Advertisement

Molecular modeling and computational study of the chiral-dependent structures and properties of self-assembling diphenylalanine peptide nanotubes

  • Vladimir S. BystrovEmail author
  • Pavel S. Zelenovskiy
  • Alla S. Nuraeva
  • Svitlana Kopyl
  • Olga A. Zhulyabina
  • Vsevolod A. Tverdislov
Original Paper
  • 54 Downloads

Abstract

The structure and properties of diphenylalanine (FF) peptide nanotubes (PNT) based on phenylalanine were investigated by various molecular modeling methods. The main approach employed semi-empirical quantum-chemical methods (PM3 and AM1). Ab initio, density functional theory methods and molecular mechanical approaches were also used. Both model structures and structures extracted from experimental crystallographic databases obtained by X-ray methods were examined. A comparison of optimized model structures and structures obtained by natural self-assembly revealed important differences depending on chirality: d and l. In both the cases, the effect of chirality on the results of self-assembly of FF PNT was established: PNT based on the d-FF has large condensation energy E0 in the transverse direction, and form thicker and shorter PNT bundles than those based on l-FF. A topological difference was established: model PNT were optimized into structures consisting of rings, while naturally self-assembled PNT consisted of helical turns. The latter nanotubes differed from the original l-FF and d-FF and formed helix structures of different chirality signs in accordance with the alternation rule of chirality due to macromolecule hierarchy. A topological transition between ring and helix turn PNT structures is discussed: self-assembled natural helix structures are favorable and their energy is lower by a value of the order of one to several eV.

Keywords

Diphenylalanine Peptide nanotube Molecular modeling Semi-empirical methods DFT Ab initio Molecular mechanics Topology Self-assembly Chirality 

Notes

Acknowledgments

The authors wish to acknowledge the Russian Foundation for Basic Research (RFBR grant 19-01-00519 А). P.Z and S.K. are grateful to FCT project PTDC/CTMCTM/31679/2017/ CENTRO-01-0145-FEDER-031679. Part of this work was funded by national funds (OE), through FCT in scope of the framework contract foreseen in numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19, and project CICECO-Aveiro Institue of Materials, FCT Ref. UID/CTM/50011/2019, financed by national funds through FCT/MCTES. Crystallographic data for D-FF nanotubes reported in the paper [72] have been deposited in the Cambridge Crystallographic Data Centre [73], no. CCDC 1853771.

Supplementary material

894_2019_4080_MOESM1_ESM.doc (423 kb)
ESM 1 (DOC 423 kb)

References

  1. 1.
    Calvin M (1969) Chemical evolution. Molecular evolution, towards the origin of living system on the Earth and elsewhere. Claredon, OxfordGoogle Scholar
  2. 2.
    Lehninger AL (1972) Biochemistry. The molecular basis of cell structure and function. Worth, New YorkGoogle Scholar
  3. 3.
    Pachahara SK, Subbalakshmi C, Nagaraj R (2017) Formation of nanostructures by peptides. Curr Protein Pept Sci 18(2):1–19Google Scholar
  4. 4.
    Aryaa SK, Solankia PR, Dattab M, Malhotra BD (2009) Recent advances in self- assembled monolayers based biomolecular electronic devices. J Biosensors Bioelectron 24(9):2810–2817CrossRefGoogle Scholar
  5. 5.
    Mendes AC, Baran ET, Reis RL, Azevedo HS (2013) Self-assembly in nature: using the principles of nature to create complex nanobiomaterials. Wiley Interdiscip Rev Nanomed Nanobiotechnol 5(6):582–612PubMedCrossRefGoogle Scholar
  6. 6.
    Orsi M (2018) Molecular simulation of self-assembly. In: Helena S. Azevedo and Ricardo M.P. da Silva (eds) Self-assembling biomaterials. 1st edn. Molecular design, characterization and application in biology and medicine. Elsevier, Amsterdam, pp 305–318CrossRefGoogle Scholar
  7. 7.
    Lee OS, Stupp SI, Schatz GC (2011) Atomistic molecular dynamics simulations of peptide amphiphile self-assembly into cylindrical nanofibers. J Am Chem Soc 133(10):3677–3683PubMedCrossRefGoogle Scholar
  8. 8.
    Frith WJ (2016) Self-assembly of small peptide amphiphiles, the structures formed and their applications. (A foods and home and personal care perspective). Philos Trans A 374(2072):20150138.  https://doi.org/10.1098/rsta.2015.0138 CrossRefGoogle Scholar
  9. 9.
    van der Lit J, Marsman JL, Koster RS, Jacobse PH, den Hartog SA, Vanmaekelbergh D, Klein Gebbink RJM, Filion L, Ingmar Swart I (2016) Modeling the self-assembly of organic molecules in 2D molecular layers with different structures. J Phys Chem C 120(1):318–323.  https://doi.org/10.1021/acs.jpcc.5b09889 CrossRefGoogle Scholar
  10. 10.
    Brandon CJ, Martin BP, McGee KJ, Stewart JJP, Braun-Sand SB (2015) An approach to creating a more realistic working model from a protein data bank entry. J Mol Modeling 21(1):11CrossRefGoogle Scholar
  11. 11.
    Ryan H, Carter M, Stenmark P, Stewart JJ, Braun-Sand SB (2016) A comparison of X-ray and calculated structures of the enzyme MTH1. J Mol Mod 22(7):1–18CrossRefGoogle Scholar
  12. 12.
    Eliel EL, Wilen S, Doyle SM (2001) Basic organic stereochemistry. Wiley-Interscience, New YorkGoogle Scholar
  13. 13.
    IUPAC. Compendium of chemical terminology (1997) 2nd edn. (The "gold book") compiled by A. D. McNaught and A. Wilkinson. Blackwell, OxfordGoogle Scholar
  14. 14.
    Mason SF (1984) Origins of biomolecular handedness. Nature 311:19–23PubMedCrossRefGoogle Scholar
  15. 15.
    Holmstedt B, Frank H, Testa B (eds) (1990) Chirality and biological activity. Liss, New YorkGoogle Scholar
  16. 16.
    Capito RM, Azevedo HS, Velichko YS, Mata A, Stupp SI (2008) Self-assembly of large and small molecules into hierarchically ordered sacs and membranes. Science 19(5871):1812–1816CrossRefGoogle Scholar
  17. 17.
    Yashima E, Ousaka N, Taura D, Shimomura K, Ikai T, Maeda K (2016) Supramolecular helical systems: helical assemblies of small molecules, foldamers, and polymers with chiral amplification and their functions. Chem Rev 116:13752PubMedCrossRefGoogle Scholar
  18. 18.
    Tverdislov VA (2013) Chirality as a primary switch of hierarchical levels in molecular biological systems. Biophysics 58(1):128–132CrossRefGoogle Scholar
  19. 19.
    Malyshko EV, Tverdislov VA (2016) IOP. J Phys Conf Ser 741:012065CrossRefGoogle Scholar
  20. 20.
    Tverdislov VA, Malyshko EV, Il’chenko SA, Zhulyabina OA, Yakovenko LV (2017) A periodic system of chiral structures in molecular biology. Biophysics 62(3):331–341.  https://doi.org/10.1134/S0006350917030228 CrossRefGoogle Scholar
  21. 21.
    Ghadiri MR, Granja JR, Milligan RA, McRee DE, Hazanovich N (1993) Self assembling organic nanotubes based on a cyclic peptide architecture. Nature 366:324–327PubMedCrossRefGoogle Scholar
  22. 22.
    Gorbitz CH (2001) Nanotube formation by hydrophobic dipeptides. Chem Eur J 7:5153–5159PubMedCrossRefGoogle Scholar
  23. 23.
    Sedman VL, Adler-Abramovich L, Allen S, Gazit E, Tendler SJB (2006) Direct observation of the release of phenylalanine from diphenilalanine nanotubes. J Am Chem Soc 128:6903–6908PubMedCrossRefGoogle Scholar
  24. 24.
    Scanlon S, Aggeli A (2008) Self-assembling peptide nanotubes. Nano Today 3:22–30CrossRefGoogle Scholar
  25. 25.
    Shklovsky J, Beker P, Amdursky N, Gazit E, Rosenman G (2010) Bioinspired peptide nanotubes: deposition technology and physical properties. Mater Sci Eng B169:62–66CrossRefGoogle Scholar
  26. 26.
    Bystrov VS, Bdikin I, Heredia A, Pullar RC, Mishina E, Sigov A, Kholkin AL (2012) Piezoelectricity and ferroelectricity in biomaterials: from proteins to self-assembled peptide nanotubes. In: Ciofani G, Menciassi A (eds) Piezoelectric nanomaterials for biomedical applications. Springer, Berlin, pp 187–211CrossRefGoogle Scholar
  27. 27.
    Bystrov VS, Seyedhosseini E, Kopyl S, Bdikin IK, Kholkin AL (2014) Piezoelectricity and ferroelectricity in biomaterials: molecular modeling and piezoresponse force microscopy measurements. J Appl Phys 116(6):066803.  https://doi.org/10.1063/1.4891443 CrossRefGoogle Scholar
  28. 28.
    Bystrov VS (2016) Computer simulation nanostructures: bioferroelectric peptide nanotubes. LAP Lambert academic press, Saarbrucken ISBN 978–3–659-92397-5Google Scholar
  29. 29.
    Bystrov VS, Paramonova EV, Bdikin IK, Kopyl S, Heredia A, Pullar RC, Kholkin AL (2012) Bioferroelectricity: diphenylalanine peptide nanotubes computational modeling and ferroelectric properties at the nanoscale. Ferroelectrics 440(1):3–24CrossRefGoogle Scholar
  30. 30.
    Bystrov VS, Kopyl SA, Zelenovskiy P, Zhulyabina OA, Tverdislov VA, Salehli F, Ghermani NE, Vya S, Kholkin AL (2018) Investigation of physical properties of diphenylalanine peptide nanotubes having different chiralities and embedded water molecules. Ferroelectrics 525:168–177.  https://doi.org/10.1080/00150193.2018.14328 CrossRefGoogle Scholar
  31. 31.
    Kholkin A, Amdursky N, Bdikin I, Gazit E, Rosenman G (2010) Strong piezoelectricity in bioinspired peptide nanotubes. ACS Nano 4:610–614PubMedCrossRefGoogle Scholar
  32. 32.
    Hereida A, Bdikin I, Kopyl S, Mishina E, Semin S, Sigov A, German K, Bystrov V, Gracio J, Kholkin AL (2013) Temperature-driven phase transformation in self-assembled diphenylalanine peptide nanotubes. J Phys D Appl Phys 43:462001CrossRefGoogle Scholar
  33. 33.
    Nguyen V, Zhu R, Jenkins K, Yang R (2016) Self-assembly of diphenylalanine peptide with controlled polarization for power generation. Nat Commun 7:13566PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Zelenovskiy P, Kornev I, Vasilev S, Kholkin A (2016) On the origin of the great rigidity of self-assembled diphenylalanine nanotubes. Phys Chem Chem Phys 18(43):29681–29685PubMedCrossRefGoogle Scholar
  35. 35.
    Zelenovskiy PS, Davydov AO, Krylov AS, Kholkin AL (2017) Raman study of structural transformations in self-assembled dipheny lalanine nanotubes at elevated temperatures. J Raman Spectrosc 48(11):1401–1405CrossRefGoogle Scholar
  36. 36.
    Bdikin I, Bystrov VS, Delgadillo I, Gracio J, Kopyl S, Wojtas M, Mishina E, Sigov A, Kholkkin AL (2012) Polarization switching and patterning in self-assembled peptide tubular structures. J Appl Phys 111:074104CrossRefGoogle Scholar
  37. 37.
    Bystrov VS (2018) Photoferroelectricity in di-phenylalanine peptide nanotubes. Comput Condensed Matter 14:94–100. 7.  https://doi.org/10.1016/j.cocom.2017.11.00 CrossRefGoogle Scholar
  38. 38.
    Zelenovskiy PS, Vya S, Nuraeva AS, Vasilev SG, Vasileva DS, Alikin DO, Chezganov DS, Krasnov VP, Kholkin AL (2015) Morphology and piezoelectric properties of diphenylalanine microcrystals grown from methanol-water solution. Ferroelectrics 475:127–134CrossRefGoogle Scholar
  39. 39.
    Nuraeva A, Vasilev S, Vasileva D, Zelenovskiy P, Chezganov D, Esin A, Kopyl S, Romanyuk K, VYa S, Kholkin AL (2016) Evaporation-driven crystallization of diphenylalanine microtubes for microelectronic applications. Cryst Growth Des 16:1472–1479CrossRefGoogle Scholar
  40. 40.
    Müller U (2013) Symmetry relationships between crystal structures. Applications of crystallographic group theory in crystal chemistry. Oxford University Press, OxfordCrossRefGoogle Scholar
  41. 41.
    Hypercube Inc (2002) HyperChem (versions 7.51 and 8.0). Hypercube Inc., Gainesville. http://www.hyper.com/?tabid=360 (accessible March 2018–January 2019)
  42. 42.
    Stewart JJP (1989) Optimization of Parameters for Semiempirical Methods. I. Method. J Comput Chem 10:209CrossRefGoogle Scholar
  43. 43.
    Stewart JJP (1989) Optimization of parameters for semiempirical methods. II. Applications. J Comput Chem 10:221CrossRefGoogle Scholar
  44. 44.
    Stewart JJP (2007) Optimization of parameters for semiempirical methods V: modification of NDDO approximations and application to 70 elements. J Mol Mod 13(12):1173–1213CrossRefGoogle Scholar
  45. 45.
    Szabo A, Ostlund N (1985) Modern quantum chemistry. Macmillan, New YorkGoogle Scholar
  46. 46.
    Clark TA (1985) Handbook of computational chemistry. Wiley, New YorkGoogle Scholar
  47. 47.
    Kohn W, Sham LJ (19655) Self-consistent equations including exchange and correlation effects. Phys Rev 140:A1133Google Scholar
  48. 48.
    Kresse G, Hafner J (1994) Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys Rev B 49:14251–14269CrossRefGoogle Scholar
  49. 49.
    Kresse G, Furthmüller J (1996) Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B 54:11169–11186CrossRefGoogle Scholar
  50. 50.
    Kresse G, Joubert D (1999) From ultrasoft pseudopotentials to the projector augmented wave method. Phys Rev B 59:1758–1775CrossRefGoogle Scholar
  51. 51.
    Perdew JP, Burke K, Ernzerhof M (1996) Generalized gradient approximation made simple. Phys Rev Lett 77:3865–3868CrossRefGoogle Scholar
  52. 52.
    Lee C, Yang W, Parr RG (1988) Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys Rev B 37:785–789CrossRefGoogle Scholar
  53. 53.
    Becke AD (1993) A new mixing of Hartree-Fock and local density-functional theories. J Chem Phys 98:1372–1377CrossRefGoogle Scholar
  54. 54.
    Pople JA, Beveridge DL (1970) Approximate molecular orbital theory. McGraw-Hill, New YorkGoogle Scholar
  55. 55.
    Krishnan R, Kinkley JS, Seeger R, Pople JA (1980) Self-consistent molecular orbital methods. XX. A basis set for correlated wave functions. J Chem Phys 72:650–654CrossRefGoogle Scholar
  56. 56.
    McLean AD, Chandler GS (1980) Contracted Gaussian basis sets for molecular calculations. I. Second row atoms, Z=11–18. J Chem Phys 72:5639–5648CrossRefGoogle Scholar
  57. 57.
    Møller C, Plesset M (1934) Note on an approximation treatment for many-Electron systems. Phys Rev 46(7):618–622CrossRefGoogle Scholar
  58. 58.
    Head-Gordon M, Pople JA, Frisch MJ (1988) MP2 energy evaluation by direct methods. Chem Phys Lett 153(6):503–506CrossRefGoogle Scholar
  59. 59.
    Hamprecht FA, Cohen AJ, Tozer DJ, Handy NC (1998) Development and assessment of new exchange-correlation functionals. J Chem Phys 109:6264CrossRefGoogle Scholar
  60. 60.
    Bystrov VS, Paramonova EV, Dekhtyar Y, Pullar RC, Katashev A, Polyaka N, Bystrova AV, Sapronova AV, Fridkin VM, Kliem H, Kholkin AL (2012) Polarization of poly(vinylidene fluoride) and poly(vinylidene fluoridetrifluoroethylene) thin films revealed by emission spectroscopy with computational simulation during phase transition. J Appl Phys 111:104113.  https://doi.org/10.1063/1.4721373 CrossRefGoogle Scholar
  61. 61.
    Bystrov VS, Paramonova EV, Bdikin IK, Bystrova AV, Pullar RC, Kholkin AL (2013) Molecular modeling of the piezoelectric effectin the ferroelectric polymer poly(vinylidene fluoride) (PVDF). J Mol Model 19:3591–3602.  https://doi.org/10.1007/s00894-013-1891-z PubMedCrossRefGoogle Scholar
  62. 62.
    Bystrov VS, Bdikin IK, Silibin M, Karpinsky D, Kopyl S, Paramonova EV, Goncalves G (2017) Molecular modeling of the piezoelectric properties in the graphene/graphene oxide and polyvinylidene fluoride (PVDF) polymer ferroelectric composites. J Mol Mod 23:128.  https://doi.org/10.1007/s00894-017-3291-2 CrossRefGoogle Scholar
  63. 63.
    Murrell JN, Harget AJ (1971) Semi-empirical self-consistent-field molecular orbital theory of molecules. Wiley Interscience, New YorkGoogle Scholar
  64. 64.
    Andrade-Filho T, Martins TC, Ferreira FF, Alves WA, Rocha AR (2016) Water-driven stabilization of diphenylalanine nanotube structures. Theor Chem Accounts 135(8):185.  https://doi.org/10.1007/s00214-016-1936-3 CrossRefGoogle Scholar
  65. 65.
    Dewar MJS, Thiel W (1977) The MNDO method. Approximations and parameters. J Am Chem Soc 99:4899–4906CrossRefGoogle Scholar
  66. 66.
    Dewar MJS, Zoebisch EG, Healy EF, Stewart JJP (1985) A new general purpose quantum mechanical molecular model. J Am Chem Soc 107:3902–3909CrossRefGoogle Scholar
  67. 67.
    Klein E, Matis M, Lukes V, Cibulkova Z (2006) The applicability of AM1 and PM3 semi- empirical methods for the study of NeH bond dissociation enthalpies and ionisation potentials of amine type antioxidants. Polym Degrad Stab 91:262–270CrossRefGoogle Scholar
  68. 68.
    Allinger NL (1977) Conformational analysis. 130. MM2. A hydrocarbon force field utilizing V1 and V2 torsional terms. J Am Chem Soc 99:8127CrossRefGoogle Scholar
  69. 69.
    Weiner SJ, Kollman PA, Case DA, Singh UC, Ghio C, Alagona G, Profeta Jr S, Weiner P (1984) A new force field for molecular mechanical simulation of nucleic acids and proteins. J Am Chem Soc 106:765–784CrossRefGoogle Scholar
  70. 70.
    Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz Jr KM, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1995) A second generation force field for the simulation of proteins and nucleic acids. J Am Chem Soc 117:5179–5197CrossRefGoogle Scholar
  71. 71.
    Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4:187–217CrossRefGoogle Scholar
  72. 72.
    Zelenovskiy PS, Nuraeva AS, Kopyl S, Arkhipov SG, Vasilev SG, Bystrov VS, Svitlyk V, Shur VYa, Mafra L, Kholkin AL (2019) Chirality-dependent growth of self-assembled diphenylalanine microtubes. Cryst Growth Des (in press)Google Scholar
  73. 73.
    The Cambridge Crystallographic Data Centre (CCDC). https://www.ccdc.cam.ac.uk/ (accessed July 2018–January 2019)
  74. 74.
    Yin P, Zhang Z-M, Lv H, Li T, Haso F, Hu L, Zhang B, Bacsa J, Wei Y, Gao Y, Hou Y, Li Y-G, Hill CL, Wang E-B, Liu T (2015) Chiral recognition and selection during the self-assembly process of protein-mimic macroanions. Nature Comm 6:6475.  https://doi.org/10.1038/ncomms7475 CrossRefGoogle Scholar
  75. 75.
    Ilawe NV, Schweitzer-Stenner R, DiGuiseppi D, Wong BM (2018) Is a cross-b-sheet structure of low molecular weight peptides necessary for the formation of fibrils and peptide hydrogels? Phys Chem Chem Phys 20:18158–18168PubMedCrossRefGoogle Scholar
  76. 76.
    Ilawe NV, Raeber AE, Schweitzer-Stenner R, Toal SE, Wong BM (2015) Assessing backbone solvation effects in the conformational propensities of amino acid residues in unfolded peptides. Phys Chem Chem Phys 17:24917–24924PubMedCrossRefGoogle Scholar
  77. 77.
    Tamamis P, Adler-Abramovich L, Reches M, Marshall K, Sikorski P, Serpell L, Gazit E, Archontis G (2009) Self-assembly of phenylalanine oligopeptides: insights from experiments and simulations. Biophys J 96:5020–5029PubMedPubMedCentralCrossRefGoogle Scholar
  78. 78.
    Villa A, van der Vegt NFA, Pete C (2009) Self-assembling dipeptides: including solvent degrees of freedom in a coarse-grained model. Phys Chem Chem Phys 11:2068–2076PubMedCrossRefGoogle Scholar
  79. 79.
    Rissanou AN, Georgilis E, Kasotakis E, Mitraki A, Yarmandaris V (2013) Effect of solvent on the self-assembly of Dialanine and diphenylalanine peptides. J Phys Chem B 117(15):3962–3975PubMedCrossRefGoogle Scholar
  80. 80.
    Flack HD, Bernardinelli G (2008) The use of X-ray crystallography to determine absolute configuration. Chirality 20:681–690PubMedCrossRefGoogle Scholar
  81. 81.
    Flack HD (2008) The use of X-ray crystallography to determine absolute configuration (II). Acta Chim Slov 55:689–691Google Scholar
  82. 82.
    Hahn T (ed) (2005) International tables for crystallography. Volume A. Springer, NetherlandsGoogle Scholar
  83. 83.
  84. 84.
    Lennard-Jones JE (1924) On the determination of molecular fields. Proc R Soc Lond A 106(738):463–477CrossRefGoogle Scholar
  85. 85.
    Hadzibabic Z, Kruger P, Cheneau M, Battelier B, Dalibard J (2006) Berezinskii-Kosterlitz-Thouless crossover in a trapped atomic gas. Nature 441:1118–1121PubMedCrossRefGoogle Scholar
  86. 86.
    Nguyen V, Jenkins K, Yang R (2015) Epitaxial growth of vertically aligned piezoelectric diphenylalanine peptide microrods with uniform polarization. Nano Energy 17:323–329CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Mathematical Problems of BiologyKeldysh Institute of Applied Mathematics, RASPushchinoRussia
  2. 2.School of Natural Sciences and MathematicsUral Federal UniversityEkaterinburgRussia
  3. 3.Department of Chemistry and CICECO-Aveiro Institute of MaterialsUniversity of AveiroAveiroPortugal
  4. 4.Department of Physics and CICECO-Aveiro Institute of MaterialsUniversity of AveiroAveiroPortugal
  5. 5.Faculty of PhysicsLomonosov Moscow State UniversityMoscowRussia

Personalised recommendations