The effect of Carbon nanotube on the most effective peptide in Alzheimer's disease in the presence of Dimethyl Sulfoxide: In Silico approach

Document Type : Reasearch Paper

Authors

• Farnaz Jahanbin
• Mohammad Reza Bozorgmehr
• Ali Morsali
• Safar Ali Beyramabadi

Department Of Chemistry, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

Abstract

Due to the non-polar nature of carbon nanotubes, their use in aqueous environments is limited. Therefore, auxiliary solvents such as dimethyl sulfoxide are used to study the interactions between the amyloid-β peptide and carbon nanotubes. In this work, the interaction of Aβ (1-42), the most effective peptide in the development of Alzheimer's disease, with the carbon nanotube was performed using molecular dynamics simulation method. The simulations were carried out in the presence of various concentrations of dimethyl sulfoxide. The stability change of the salt bridge Lys28-Ala42, used in experimental studies, was investigated as a measure of aggregation tendency. Therefore, the radial distribution function of water oxygen atoms and the atoms involved in the salt bridge were used.
The results show that the peak height of the radial distribution function around the oxygen of the residue Ala42 is greater than that of the N_x atom of the residue Lys28. By determining the side-chain orientation of the aromatic residues Phe4, Tyr10, Phe19, Phe20 with the carbon nanotube, it was found that the residues Phe4 and Tyr10 have a stronger interaction with the carbon nanotube than the residues Phe19 and Phe20. The results in this study are in good agreement with the experimental data and could be helpful to understand the mechanism of amyloid-β aggregation.

Keywords

• Alzheimer
• Hydrophobicity
• Peptide
• Radial Distribution Function
• Salt-Bridge

20.1001.1.20088868.2020.11.1.7.5

Main Subjects

• Carbon Nanotubs
• Nano Bio Technology

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References

1.         Haass C., Selkoe D. J. (2007), Soluble protein oligomers in neurodegeneration: Lessons from the Alzheimer's amyloid β-peptide. Nat. Rev. Molec. Cell Biol. 8: 101-106.

2.         Gervais F., Paquette J., Morissette C., Krzywkowski P., Yu M., Azzi M., Lacombe D., Kong X., Aman A., Laurin J., Szarek W. A., Tremblay P., (2007), Targeting soluble Aβ peptide with Tramiprosatefor the treatment of brain amyloidosis. Neurobiol. Aging. 28: 537-547.

3.         Salomone S., Caraci F., Leggio G. M., Fedotova J., Drago F., (2012), New pharmacological strategies for treatment of Alzheimer's disease: Focus on disease modifying drugs. Brit. J. Clinical Pharmacol. 73: 504-517.

4.         Parvaee E., Bozorgmehr M. R., Morsali A., (2019), Role of repulsive forces on self-assembly behavior of amyloid β-peptide (1-40): Molecular dynamics simulation approach. Physica A: Statistic. Mechan. Applicat. 513: 524-535.

5.         Ghule A. V., Kathir K. M., Kumar T. K. S., Tzing S-H., Chang J-Y., Yu C., Ling Y-C., (2007), Carbon nanotubes prevent 2, 2, 2-Trifluoroethanol induced aggregation of protein. Carbon. 45: 1586-1589.

6.         Linse, S., Cabaleiro-Lago C., Xue W-F., Lynch I., Lindman S., Thulin E., Radford Sh-E., Dawson K-A., (2007), Nucleation of protein fibrillation by nanoparticles. Proceed. Nat. Acad. Sci. 104: 8691-8696.

7.         Baweja L., Balamurugan K., Subramanian V., Dhawan A., (2015), Effect of graphene oxide on the conformational transitions of amyloid beta peptide: A molecular dynamics simulation study. J. Molec. Graph. Model. 61: 175-185.

8.         Bussy C., Ali-Boucetta H., Kostarelos K., (2012), Safety considerations for graphene: lessons learnt from carbon nanotubes. Accoun. Chem. Res. 46: 692-701.

9.         Singer S., (1963), The properties of proteins in nonaqueous solvents. Adv. Protein chem.  17: 1-68.

10.       Karle I. L., Flippen-Anderson J. L., Uma K., Balaram P., (1993),Unfolding of an α‐helix in peptide crystals by solvation: Conformational fragility in a heptapeptide. Biopolymers: Orig. Res. Biomolec. 33: 827-837.

11.       Brambilla D., Verpillot R., Le Droumaguet B., Nicolas J., Taverna M., Kóňa J., Lettiero B., Hashemi SH., De Kimpe L., Canovi M., Gobbi M., Nicolas V., Scheper W., Moghimi S. M., Tvaroška I., Couvreur P., Andrieux K. (2012), PEGylated nanoparticles bind to and alter amyloid-beta peptide conformation: Toward engineering of functional nanomedicines for Alzheimer’s disease. ACS Nano. 6: 5897-5908.

12.       Luo J., Wärmländer S. K., Yu C. H., Muhammad K., Gräslund A., Pieter Abrahams J., (2014), The Aβ peptide forms non-amyloid fibrils in the presence of carbon nanotubes. Nanoscale. 6: 6720-6726.

13.       Stefansson S., Knight M., Ahn S., (2012), Specific binding of Alzheimer's Aβ peptide fibrils to single-walled carbon nanotubes. Nanomater. Nanotechnol. 2: 1-7.

14.       Lehmann M., Stansfield R., (1989), Binding of dimethyl sulfoxide to lysozyme in crystals, studied with neutron diffraction. Biochem. 28: 7028-7033.

15.       Shen C.-L., Murphy R. M., (1995), Solvent effects on self-assembly of amyloid-β peptide. Biophys. J. 69: 640-651.

16.       Bussi G., Donadio D., Parrinello M., (2007), Canonical sampling through velocity rescaling. J. Chem. Phys. 126: 014101.

17.       Hess B., Bekker H., Berendsen H. J. C., Fraaije J. G. M., (1998),  LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem. 18: 1463-1472.

18.       Miyamoto S., Kollman P. A., (1992), Settle: An analytical version of the SHAKE and RATTLE algorithm for rigidwater models. J. Comput. Chem. 13: 952-962.

19.       Essmann, U., Perera L.,Berkowitz M. L., (1995), A smooth particle mesh Ewald method. J. Chem. Phys. 103: 8577-8593.

20.       Paravastu A. K., Leapman R. D., Yau W. M., Tycko R., (2008), Molecular structural basis for polymorphism in Alzheimer's β-amyloid fibrils. Proc. Natl. Acad. Sci. U. S. A. 105: 18349-18354.

21.       Meinhardt J., Sachse C., Hortschansky P., Grigorieff N., Fändrich M., (2009), Aβ (1-40) fibril polymorphism implies diverse interaction patterns in amyloid fibrils. J. Molec. Biolog. 386: 869-877.

22.       Honarparvar B., Skelton A. A., (2015), Molecular dynamics simulation and conformational analysis of some catalytically active peptides. J. Molec. Model. 21: 100-106.

23.       Jahanbin F., Bozorgmehr M. R., Morsali A., Beyramabadi S. A., (2019), The effect of different alcohols on the Asp23-Lys28 and Asp23-Ala42 salt bridges of the most effective peptide in Alzheimer's disease: Molecular dynamics viewpoints. J. Molec. Graph. Model. 86: 199-206.

24.       Liu, R., McAllister C., Lyubchenko Y., Sierks M. R., (2004), Residues 17–20 and 30–35 of beta‐amyloid play critical roles in aggregation. J. Neurosc. Res. 75: 162-171.

25.       Gu L., Ngo S., Guo Z., (2012), Solid-support electron paramagnetic resonance (EPR) studies of Aβ40 monomers reveal a structured state with three ordered segments. J.  Biolog. Chem. 287: 9081-9089.

26.       Zhang S., Zhang S., Iwata K., Lachenmann M. J., Peng J. W., Li S., Stimson E. R., Lu Y., Felix A. M., Maggio J. E., Lee J. P., (2000), The Alzheimer's peptide Aβ adopts a collapsed coil structure in water. J. Struc. Biol. 130: 130-141.

27.       Lim K. H., Collver H. H., Le Y. T., Nagchowdhuri P., Kenney J. M., (2007), Characterizations of distinct amyloidogenic conformations of the Aβ (1–40) and (1–42) peptides. Biochem. Biophys. Res. Communic. 2: 443-449.