Russian version English version
Volume 18   Issue 1   Year 2023
Likhachev I.V., Bystrov V.S., Filippov S.V.

Assembly of a Diphenylalanine Peptide Nanotube by Molecular Dynamics Methods

Mathematical Biology & Bioinformatics. 2023;18(1):251-266.

doi: 10.17537/2023.18.251.

References

  1. Nepal D., Kang S., Adstedt K.M., Kanhaiya K., Bockstaller M.R., Brinson L.C., Buehler M.J., Coveney P.V., Dayal K., El-Awady J.A. et al. Hierarchically structured bioinspired nanocomposites: 1. Nat. Mater. 2023;22(1):18–35. doi: 10.1038/s41563-022-01384-1
  2. Pachahara S.K., Subbalakshmi C., Nagaraj R. Formation of Nanostructures by Peptides. Curr. Protein Pept. Sci. 2017;18(9):920–938. doi: 10.2174/1389203717666160724210122
  3. Makam P., Gazit E. Minimalistic peptide supramolecular co-assembly: expanding the conformational space for nanotechnology. Chem. Soc. Rev. 2018;47(10):3406–3420. doi: 10.1039/C7CS00827A
  4. Yuan C., Ji W., Xing R., Li J., Gazit E., Yan X. Hierarchically oriented organization in supramolecular peptide crystals. Nature Reviews Chemistry. 2019;3(10):567–588. doi: 10.1038/s41570-019-0129-8
  5. Raymond D.M., Nilsson B.L. Multicomponent peptide assemblies. Chem. Soc. Rev. 2018;47(10):3659–3720.  doi: 10.1039/C8CS00115D
  6. Scanlon S., Aggeli A. Self-assembling peptide nanotubes. Nano Today. 2008;3(3):22–30. doi: 10.1016/S1748-0132(08)70041-0
  7. Bystrov V.S., Zelenovskiy P.S., Nuraeva A.S., Kopyl S., Zhulyabina O.A., Tverdislov V.A. Chiral peculiar properties of self-organization of diphenylalanine peptide nanotubes: Modeling of structure and properties. Mathematical Biology and Bioinformatics. 2019;14(1):94–125. doi: 10.17537/2019.14.94
  8. Kol N., Adler-Abramovich L., Barlam D., Shneck R.Z., Gazit E., Rousso I. Self-Assembled Peptide Nanotubes Are Uniquely Rigid Bioinspired Supramolecular Structures. Nano Lett. 2005;5(7):1343–1346. 10.1021/nl0505896. doi: 10.1021/nl0505896
  9. Shklovsky J., Beker P., Amdursky N., Gazit E., Rosenman G. Bioinspired peptide nanotubes: Deposition technology and physical properties. Materials Science and Engineering: B. 2010;169(1):62–66. doi: 10.1016/j.mseb.2009.12.040
  10. Reches M., Gazit E. Controlled patterning of aligned self-assembled peptide nanotubes: 3. Nature Nanotech. 2006;1(3):195–200. doi: 10.1038/nnano.2006.139
  11. Görbitz C.H. Nanotube formation by hydrophobic dipeptides. Chemistry. 2001;7(23):5153–5159. doi: 10.1002/1521-3765(20011203)7:23<5153::AID-CHEM5153>3.0.CO;2-N
  12. Bystrov V.S., Paramonova E., Bdikin I., Kopyl S., Heredia A., Pullar R.C., Kholkin A.L. BioFerroelectricity: Diphenylalanine Peptide Nanotubes Computational Modeling and Ferroelectric Properties at the Nanoscale. Ferroelectrics. 2012;440:3–24. doi: 10.1080/00150193.2012.741923
  13. Bystrov V., Sidorova A., Lutsenko A., Shpigun D., Malyshko E., Nuraeva A., Zelenovskiy P., Kopyl S., Kholkin A. Modeling of Self-Assembled Peptide Nanotubes and Determination of Their Chirality Sign Based on Dipole Moment Calculations: 9. Nanomaterials. 2021;11(9):2415. doi: 10.3390/nano11092415
  14. Bystrov V., Coutinho J., Zelenovskiy P., Nuraeva A., Kopyl S., Zhulyabina O., Tverdislov V. Structures and Properties of the Self-Assembling Diphenylalanine Peptide Nanotubes Containing Water Molecules: Modeling and Data Analysis: 10. Nanomaterials. 2020;10(10):1999. doi: 10.3390/nano10101999
  15. Bystrov V.S., Filippov S.V. Molecular modelling and computational studies of peptide diphenylalanine nanotubes, containing waters: structural and interactions analysis. J. Mol. Model. 2022;28(4):81. doi: 10.1007/s00894-022-05074-2
  16. CCDC . https://www.ccdc.cam.ac.uk/ (accessed: 30.06.2023).
  17. Zelenovskiy P.S., Nuraeva A.S., Kopyl S., Arkhipov S.G., Vasilev S.G., Bystrov V.S., Gruzdev D.A., Waliczek M., Svitlyk V., Shur V.Y., Mafra L., Kholkin A.L. Chirality-Dependent Growth of Self-Assembled Diphenylalanine Microtubes. Crystal Growth and Design. 2019;19(11):6414–6421. doi: 10.1021/acs.cgd.9b00884
  18. Tverdislov V.A. Chirality as a primary switch of hierarchical levels in molecular biological systems. Biophysics. 2013;58(1):128–132. doi: 10.1134/S0006350913010156
  19. Zelenovskiy P., Kornev I., Vasilev S., Kholkin A. On the origin of the great rigidity of self-assembled diphenylalanine nanotubes. Phys. Chem. Chem. Phys. 2016;18(43):29681–29685. doi: 10.1039/C6CP04337B
  20. Tao K., Makam P., Aizen R., Gazit E. Self-assembling peptide semiconductors. Science. 2017;358(6365):9756. doi: 10.1126/science.aam9756
  21. Amdursky N., Molotskii M., Aronov D., Adler-Abramovich L., Gazit E., Rosenman G. Blue Luminescence Based on Quantum Confinement at Peptide Nanotubes. Nano Lett. 2009;9(9):3111–3115. doi: 10.1021/nl9008265
  22. Gan Z., Wu X., Zhu X., Shen J. Light-Induced Ferroelectricity in Bioinspired Self-Assembled Diphenylalanine Nanotubes/Microtubes. Angewandte Chemie International Edition. 2013;52(7):2055–2059. doi: 10.1002/anie.201207992
  23. Gan Z., Wu X., Zhang J., Zhu X., Chu P.K. In situ thermal imaging and absolute temperature monitoring by luminescent diphenylalanine nanotubes. Biomacromolecules. 2013;14(6):2112–2116. doi: 10.1021/bm400562c
  24. Nikitin T., Kopyl S., Shur V.Ya., Kopelevich Y.V., Kholkin A.L. Low-temperature photoluminescence in self-assembled diphenylalanine microtubes. Physics Letters A. 2016;380(18):1658–1662. doi: 10.1016/j.physleta.2016.02.043
  25. Nguyen V., Zhu R., Jenkins K., Yang R. Self-assembly of diphenylalanine peptide with controlled polarization for power generation: 1. Nat. Commun. 2016;7(1):13566. doi: 10.1038/ncomms13566
  26. Jenkins K., Kelly S., Nguyen V., Wu Y., Yang R. Piezoelectric diphenylalanine peptide for greatly improved flexible nanogenerators. Nano Energy. 2018;51:317–323. doi: 10.1016/j.nanoen.2018.06.061
  27. Vasilev S., Zelenovskiy P., Vasileva D., Slautina A., Shur V., Kholkin A. Piezoelectric properties of diphenylalanine microtubes prepared from the solution. Journal of Physics and Chemistry of Solids. 2016;93. doi: 10.1016/j.jpcs.2016.02.002
  28. Bystrov V.S. Photoferroelectricity in di-phenylalanine peptide nanotubes. Computational Condensed Matter. 2018;14. doi: 10.1016/j.cocom.2017.11.007
  29. Bystrov V., Paramonova E., Zelenovskii P., Kopyl S., Shen H., Lin T., Fridkin V. Photoelectronic Properties of Chiral Self-Assembled Diphenylalanine Nanotubes: A Computational Study: 2. Symmetry. 2023;15(2):504. doi: 10.3390/sym15020504
  30. Likhachev I.V., Bystrov V.S. Assembly of a Phenylalanine Nanotube by the use of Molecular Dynamics Manipulator. Math. Biol. Bioinf. 2021;16(2):244–255. doi: 10.17537/2021.16.244
  31. Likhachev I., Balabaev N., Bystrov V., Paramonova E., Avakyan L., Bulina N. Molecular Dynamics Simulation of the Thermal Behavior of Hydroxyapatite: 23. Nanomaterials. 2022;12(23):4244. doi: 10.3390/nano12234244
  32. German H.W., Uyaver S., Hansmann U.H.E. Self-Assembly of Phenylalanine-Based Molecules. J. Phys. Chem. A. 2015;119(9):1609–1615. doi: 10.1021/jp5077388
  33. Adler-Abramovich L., Vaks L., Carny O., Trudler D., Magno A., Caflisch A., Frenkel D., Gazit E. Phenylalanine assembly into toxic fibrils suggests amyloid etiology in phenylketonuria: 8. Nat. Chem. Biol. 2012;8(8):701–706. doi: 10.1038/nchembio.1002
  34. Novosibirsk State University Research Portal. https://research.nsu.ru/ru/publications/chirality-dependent-growth-of-self-assembled-diphenylalanine-micr (accessed: 30.06.2023).
  35. Lemak A.S., Balabaev N.K. A comparison between collisional dynamics and brownian dynamics: 4. Molecular Simulation. 1995;15(4). doi: 10.1080/08927029508022336
  36. Lemak A.S., Balabaev N.K. Molecular dynamics simulation of a polymer chain in solution by collisional dynamics method: 15. Journal of Computational Chemistry. 1996;17(15). doi: 10.1002/(SICI)1096-987X(19961130)17:15<1685::AID-JCC1>3.0.CO;2-L
  37. Filippov S.V., Bystrov V.S. A visual differential analysis of structural features of internal cavities in two chiral forms of diphenylalanine nanotubes. Biophysics. 2020;65(3):374-380. doi: 10.1134/S0006350920030057
  38. Filippov S.V., Polozov R.V., Sivozhelezov V.S. Hypsometric mapping based visualization of (bio)macromolecular 3D structures. Keldysh Institute Preprints. 2019(61):1–14. doi: 10.20948/prepr-2019-61
  39. Wang J.M., Cieplak P., Kollman P. How Well Does a Restrained Electrostatic Potential (RESP) Model Perform in Calculating Conformational Energies of Organic and Biological Molecules? J. Comput. Chem. 1999;21:1049. doi: 10.1002/1096-987X(200009)21:12<1049::AID-JCC3>3.0.CO;2-F
  40. Glyakina A.V., Likhachev I.V., Balabaev N.K., Galzitskaya O.V. Comparative mechanical unfolding studies of spectrin domains R15, R16 and R17. J. Struct. Biol. 2018;201(2):162–170. doi: 10.1016/j.jsb.2017.12.003
  41. Likhachev I.V., Balabaev N.K. Trajectory Analyzer of Molecular Dynamics. Math. Biol. Bioinf. 2007;2(1):120–129. doi: 10.17537/2007.2.120
  42. Likhachev I.V., Balabaev N.K., Galzitskaya O.V. Available Instruments for Analyzing Molecular Dynamics Trajectories. Open Biochem. J. 2016;10:1–11. doi: 10.2174/1874091X01610010001
  43. HyperChem. http://www.hypercubeusa.com/?tabid=360 (accessed: 30.06.2023).
  44. PyMOL by Schrödinger. https://pymol.org/2/ (accessed: 10.04.2023).
  45. Tverdislov V.A., Sidorova A.E., Bagrova O.E., Belova E.V., Bystrov V.S., Levashova N.T., Lutsenko A.O., Semenova E.V., Shpigun D.K. Chirality As a Symmetric Basis of Self-Organization of Biomacromolecules. Biophysics. 2022;67(5):673–691. doi: 10.1134/S0006350922050190
  46. Filippov S.V., Polozov R.V., Sivozhelezov V.S. Visualization of spatial structures of (bio)macromolecules: construction of "hypsometric" maps. In: Information Technology and Mathematical Modeling: Proceedings of the XVIII International Conference named after A.F. Terpugov (June 26-30, 2019). Vol. 1. Tomsk; 2019. P. 163–168 (in Russ.).
  47. Filippov S.V. Blender software platform as an environment for modeling objects and processes of science disciplines. KIAM Prepr. 2018(230):1–42. doi: 10.20948/prepr-2018-230
  48. Filippov S.V. Methods of working with dynamic molecular models, built in an environment of open 3D editor Blender. In: Proceedings of the International Conference “Mathematical Biology and Bioinformatics”. Ed. V.D. Lakhno. Vol. 7. Pushchino: IMPB RAS, 2018. Paper No. e43. doi: 10.17537/icmbb18.62
  49. Filippov S.V. Method for the identification of atoms of macromolecules visualized in 3D-editors. KIAM Prepr. 2019(97):1–10. doi: 10.20948/prepr-2019-97
  50. Blender 3.6 LTS: Simulation Nodes, better UV Packing, performance improvements, and much more!. https://www.blender.org/ (accessed: 30.06.2023).
  51. G’MIC - GREYC’s Magic for Image Computing: A Full-Featured Open-Source Framework for Image Processing. https://gmic.eu/ (accessed: 30.06.2023).

 

Table of Contents Original Article
Math. Biol. Bioinf.
2023;18(1):251-266
doi: 10.17537/2023.18.251
published in Russian

Abstract (rus.)
Abstract (eng.)
Full text (rus., pdf)
References

 

  Copyright IMPB RAS © 2005-2024