Multiscale Modelling of Bionano Interface

  • Hender Lopez
  • Erik G. Brandt
  • Alexander Mirzoev
  • Dmitry Zhurkin
  • Alexander Lyubartsev
  • Vladimir LobaskinEmail author
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 947)


We present a framework for coarse-grained modelling of the interface between foreign nanoparticles (NP) and biological fluids and membranes. Our model includes united-atom presentations of membrane lipids and globular proteins in implicit solvent, which are based on all-atom structures of the corresponding molecules and parameterised using experimental data or atomistic simulation results. The NPs are modelled by homogeneous spheres that interact with the beads of biomolecules via a central force that depends on the NP size. The proposed methodology is used to predict the adsorption energies for human blood plasma proteins on NPs of different sizes as well as the preferred orientation of the molecules upon adsorption. Our approach allows one to rank the proteins by their binding affinity to the NP, which can be used for predicting the composition of the NP-protein corona for the corresponding material. We also show how the model can be used for studying NP interaction with a lipid bilayer membrane and thus can provide a mechanistic insight for modelling NP toxicity.


Nanoparticle Toxicity Coarse-grained molecular dynamics Protein corona Cell membrane 


  1. 1.
    Sharifi S, Behzadi S, Laurent S, Forrest ML, Stroevee P, Mahmoudi M (2012) Toxicity of nanomaterials. Chem Soc Rev 41:2323CrossRefPubMedGoogle Scholar
  2. 2.
    Borm PJA et al (2006) The potential risks of nanomaterials. Part Fibre Toxicol 3:11CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Johnston HJ, Hutchison GR, Christensen FM et al (2009) Identification of the mechanisms that drive the toxicity of tio2 particulates: the contribution of physicochemical characteristics. Part Fibre Toxicol 6:33CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Nel AE, Maedler L, Velegol D et al (2009) Understanding biophysicochemical interactions at the nano-bio interface. Nat Mater 8:543CrossRefPubMedGoogle Scholar
  5. 5.
    Verma A, Uzun O, Hu Y et al (2008) Surface-structure-regulated cell-membrane penetration by monolayer-protected nanoparticles. Nat Mater 7:588CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Schlick T, Collepardo-Guevara R, Halvorsen LA et al (2011) Biomolecular modeling and simulation: a field coming of age. Q Rev Biol 44:191Google Scholar
  7. 7.
    Valerio LG Jr (2009) In silico toxicology for the pharmaceutical sciences. Toxicol Appl Pharmacol 241:356CrossRefPubMedGoogle Scholar
  8. 8.
    Nigsch F, Macaluso NJ, Mitchell JB, Zmuidinavicius D (2009) Computational toxicology: an overview of the sources of data and of modelling methods. Expert Opin Drug Metab Toxicol 5:1CrossRefPubMedGoogle Scholar
  9. 9.
    Dearden JC (2003) In silico prediction of drug toxicity. J Comput Aided Mol Des 17:119CrossRefPubMedGoogle Scholar
  10. 10.
    Lyubartsev AP, Rabinovich AL (2011) Recent development in computer simulations of lipid bilayers. Soft Matter 7:25CrossRefGoogle Scholar
  11. 11.
    Wong-Ekkabut J, Baoukina S, Triampo W, Tang I-M, Tieleman DP (2008) Computer simulation study of fullerene translocation through lipid membranes. Nat Nanotechnol 3:363CrossRefPubMedGoogle Scholar
  12. 12.
    Hou WC, Moghadam BY, Westerhoff P, Posner JD (2011) Distribution of fullerene nanomaterials between water and model biological membranes. Langmuir 27:11899CrossRefPubMedGoogle Scholar
  13. 13.
    Yang K, Ma YQ (2010) Computer simulation of the translocation of nanoparticles with different shapes across a lipid bilayer. Nat Nanotechnol 5:579CrossRefPubMedGoogle Scholar
  14. 14.
    Monticelli L, Salonen E, Ke PC, Vattulainen I (2009) Effects of carbon nanoparticles on lipid membranes: a molecular simulation perspective. Soft Matter 5:4433CrossRefGoogle Scholar
  15. 15.
    Izvekov S, Voth GA (2005) Multiscale coarse-graining method for biomolecular systems. J Phys Chem B 109:2469CrossRefPubMedGoogle Scholar
  16. 16.
    Ayton GS, Noid WG, Voth GA (2007) Multiscale modeling of biomolecular systems: in serial and in parallel. Curr Opin Struct Biol 17:192CrossRefPubMedGoogle Scholar
  17. 17.
    Lyubartsev AP, Laaksonen A (1995) Calculation of effective interaction potentials from radial distribution functions: a reverse monte carlo approach. Phys Rev E 52:3730CrossRefGoogle Scholar
  18. 18.
    Lyubartsev AP, Mirzoev A, Chen L-J, Laaksonen A (2010) Systematic coarse-graining of molecular models by the newton inversion method. Faraday Discuss 144:43CrossRefPubMedGoogle Scholar
  19. 19.
    Lyubartsev AP, Laaksonen A (1999) Effective potentials for ion-DNA interactions. J Chem Phys 111:11207Google Scholar
  20. 20.
    Tozzini V (2005) Coarse-grained models for proteins. Curr Opin Struct Biol 15(2):144–150CrossRefPubMedGoogle Scholar
  21. 21.
    Bereau T, Deserno M (2009) Generic coarse-grained model for protein folding and aggregation. J Chem Phys 130(23):235106CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Takada S (2012) Coarse-grained molecular simulations of large biomolecules. Curr Opin Struct Biol 22(2):130–137CrossRefPubMedGoogle Scholar
  23. 23.
    Wei S, Knotts T (2013) A coarse grain model for protein-surface interactions. J Chem Phys 139(9):095102CrossRefPubMedGoogle Scholar
  24. 24.
    Lobaskin V, Lyubartsev AP, Linse P (2001) Effective macroion-macroion potentials in asymmetric electrolytes. Phys Rev E 63:020401CrossRefGoogle Scholar
  25. 25.
    Brunner M, Bechinger C, Strepp W, Lobaskin V, von Gruenberg HH (2002) Density-dependent pair interactions in 2D colloidal dispersions. Europhys Lett 58:926Google Scholar
  26. 26.
    Lynch I, Salvati A, Dawson KA (2009) Protein-nanoparticle interactions. What does the cell see? Nat Nanotechnol 4:546Google Scholar
  27. 27.
    Lynch I, Dawson KA, Linse S (2006) Detecting cryptic epitopes created by nanoparticles. Sci STKE 2006:14Google Scholar
  28. 28.
    Cedervall T et al (2007) Understanding the nanoparticle protein corona using methods to quantify exchange rates and affinities of proteins for nanoparticles. Proc Natl Acad Sci U S A 104:2050CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Lindman S et al (2007) Systematic investigation of the thermodynamics of HSA adsorption to n-iso-propylacrylamide/n-tert-butylacrylamide copolymer nanoparticles. effects of particle size and hydrophobicity. Nanoletters 7:914Google Scholar
  30. 30.
    Allen LT et al (2006) Surface-induced changes in protein adsorption and implications for cellular phenotypic responses to surface interaction. Biomaterials 27:3096CrossRefPubMedGoogle Scholar
  31. 31.
    Radke CE, Prausnitz JM (1972) Thermodynamics of multisolute adsorption from dilute liquid solutions. AIChE J 18:761CrossRefGoogle Scholar
  32. 32.
    Lesniak A, Campbell A, Monopoli MP, Lynch I, Salvati A, Dawson KA (2010) Serum heat inactivation affects protein corona composition and nanoparticle uptake. Biomaterials 31:9511CrossRefPubMedGoogle Scholar
  33. 33.
    Kamath P, Fernandez A, Giralt F, Rallo R (2015) Predicting cell association of surface-modified nanoparticles using protein corona structure – activity relationships (PCSAR). Curr Top Med Chem 15(18):1930–1937Google Scholar
  34. 34.
    Noid WG (2013) Perspective: Coarse-grained models for biomolecular systems. J Chem Phys 139(9):090901CrossRefPubMedGoogle Scholar
  35. 35.
    Lopez H, Lobaskin V (2015) Coarse-grained model of adsorption of blood plasma proteins onto nanoparticles. J Chem Phys 143:243138CrossRefPubMedGoogle Scholar
  36. 36.
    Miyazawa S, Jernigan RL (1996) Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J Mol Biol 256(3):623–644CrossRefPubMedGoogle Scholar
  37. 37.
    Kim Y, Tang C, Clore G, Hummer G (2008) Replica exchange simulations of transient encounter complexes in protein-protein association. Proc Natl Acad Sci U S A 105(35):12855–12860CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Kim Y, Hummer G (2008) Coarse-grained models for simulations of multiprotein complexes: application to ubiquitin binding. J Mol Biol 375(5):1416–1433CrossRefPubMedGoogle Scholar
  39. 39.
    Agashe M, Raut V, Stuart S, Latour R (2005) Molecular simulation to characterize the adsorption behavior of a Fibrinogen γ-chain fragment. Langmuir 21(3):1103–1117Google Scholar
  40. 40.
    Sun Y, Welsh W, Latour R (2005) Prediction of the orientations of adsorbed protein using an empirical energy function with implicit solvation. Langmuir 21(12):5616–5626CrossRefPubMedGoogle Scholar
  41. 41.
    Kokh D, Corni S, Winn P, Hoefling M, Gottschalk K, Wade R (2010) Prometcs: An atomistic force field for modeling proteinmetal surface interactions in a continuum aqueous solvent. J Chem Theory Comput 6(5):1753–1768CrossRefPubMedGoogle Scholar
  42. 42.
    Limbach H, Arnold A, Mann B, Holm C (2006) ESPResSo – an extensible simulation package for research on soft matter systems. Comput Phys Commun 174(9):704–727CrossRefGoogle Scholar
  43. 43.
    Chen W, Huang H, Lin C, Lin F, Chan Y (2003) Effect of temperature on hydrophobic interaction between proteins and hydrophobic adsorbents: studies by isothermal titration calorimetry and the van’t Hoff equation. Langmuir 19(22):9395–9403Google Scholar
  44. 44.
    Lacerda S, Park JJ, Meuse C, Pristinski D, Becker M, Karim A, Douglas J (2010) Interaction of gold nanoparticles with common human blood proteins. ACS Nano 4(1):365–379CrossRefPubMedGoogle Scholar
  45. 45.
    Vilaseca P, Dawson K, Franzese G (2013) Understanding and modulating the competitive surface-adsorption of proteins through coarse-grained molecular dynamics simulations. Soft Matter 9:6978–6985CrossRefGoogle Scholar
  46. 46.
    Vijay-Kumar S, Bugg C, Cook W (1987) Structure of ubiquitin refined at 1.8 resolution. J Mol Biol 194:531–544CrossRefPubMedGoogle Scholar
  47. 47.
    Momma K, Izumi F (2011) VESTA3 for three-dimensional visualization of crystal, volumetric and morphology data. J Appl Cryst 44:1272–1276CrossRefGoogle Scholar
  48. 48.
    Brandt EG, Lyubartsev A (2015) Systematic optimization of a force field for classical simulations of TiO2-water interfaces. J Phys Chem C 119:18110–18125Google Scholar
  49. 49.
    Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690CrossRefGoogle Scholar
  50. 50.
    Hoover W (1985) Canonical dynamics: equilibrium phase-space distributions. Phys Rev A 31:1695–1697CrossRefGoogle Scholar
  51. 51.
    Nose S (1984) A unified formulation of the constant temperature molecular dynamics methods. J Chem Phys 81:511–519CrossRefGoogle Scholar
  52. 52.
    Jämbeck JPM, Lyubartsev AP (2012) Derivation and systematic validation of a refined all-atom force field for phosphatidylcholine lipids. J Phys Chem B 116(10):3164–3179Google Scholar
  53. 53.
    Jämbeck JPM, Lyubartsev AP (2013) Another piece of the membrane puzzle: extending Slipids further. J Chem Theory Comput 9(1):774–784Google Scholar
  54. 54.
    Mirzoev A, Lyubartsev AP (2013) MagiC: software package for multiscale modeling. J Chem Theory Comput 9(3):1512–1520Google Scholar
  55. 55.
    Qin S-S, Yu ZW, Yu Y-X (2009) Structural characterization on the gel to liquid-crystal phase transition of fully hydrated DSPC and DSPE bilayers. J Phys Chem B 113:8114–8123CrossRefPubMedGoogle Scholar
  56. 56.
    Kučerka N, Liu Y, Chu N, Petrache HI, Tristram-Nagle S, Nagle JF (2005) Structure of fully hydrated fluid phase DMPC and DLPC lipid bilayers using X-ray scattering from oriented multilamellar arrays and from unilamellar vesicles. Biophys J 88:2626–2637CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Koenig BW, Strey HH, Gawrisch K (1997) Membrane lateral compressibility determined by NMR and X-ray diffraction: effect of acyl chain polyunsaturation. Biophys J 73(4):1954–1966Google Scholar
  58. 58.
    Kučerka N, Nagle JF, Sachs JN, Feller SE, Pencer J, Jackson A, Katsaras J (2008) Lipid bilayer structure determined by the simultaneous analysis of neutron and X-Ray scattering data. Biophys J 95(5):2356–2367CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Bereau T, Wang Z-J, Deserno M (2014) More than the sum of its parts: Coarse-grained peptide-lipid interactions from a simple cross-parametrization. J Chem Phys 140(11):115101CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Lin J, Zhang H, Chen Z, Zheng Y (2010) Penetration of lipid membranes by gold nanoparticles: Insights into cellular uptake, cytotoxicity, and their relationship. ACS Nano 4(9):5421–5429CrossRefPubMedGoogle Scholar
  61. 61.
    Hong-Ming D, Yu-Qiang M (2014) Computer simulation of the role of protein corona in cellular delivery of nanoparticles. Biomaterials 35(30):8703–8710CrossRefGoogle Scholar
  62. 62.
    Monopoli M, Aberg C, Salvati A, Dawson KA (2012) Biomolecular coronas provide the biological identity of nanosized materials. Nat Nanotechnol 7(12):779–786CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hender Lopez
    • 1
  • Erik G. Brandt
    • 2
  • Alexander Mirzoev
    • 2
  • Dmitry Zhurkin
    • 2
  • Alexander Lyubartsev
    • 2
  • Vladimir Lobaskin
    • 1
    Email author
  1. 1.School of Physics, Complex and Adaptive Systems LabUniversity College DublinDublin 4Ireland
  2. 2.Department of Materials and Environmental ChemistryStockholm UniversityStockholmSweden

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