First principles characterisation of bio-nano interface.

Nanomaterials possess a wide range of potential applications due to their novel properties and exceptionally high activity as a result of their large surface to volume ratios compared to bulk matter. The active surface may present both advantage and risk when the nanomaterials interact with living organisms. As the overall biological impact of nanomaterials is triggered and mediated by interactions at the bio-nano interface, an ability to predict those from the atomistic descriptors, especially before the material is produced, can present enormous advantage for the development of nanotechnology. Fast screening of nanomaterials and their variations for specific biological effects can be enabled using computational materials modelling. The challenge lies in the range of scales that needs to be crossed from the material-specific atomistic representation to the relevant length scales covering typical biomolecules (proteins and lipids). In this work, we present a systematic multiscale approach that allows one to evaluate crucial interactions at the bionano interface from the first principles without any prior information about the material and thus establish links between the details of the nanomaterials structure to protein-nanoparticle interactions. As an example, an advanced computational characterization of titanium dioxide nanoparticles (6 different surfaces of rutile and anatase polymorphs) has been performed. We computed characteristics of the titanium dioxide interface with water using density functional theory for electronic density, used these parameters to derive an atomistic force field, and calculated adsorption energies for essential biomolecules on the surface of titania nanoparticles via direct atomistic simulations and coarse-grained molecular dynamics. Hydration energies, as well as adsorption energies for a set of 40 blood proteins are reported.

[1]  Ian Rouse,et al.  In Silico Prediction of Protein Adsorption Energy on Titanium Dioxide and Gold Nanoparticles , 2020, Nanomaterials.

[2]  Hwankyu Lee Effects of Nanoparticle Electrostatics and Protein-Protein Interactions on Corona Formation: Conformation and Hydrodynamics. , 2020, Small.

[3]  Nicklas Raun Jacobsen,et al.  Effects of physicochemical properties of TiO2 nanomaterials for pulmonary inflammation, acute phase response and alveolar proteinosis in intratracheally exposed mice. , 2019, Toxicology and applied pharmacology.

[4]  Alexander P. Lyubartsev,et al.  A multiscale model of protein adsorption on a nanoparticle surface , 2019, Modelling and Simulation in Materials Science and Engineering.

[5]  M. Předota,et al.  Modeling of solid-liquid interfaces using scaled charges: rutile (110) surfaces. , 2018, Physical chemistry chemical physics : PCCP.

[6]  M. Schneemilch,et al.  Free energy of adhesion of lipid bilayers on silica surfaces. , 2018, The Journal of chemical physics.

[7]  Thomas A. Manz,et al.  Introducing DDEC6 atomic population analysis: part 3. Comprehensive method to compute bond orders , 2017 .

[8]  Erik G. Brandt,et al.  Diffusion and reaction pathways of water near fully hydrated TiO2 surfaces from ab initio molecular dynamics. , 2017, The Journal of chemical physics.

[9]  Niall J. English,et al.  Exploring Rutile (110) and Anatase (101) TiO2 Water Interfaces by Reactive Force-Field Simulations , 2017 .

[10]  R. Zhou,et al.  An In Silico study of TiO2 nanoparticles interaction with twenty standard amino acids in aqueous solution , 2016, Scientific Reports.

[11]  M. Schneemilch,et al.  Free energy of adsorption of supported lipid bilayers from molecular dynamics simulation , 2016 .

[12]  L Mädler,et al.  Parametrization of nanoparticles: development of full-particle nanodescriptors. , 2016, Nanoscale.

[13]  T. Gould How polarizabilities and C6 coefficients actually vary with atomic volume. , 2016, The Journal of chemical physics.

[14]  A. Nelson,et al.  Significance of particle size and charge capacity in TiO2 nanoparticle-lipid interactions. , 2016, Journal of colloid and interface science.

[15]  Thomas A. Manz,et al.  Introducing DDEC6 atomic population analysis: part 1. Charge partitioning theory and methodology , 2016 .

[16]  T. Bučko,et al.  C6 Coefficients and Dipole Polarizabilities for All Atoms and Many Ions in Rows 1-6 of the Periodic Table. , 2016, Journal of chemical theory and computation.

[17]  Iseult Lynch,et al.  How safe are nanomaterials? , 2015, Science.

[18]  Alberto Fernandez,et al.  Predicting Cell Association of Surface-Modified Nanoparticles Using Protein Corona Structure - Activity Relationships (PCSAR). , 2015, Current topics in medicinal chemistry.

[19]  Vladimir Lobaskin,et al.  Coarse-grained model of adsorption of blood plasma proteins onto nanoparticles. , 2015, The Journal of chemical physics.

[20]  Alexander P. Lyubartsev,et al.  Systematic Optimization of a Force Field for Classical Simulations of TiO2-Water Interfaces , 2015 .

[21]  Jerzy Leszczynski,et al.  Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: Hints from nano-QSAR studies , 2015, Nanotoxicology.

[22]  Jerzy Leszczynski,et al.  Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach. , 2014, Ecotoxicology and environmental safety.

[23]  Andrew Emili,et al.  Protein corona fingerprinting predicts the cellular interaction of gold and silver nanoparticles. , 2014, ACS nano.

[24]  A. Walsh,et al.  Band alignment of rutile and anatase TiO₂. , 2013, Nature materials.

[25]  Caterina Scoglio,et al.  Dynamics of Nanoparticle-Protein Corona Complex Formation: Analytical Results from Population Balance Equations , 2013, PloS one.

[26]  Sara A Love,et al.  Assessing nanoparticle toxicity. , 2012, Annual review of analytical chemistry.

[27]  Lutz Mädler,et al.  Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation. , 2012, ACS nano.

[28]  A. Becke,et al.  Atomic volumes and polarizabilities in density-functional theory. , 2012, The Journal of chemical physics.

[29]  R. Zhou,et al.  Binding of blood proteins to carbon nanotubes reduces cytotoxicity , 2011, Proceedings of the National Academy of Sciences.

[30]  P. Brown,et al.  On the distribution of protein refractive index increments. , 2011, Biophysical journal.

[31]  T. Higashi,et al.  Binding of Human Serum Proteins to Titanium Dioxide Particles In Vitro , 2011, Journal of occupational health.

[32]  Jerzy Leszczynski,et al.  Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. , 2011, Nature nanotechnology.

[33]  Jim E Riviere,et al.  An index for characterization of nanomaterials in biological systems. , 2010, Nature nanotechnology.

[34]  Sara Linse,et al.  Modeling the Time Evolution of the Nanoparticle-Protein Corona in a Body Fluid , 2010, PloS one.

[35]  S. Grimme,et al.  A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. , 2010, The Journal of chemical physics.

[36]  Bálint Aradi,et al.  An Improved Self-Consistent-Charge Density-Functional Tight-Binding (SCC-DFTB) Set of Parameters for Simulation of Bulk and Molecular Systems Involving Titanium. , 2010, Journal of chemical theory and computation.

[37]  A. Tkatchenko,et al.  Accurate molecular van der Waals interactions from ground-state electron density and free-atom reference data. , 2009, Physical review letters.

[38]  T. Frauenheim,et al.  DFTB+, a sparse matrix-based implementation of the DFTB method. , 2007, The journal of physical chemistry. A.

[39]  Sara Linse,et al.  Understanding the nanoparticle–protein corona using methods to quantify exchange rates and affinities of proteins for nanoparticles , 2007, Proceedings of the National Academy of Sciences.

[40]  W. Gelbart,et al.  Adhesion and Wrapping in Colloid−Vesicle Complexes , 2002 .

[41]  Sándor Suhai,et al.  Self-consistent-charge density-functional tight-binding method for simulations of complex materials properties , 1998 .

[42]  Burke,et al.  Generalized Gradient Approximation Made Simple. , 1996, Physical review letters.

[43]  Masanori Matsui,et al.  Molecular Dynamics Simulation of the Structural and Physical Properties of the Four Polymorphs of TiO2 , 1991 .

[44]  Martins,et al.  Efficient pseudopotentials for plane-wave calculations. , 1991, Physical review. B, Condensed matter.

[45]  H. Monkhorst,et al.  SPECIAL POINTS FOR BRILLOUIN-ZONE INTEGRATIONS , 1976 .

[46]  W. Helfrich Elastic Properties of Lipid Bilayers: Theory and Possible Experiments , 1973, Zeitschrift fur Naturforschung. Teil C: Biochemie, Biophysik, Biologie, Virologie.

[47]  H. C. Hamaker The London—van der Waals attraction between spherical particles , 1937 .

[48]  M. Schneemilch,et al.  Free energy of adhesion of lipid bilayers on titania surfaces. , 2019, The Journal of chemical physics.

[49]  Peter Beike,et al.  Intermolecular And Surface Forces , 2016 .