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
- 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
- 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
- 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
- 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
- Raymond D.M., Nilsson B.L. Multicomponent peptide assemblies. Chem. Soc. Rev. 2018;47(10):3659–3720. doi: 10.1039/C8CS00115D
- Scanlon S., Aggeli A. Self-assembling peptide nanotubes. Nano Today. 2008;3(3):22–30. doi: 10.1016/S1748-0132(08)70041-0
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- CCDC . https://www.ccdc.cam.ac.uk/ (accessed: 30.06.2023).
- 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
- 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
- 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
- Tao K., Makam P., Aizen R., Gazit E. Self-assembling peptide semiconductors. Science. 2017;358(6365):9756. doi: 10.1126/science.aam9756
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Bystrov V.S. Photoferroelectricity in di-phenylalanine peptide nanotubes. Computational Condensed Matter. 2018;14. doi: 10.1016/j.cocom.2017.11.007
- 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
- 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
- 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
- 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
- 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
- Novosibirsk State University Research Portal. https://research.nsu.ru/ru/publications/chirality-dependent-growth-of-self-assembled-diphenylalanine-micr (accessed: 30.06.2023).
- Lemak A.S., Balabaev N.K. A comparison between collisional dynamics and brownian dynamics: 4. Molecular Simulation. 1995;15(4). doi: 10.1080/08927029508022336
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- HyperChem. http://www.hypercubeusa.com/?tabid=360 (accessed: 30.06.2023).
- PyMOL by Schrödinger. https://pymol.org/2/ (accessed: 10.04.2023).
- 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
- 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.).
- 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
- 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
- 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
- Blender 3.6 LTS: Simulation Nodes, better UV Packing, performance improvements, and much more!. https://www.blender.org/ (accessed: 30.06.2023).
- G’MIC - GREYC’s Magic for Image Computing: A Full-Featured Open-Source Framework for Image Processing. https://gmic.eu/ (accessed: 30.06.2023).
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