Nanoparticles are increasingly used in biomedical applications, including drug delivery. When exposed to biological fluids, nanoparticles are rapidly coated with proteins forming the “protein corona”. Depending on the binding affinities and the exchange rates of the proteins, the protein corona can be composed of multiple protein layers on the surface, a “hard” and “soft” corona. The tightly binding proteins with high binding affinities form the hard corona, and the loosely bound proteins with high exchange rates form the outer, soft corona. The identities and orientations of proteins in the corona strongly influence the biological fate of nanoparticle therapeutics. However, the prediction of protein-nanoparticle interactions remains a significant challenge. In this study, we examine how protein charge distribution influences nanoparticle-protein interactions. Specifically, we investigate how protein variants (KXA variants of GB3) interact with polystyrene nanoparticles (PSNPs) and study the nature of the protein corona. The binding affinities of the different variants to PSNPs are determined using isothermal titration calorimetry (ITC). It was found that the K19A GB3 variant exhibited the highest binding affinity for both non-functionalized PSNPs and carboxylate-functionalized PSNPs. Further, ITC thermograms suggest that this variant forms a monolayer, while other variants appear to bind in multiple layers. We hypothesize that the K19A variant has a flatter interaction surface with neutral charge that enables more efficient PSNP binding than other variants. Fluorescence denaturation experiments show that PSNPs destabilize GB3 variants. The correlation of ITC and fluorescence data reveal that the variants with higher binding affinity are destabilized more on nanoparticles. This suggests that GB3-PSNP binding has multiple thermodynamic signatures, each with its own characteristic binding interactions. This work demonstrates that the thermodynamics of nanoparticle binding can be understood using a protein’s structure, an important first step toward predicting the corona.

Protein charge distribution governs the nature of the nanoparticle protein corona

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