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The effect of PC20:0 and di-C7-PC amphiphilic surfactants on the aggregation of Aβ1–40 and Aβ1–42 using molecular dynamics simulation

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Abstract

At least 25 types of degenerative diseases are thought to be caused by amyloid-beta, including Alzheimer’s. However, the exact mechanism of Alzheimer’s is still unknown. It has been shown that amphiphilic surfactants with varying tail lengths, PC20:0 and di-C7-PC, have differing effects on the structures of Aβ (1–40) and Aβ (1–42). This paper examines the interaction between these surfactants and peptides. The calculations were performed at concentrations lower than the critical micelle concentration. Four and 12 peptides of beta-amyloid isomers regimens were used for each simulation. Direct and indirect behavioral effects were extensively investigated. A number of critical analyses, including RMSD, ∆RMSF, Pi, and the hydration network, were performed to examine these peptides in the absence and presence of ligands. There can be a negative ∆RMSF for each sequence as a result of ligand or surfactant binding to that sequence; otherwise, the structure is partially or locally folded. Demonstrated that PC20:0 and di-C7-PC amphiphilic surfactants affect the aggregation of Aβ1–40 and Aβ1–42. The results of our simulations showed that the negative values of ΔRMSF match with some Pi > 1 values for both the hydrophobic and hydrophilic sides of ligands. Therefore, potential interactions between residual peptides and ligands were identified. A negative ΔRMSF did not match the Pi > 1 value, indicating the folding behavior of peptide residues.

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References

  1. Cuba’s aging and Alzheimer longitudinal study‏. SciELO Public Health‏, 2022

  2. L. Gravitz, Drugs: a tangled web of targets. Nature 475, S9-11 (2011)

    Article  CAS  PubMed  Google Scholar 

  3. G. Yamin, K. Ono, M. Inayathullah, D.B. Teplow, Amyloid β-protein assembly as a therapeutic target of Alzheimer’s disease. Curr. Pharm. Des. 14, 3231–3246 (2008)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. G. Chen, T. Xu, Y. Yan, Y. Zhou, Y. Jiang, K. Melcher, H. Eric Xu, Amyloid beta: structure, biology and structure-based therapeutic development. Nature 38, 1205–1235 (2017)

    CAS  Google Scholar 

  5. B. de Strooper, Proteases and proteolysis in Alzheimer disease: a multifactorial view on the disease process. Physiol. Rev. 90, 465–494 (2010)

    Article  PubMed  Google Scholar 

  6. M. Margittai, R. Langen, Fibrils with parallel in-register structure constitute a major class of amyloid fibrils: molecular insights from electron paramagnetic resonance spectroscopy. Q. Rev. Biophys. 41, 265–297 (2008)

    Article  CAS  PubMed  Google Scholar 

  7. B. Cheng, H. Gong, H. Xiao, R.B. Petersen, L. Zheng, K. Huang, Inhibiting toxic aggregation of Amyloidogenic proteins: a therapeutic strategy for protein misfolding diseases. Biochem. Biophys. Acta. 1830, 4860–4871 (2013)

    Article  CAS  PubMed  Google Scholar 

  8. D. J. Selkoe, in Synaptic Plasticity and the Mechanism of Alzheimer's Disease, ed. By Y. Christen (Springer, New York), 2008, p. 89

  9. J.N. Gillet, From molecular dynamics to quantum mechanics of misfolded proteins and amyloid-like macroaggregates applied to neurodegenerative diseases. J. Mol. Graph. Model. 110, 108046 (2022)

    Article  CAS  PubMed  Google Scholar 

  10. D. Smith, R. Cappai, K. Barnham, The redox chemistry of the Alzheimer’s disease amyloid β peptide. Biochem. Biophys. Acta 1768, 1976–1990 (2007)

    Article  CAS  PubMed  Google Scholar 

  11. A. Rauk, The chemistry of Alzheimer’s disease. Chem. Soc. Rev. 38, 2698–2715 (2009)

    Article  CAS  PubMed  Google Scholar 

  12. S. Şahin, N. Dege, A newly synthesized small molecule: the evaluation against Alzheimer’s disease by in silico drug design and computational structure analysis methods. J. Mol. Struct. 1236, 130337 (2021)

    Article  Google Scholar 

  13. W.I. Rosenblum, Why Alzheimer trials fail: removing soluble oligomeric beta amyloid is essential, inconsistent, and difficult. Neurobiol. Aging 35, 969–974 (2014)

    Article  CAS  PubMed  Google Scholar 

  14. J.H. Lee, N.H. Ahn, S. Bin Choi, Y. Kwon, S.H. Yang, Natural products targeting amyloid beta in Alzheimer’s disease. Int. J. Mol. Sci. 22, 1–17 (2021)

    Google Scholar 

  15. J. Mett, T. Hartmann, M.O.W. Grimm, the effects of glycerophospholipids and fatty acids on APP processing: implications for Alzheimer’s disease, ed by Handbook of Lipids in Human Function: Fatty Acids (2016), p. 377–421

  16. M.O.W. Grimm, J. Mett, H.S. Grimm, T. Hartmann, App function and lipids: a bidirectional link. Front. Mol. Neurosci. 10, 63 (2017)

    Article  PubMed  PubMed Central  Google Scholar 

  17. J. Mett, A.A. Lauer, D. Janitschke, L.V. Griebsch, E.L. Theiss, H.S. Grimm, H. Koivisto, H. Tanila, T. Hartmann, M.O.W. Grimm, Medium-chain length fatty acids enhance aβ degradation by affecting insulin-degrading enzyme. Cells 10, 2941 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. S.S.S. Wang, Y.T. Chen, S.W. Chou, Inhibition of amyloid fibril formation of β-amyloid peptides via the amphiphilic surfactants. Biochim. Biophys. 1741, 307–313 (2005)

    Article  CAS  Google Scholar 

  19. P.L. Rosen, J.N. Palmer, B.W. O’Malley, N.A. Cohen, Surfactants in the management of rhinopathologies. Am. J. Rhinol. Allergy 27, 177–180 (2013)

    Article  PubMed  PubMed Central  Google Scholar 

  20. Y-M. Tricot, in Liquid Film Coating, ed. By S. F. Kistler and P. M. Schweizer (Springer, New York, 1997), p. 99

  21. K. Jong, L. Grisanti, A. Hassanali, Hydrogen bond networks and hydrophobic effects in the amyloid β30-35 chain in water: a molecular dynamics study. J. Chem. Inf. Model. 57, 1548–1562 (2017)

    Article  CAS  PubMed  Google Scholar 

  22. M.H. Dehabadi, R. Firouzi, Constructing conformational library for amyloid-β42 dimers as the smallest toxic oligomers using two CHARMM force fields. J. Mol. Graph. Model. 115, 108207 (2022)

    Article  CAS  PubMed  Google Scholar 

  23. M. Badar, S. Shamsi, J. Ahmed, A. Alam, in Transdisciplinarity ed, By N. Rezaei (Springer, New York, 2022), p. 131

  24. A. Rojas, N. Maisuradze, K. Kachlishvili, H.A. Scheraga, G.G. Maisuradze, Elucidating important sites and the mechanism for amyloid fibril formation by coarse-grained molecular dynamics. ACS Chem. Neurosci. 8, 201–209 (2017)

    Article  CAS  PubMed  Google Scholar 

  25. M.J. Abraham, T. Murtola, R. Schulz, S. Páll, J.C. Smith, B. Hess, E. Lindah, GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. Software X 1–2, 19–25 (2015)

    Google Scholar 

  26. A.K. Somavarapu, K.P. Kepp, The dependence of amyloid-β dynamics on protein force fields and water models. ChemPhysChem 16, 3278–3289 (2015)

    Article  CAS  PubMed  Google Scholar 

  27. B.R. Brooks, R.E. Bruccoleri, B.D. Olafson, D.J. States, S. Swaminathan, M. Karplus, CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 4, 187–217 (1983)

    Article  CAS  Google Scholar 

  28. V. Zoete, M.A. Cuendet, A. Grosdidier, O. Michielin, SwissParam: a fast force field generation tool for small organic molecules. J. Comput. Chem. 32, 2359–2368 (2011)

    Article  CAS  PubMed  Google Scholar 

  29. A. Ahmadzade, M. Bozorgmehr, E. Parvaee, The effect of sodium dodecyl sulfate concentration on the aggregation behavior of Aβ (1–42) peptide: molecular dynamics simulation approach. J. Mol. Liq. 303, 112651 (2020)

    Article  CAS  Google Scholar 

  30. M.R. Bozorgmehr, M.R. Housaindokht, Effects of sodium dodecyl sulfate concentration on the structure of bovine carbonic anhydrase: molecular dynamics simulation approach. Rom. J. Biochem. 47, 3–15 (2010)

    CAS  Google Scholar 

  31. M. Ghoula, N. Janel, A.C. Camproux, G. Moroy, Exploring the structural rearrangements of the human insulin-degrading enzyme through molecular dynamics simulations. Int. J. Mol. Sci. 23, 1746 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. N. Jeszenoi, I. Horváth, M. Bálint, D. van der Spoel, C. Hetényi, Mobility-based prediction of hydration structures of protein surfaces. Bioinformatics 31, 1959–1965 (2015)

    Article  CAS  PubMed  Google Scholar 

  33. H. Monhemi, M.R. Reza Housaindokht, A.A. Moosavi-Movahedi, M.R. Bozorgmehr, How a protein can remain stable in a solvent with high content of urea: insights from molecular dynamics simulation of Candida Antarctica lipase B in urea: choline chloride deep eutectic solvent. Phys. Chem. Chem. Phys. 16, 14882 (2014)

    Article  CAS  PubMed  Google Scholar 

  34. G.C. Justino, C.P. Nascimento, M.C. Justino, Molecular dynamics simulations and analysis for bioinformatics undergraduate students. Biochem. Mol. Biol. Educ. 49, 570–582 (2021)

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We gratefully thank the Ferdowsi University of Mashhad for the support (Grant No. 3/55508).

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Authors and Affiliations

  1. Department of Chemistry, Faculty of Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

    Saja Mohammed Abdulkareem & M. R. Housaindokht

  2. Ministry of Higher Education and Scientific Research, Baghdad, Iraq

    Saja Mohammed Abdulkareem

  3. Department of Chemistry, Mashhad Branch, Islamic Azad University, Mashhad, Iran

    M. R. Bozorgmehr

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  1. Saja Mohammed Abdulkareem
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Correspondence to M. R. Housaindokht.

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Abdulkareem, S.M., Housaindokht, M.R. & Bozorgmehr, M.R. The effect of PC20:0 and di-C7-PC amphiphilic surfactants on the aggregation of Aβ1–40 and Aβ1–42 using molecular dynamics simulation. J IRAN CHEM SOC 20, 1357–1370 (2023). https://doi.org/10.1007/s13738-023-02761-6

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