Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches

Abstract

The understanding of the mechanisms and interactions that occur when nanomaterials enter biological systems is important to improve their future use. The adsorption of proteins from biological fluids in a physiological environment to form a corona on the surface of nanoparticles represents a key step that influences nanoparticle behaviour. In this study, the quantitative description of the composition of the protein corona was used to study the effect on cell association induced by 84 surface-modified gold nanoparticles of different sizes. Quantitative relationships between the protein corona and the activity of the gold nanoparticles were modelled by using several machine learning-based linear and non-linear approaches. Models based on a selection of only six serum proteins had robust and predictive results. The Projection Pursuit Regression method had the best performances (r² = 0.91; Q²loo = 0.81; r²ext = 0.79). The present study confirmed the utility of protein corona composition to predict the bioactivity of gold nanoparticles and identified the main proteins that act as promoters or inhibitors of cell association. In addition, the comparison of several techniques showed which strategies offer the best results in prediction and could be used to support new toxicological studies on gold-based nanomaterials.

Keywords:

• Nanoparticles
• in silico models
• machine learning
• computational nanotoxicology
• protein corona

Acknowledgements

We thank Professor Paola Gramatica from the University of Insubria for helpful discussions. We also thank MIUR and University of Insubria for A. Sangion’s PhD scholarship and for the financial support during his experience as visiting PhD student at Paris Diderot University, Laboratoire ITODYS.