phys. Chem. B. 119, 11196–11202 (2015).

    CAS  Article  Google Scholar 

  27. 27.

    A.C. Kaushik, S. Sahi: Boolean network model for GPR142 against Type 2 diabetes and relative dynamic change ratio analysis using systems and biological circuits approach. Systems and synthetic biology 1; 9(1-2): 45–54 (2015).

    Article  Google Scholar 

  28. 28.

    L.M. Adleman: Molecular computation of solutions to combinatorial problems. Nature 369, 40 (1994).

    Article  Google Scholar 

  29. 29.

    D. Boneh, C. Dunworth, R.J. Lipton, and J. Sgall: On the computational power of DNA. Discrete Appl. Math. 71, 79–94 (1996).

    Article  Google Scholar 

  30. 30.

    L. Kari, G. Gloor, and S. Yu: Using DNA to solve the bounded post correspondence problem. Theor. Comput. Sci. 231, 193–203 (2000).

    Article  Google Scholar 

  31. 31.

    M. Ogihara and A. Ray: Simulating Boolean circuits on a DNA computer. Algorithmica 25, 239–250 (1999).

    Article  Google Scholar 

  32. 32.

    Y. Benenson, B. Gil, U. Ben-Dor, R. Adar, and E. Shapiro: An autonomous molecular computer for logical control of gene expression. Nature 429, 423–429 (2004).

    CAS  Article  Google Scholar 

  33. 33.

    PubChem Home Page: Available at: http://pubchem.ncbi.nlm.nih.gov/ (accessed November 22, 2017).

  34. 34.

    G.M. Morris, R. Huey, W. Lindstrom, M.F. Sanner, R.K. Belew, D.S. Goodsell, and A.J. Olson: AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 30, 2785–2791 (2009).

    CAS  Article  Google Scholar 

  35. 35.

    A.C. Kaushik, S. Bharadwaj, S. Kumar, D.Q. Wei: Nano-particle mediated inhibition of Parkinson’s disease using computational biology approach. Sci. Rep. 8, 9169 (2018).

    Article  Google Scholar 

  36. 36.

    C. Burch: Logisim a graphical system for logic circuit design and simulation. J. Educ. Resour. Comput. 2, 5–16 (2002).

    Article  Google Scholar 

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Acknowledgments

The simulations in this work were supported by the Center for High Performance Computing, Shanghai Jiao Tong University.

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Correspondence to Dong-Qing Wei.

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Kaushik, A.C., Kumar, A., Peng, Z. et al. Evaluation and validation of synergistic effects of amyloid-beta inhibitor–gold nanoparticles complex on Alzheimer’s disease using deep neural network approach. Journal of Materials Research 34, 1845–1853 (2019). https://doi.org/10.1557/jmr.2018.452

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