In silico profiling nanoparticles: predictive nanomodeling using universal nanodescriptors and various machine learning approaches.

Rational nanomaterial design is urgently demanded for new nanomaterial development with desired properties. However, computational nanomaterial modeling and virtual nanomaterial screening are not applicable for this purpose due to the complexity of nanomaterial structures. To address this challenge, a new computational workflow is established in this study to virtually profile nanoparticles by (1) constructing a structurally diverse virtual gold nanoparticle (GNP) library and (2) developing novel universal nanodescriptors. The emphasis of this study is the second task by developing geometrical nanodescriptors that are suitable for the quantitative modeling of GNPs and virtual screening purposes. The feasibility, rigor and applicability of this novel computational method are validated by testing seven GNP datasets consisting of 191 unique GNPs of various nano-bioactivities and physicochemical properties. The high predictability of the developed GNP models suggests that this workflow can be used as a universal tool for nanomaterial profiling and rational nanomaterial design.

[1]  Vincent M Rotello,et al.  Tunable inhibition and denaturation of alpha-chymotrypsin with amino acid-functionalized gold nanoparticles. , 2005, Journal of the American Chemical Society.

[2]  Bin Zhao,et al.  Elucidation of the Molecular Determinants for Optimal Perfluorooctanesulfonate Adsorption Using a Combinatorial Nanoparticle Library Approach. , 2017, Environmental science & technology.

[3]  D. Winkler,et al.  Probing enzyme-nanoparticle interactions using combinatorial gold nanoparticle libraries , 2015, Nano Research.

[4]  M. Hormozi-Nezhad,et al.  Using nano-QSAR to determine the most responsible factor(s) in gold nanoparticle exocytosis , 2015 .

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

[6]  A. Tropsha,et al.  Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles , 2016, Nanotoxicology.

[7]  Jerzy Leszczynski,et al.  Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides , 2014, Environmental Science and Pollution Research.

[8]  Morteza Mahmoudi,et al.  Exploring Cellular Interactions of Liposomes Using Protein Corona Fingerprints and Physicochemical Properties. , 2016, ACS nano.

[9]  Jerzy Leszczynski,et al.  From basic physics to mechanisms of toxicity: the "liquid drop" approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles. , 2014, Nanoscale.

[10]  Dan Peer,et al.  Nanoparticle hydrophobicity dictates immune response. , 2012, Journal of the American Chemical Society.

[11]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[12]  Danail Hristozov,et al.  Predicting toxic potencies of metal oxide nanoparticles by means of nano-QSARs , 2016, Nanotoxicology.

[13]  V. Rotello,et al.  Modulating Pharmacokinetics, Tumor Uptake and Biodistribution by Engineered Nanoparticles , 2011, PloS one.

[14]  David A Winkler,et al.  Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials. , 2016, Toxicology and applied pharmacology.

[15]  Hainan Sun,et al.  Induction of oxidative stress and sensitization of cancer cells to paclitaxel by gold nanoparticles with different charge densities and hydrophobicities. , 2018, Journal of materials chemistry. B.

[16]  Jerzy Leszczynski,et al.  Inhibitors or toxins? Large library target-specific screening of fullerene-based nanoparticles for drug design purpose. , 2017, Nanoscale.

[17]  Marlene T. Kim,et al.  Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling , 2015, Pharmaceutical Research.

[18]  Stefan Seeger,et al.  Industrial production quantities and uses of ten engineered nanomaterials in Europe and the world , 2012, Journal of Nanoparticle Research.

[19]  Yi Zhang,et al.  Repeated carbon nanotube administrations in male mice cause reversible testis damage without affecting fertility , 2010, Nature Nanotechnology.

[20]  Arnaud Magrez,et al.  In vitro investigation of the cellular toxicity of boron nitride nanotubes. , 2011, ACS nano.

[21]  Jian Wang,et al.  Nano(Q)SAR: Challenges, pitfalls and perspectives , 2015, Nanotoxicology.

[22]  Hongyu Zhou,et al.  A nano-combinatorial library strategy for the discovery of nanotubes with reduced protein-binding, cytotoxicity, and immune response. , 2008, Nano letters.

[23]  Shikha Gupta,et al.  Nano-QSAR modeling for predicting biological activity of diverse nanomaterials , 2014 .

[24]  Christy L. Haynes,et al.  Functional assessment of metal oxide nanoparticle toxicity in immune cells. , 2010, ACS nano.

[25]  M. Natália D. S. Cordeiro,et al.  Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory , 2017, Nanotoxicology.

[26]  Paul R. Gerber,et al.  MAB, a generally applicable molecular force field for structure modelling in medicinal chemistry , 1995, J. Comput. Aided Mol. Des..

[27]  Rong Liu,et al.  Nano-SAR development for bioactivity of nanoparticles with considerations of decision boundaries. , 2013, Small.

[28]  Daniel P Russo,et al.  Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling. , 2017, ACS nano.

[29]  Sung Tae Kim,et al.  Regulation of Macrophage Recognition through the Interplay of Nanoparticle Surface Functionality and Protein Corona. , 2016, ACS nano.

[30]  Andrew P. Worth,et al.  QSAR modeling of nanomaterials. , 2011, Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology.

[31]  Marlene T. Kim,et al.  Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches , 2013, Pharmaceutical Research.

[32]  Ruili Huang,et al.  Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big Data , 2015, Environmental health perspectives.

[33]  Julie Clark,et al.  Discovery of Novel Antimalarial Compounds Enabled by QSAR-Based Virtual Screening , 2013, J. Chem. Inf. Model..

[34]  Hongmao Sun,et al.  A Universal Molecular Descriptor System for Prediction of LogP, LogS, LogBB, and Absorption , 2004, J. Chem. Inf. Model..

[35]  Alexander Tropsha,et al.  Novel Variable Selection Quantitative Structure-Property Relationship Approach Based on the k-Nearest-Neighbor Principle , 2000, J. Chem. Inf. Comput. Sci..

[36]  Bo Yan,et al.  Fabrication of Corona-Free Nanoparticles with Tunable Hydrophobicity , 2014, ACS nano.

[37]  Timo Laaksonen,et al.  Stability and electrostatics of mercaptoundecanoic acid-capped gold nanoparticles with varying counterion size. , 2006, Chemphyschem : a European journal of chemical physics and physical chemistry.

[38]  M. Barkley,et al.  Toward understanding tryptophan fluorescence in proteins. , 1998, Biochemistry.

[39]  Robert C. Glen,et al.  Novel Methods for the Prediction of logP, pKa, and logD , 2002, J. Chem. Inf. Comput. Sci..

[40]  S. Hou,et al.  The Interplay of Size and Surface Functionality on the Cellular Uptake of Sub-10 nm Gold Nanoparticles. , 2015, ACS nano.

[41]  V. Pande,et al.  Heterogeneity even at the speed limit of folding: large-scale molecular dynamics study of a fast-folding variant of the villin headpiece. , 2007, Journal of molecular biology.

[42]  Julio C. Facelli,et al.  A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles , 2016, Comput. Methods Programs Biomed..

[43]  K. L. D. M. Weerawardene,et al.  Quantum Mechanical Studies of Large Metal, Metal Oxide, and Metal Chalcogenide Nanoparticles and Clusters. , 2015, Chemical reviews.

[44]  Feng Luan,et al.  Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach. , 2014, Nanoscale.

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

[46]  Hao Zhu,et al.  Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers , 2014, Journal of Computer-Aided Molecular Design.

[47]  Bo Yan,et al.  Surface Charge Controls the Suborgan Biodistributions of Gold Nanoparticles. , 2016, ACS nano.

[48]  Vincent M. Rotello,et al.  Enzyme-amplified array sensing of proteins in solution and in biofluids. , 2010, Journal of the American Chemical Society.

[49]  Louis H. Haber,et al.  Determination of the Surface Charge Density of Colloidal Gold Nanoparticles Using Second Harmonic Generation , 2015 .

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

[51]  Iosif I. Vaisman,et al.  Delaunay Tessellation of Proteins: Four Body Nearest-Neighbor Propensities of Amino Acid Residues , 1996, J. Comput. Biol..

[52]  Andrey A Toropov,et al.  Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. , 2015, Chemosphere.

[53]  Ran Chen,et al.  Quantification of nanoparticle pesticide adsorption: computational approaches based on experimental data , 2016, Nanotoxicology.

[54]  T. Puzyn,et al.  Toward the development of "nano-QSARs": advances and challenges. , 2009, Small.

[55]  D. Astruc,et al.  Gold nanoparticles: assembly, supramolecular chemistry, quantum-size-related properties, and applications toward biology, catalysis, and nanotechnology. , 2004, Chemical reviews.

[56]  W. Chan,et al.  Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties. , 2015, Nanoscale.

[57]  Bing Yan,et al.  Enhancing cell recognition by scrutinizing cell surfaces with a nanoparticle array. , 2011, Journal of the American Chemical Society.

[58]  A. Tropsha,et al.  Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces. , 2006, Journal of medicinal chemistry.

[59]  Bo Yan,et al.  Dual-Mode Mass Spectrometric Imaging for Determination of in Vivo Stability of Nanoparticle Monolayers. , 2017, ACS nano.

[60]  Yang Li,et al.  Perturbation of physiological systems by nanoparticles. , 2014, Chemical Society reviews.

[61]  Arafeh Bigdeli,et al.  Towards defining new nano-descriptors: extracting morphological features from transmission electron microscopy images , 2014 .

[62]  Kyle A. Beauchamp,et al.  Quantitative comparison of villin headpiece subdomain simulations and triplet–triplet energy transfer experiments , 2011, Proceedings of the National Academy of Sciences.

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

[64]  Tarmo Tamm,et al.  Fe-Doped ZnO nanoparticle toxicity: assessment by a new generation of nanodescriptors. , 2018, Nanoscale.

[65]  Lianhui Wang,et al.  Intracellular Adenosine Triphosphate Deprivation through Lanthanide-Doped Nanoparticles. , 2015, Journal of the American Chemical Society.

[66]  Jerzy Leszczynski,et al.  SMILES‐based optimal descriptors: QSAR analysis of fullerene‐based HIV‐1 PR inhibitors by means of balance of correlations , 2009, J. Comput. Chem..

[67]  A. Tropsha,et al.  Quantitative nanostructure-activity relationship modeling. , 2010, ACS nano.

[68]  Craig A. Poland,et al.  Zeta potential and solubility to toxic ions as mechanisms of lung inflammation caused by metal/metal oxide nanoparticles. , 2012, Toxicological sciences : an official journal of the Society of Toxicology.

[69]  R. Weissleder,et al.  Modeling biological activities of nanoparticles. , 2012, Nano letters.

[70]  David Rejeski,et al.  Nanotechnology in the real world: Redeveloping the nanomaterial consumer products inventory , 2015, Beilstein journal of nanotechnology.

[71]  Bradley Duncan,et al.  Targeting bacterial biofilms via surface engineering of gold nanoparticles. , 2015, RSC advances.

[72]  Vincent M Rotello,et al.  The role of surface functionality on acute cytotoxicity, ROS generation and DNA damage by cationic gold nanoparticles. , 2010, Small.

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

[74]  Hao Zhu,et al.  Universal nanohydrophobicity predictions using virtual nanoparticle library , 2019, Journal of Cheminformatics.

[75]  G. Caracciolo,et al.  Nanoparticles-cell association predicted by protein corona fingerprints. , 2016, Nanoscale.

[76]  Bin Zhao,et al.  Modulation of Carbon Nanotubes' Perturbation to the Metabolic Activity of CYP3A4 in the Liver , 2016 .

[77]  A. Nel,et al.  Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles. , 2011, Small.

[78]  Natalia Barkalina,et al.  Nanotechnology in reproductive medicine: emerging applications of nanomaterials. , 2014, Nanomedicine : nanotechnology, biology, and medicine.

[79]  Xue Z. Wang,et al.  (Q)SAR Modelling of Nanomaterial Toxicity - A Critical Review , 2015 .

[80]  Kyeongjae Cho,et al.  Developing Descriptors To Predict Mechanical Properties of Nanotubes , 2013, J. Chem. Inf. Model..

[81]  Serdar Durdagi,et al.  Computational design of novel fullerene analogues as potential HIV-1 PR inhibitors: Analysis of the binding interactions between fullerene inhibitors and HIV-1 PR residues using 3D QSAR, molecular docking and molecular dynamics simulations. , 2008, Bioorganic & medicinal chemistry.

[82]  Alexander Tropsha,et al.  Chembench: a cheminformatics workbench , 2010, Bioinform..