The proteins that sorb to the surface of engineered nanomaterials (ENMs) dramatically alter the particle surface chemistry, reactivity, and fate in living systems. Despite the importance of these protein coronas (PCs), they are not yet routinely incorporated into models of ENM cell update, toxicity, or environmental fate. In part, this is because of the high cost in PC characterization and the fact that PCs have a seemly endless diversity; the population of proteins in a PC depend upon ENM properties, available proteins, and reaction conditions. Here-in, we expand an experimental database of LC-MS/MS proteomics characterization of PCs on Ag ENMs. The PC populations are characterized and compared across Ag ENMs of three different sizes and surface coatings, as well as across two different populations of proteins (yeast and human). By analysing the PC database using a random forest classification (RFC) approach, a predictive analysis of PC populations is developed. Although initially trained on the yeast PC dataset, the RFC model can be applied to human PCs as well. Weighted contributions of ENM, solvent, and protein biophysical features within the model provide insight into PC formation across protein populations, including the uniform importance of protein-particle electrostatics and size. The developed methodology is broadly applicable to other nanoparticles and cell types, representing a valuable tool for PC evaluation and prediction.<br/>

Toward predictive analysis of nanoparticle protein corona populations

2019 Fall National Meeting

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