Computational Analysis of Nanoparticle Features on Protein Corona Composition in Biological Nanoparticle-Protein Interactions

Abstract

the key role of protein-nanoparticle (NP) interactions in biological mediums has begun to emerge recently with the development of the concept of NP-protein ‘corona’. A dynamic layer of proteins- referred to as corona- adsorb on to NP surfaces immediately upon entering a biological milieu. This layer of protein is mainly constructed via hydrophobic interactions in addition to the entropy-driven mechanisms. The unique fingerprint of protein corona for each NP type arises from the differences in the characteristics of NPs including SSA, Dxrd, ρ, Dh, PdI and Zeta. Therefore, in this paper, according to the characteristics of four different NPs and their corresponding quantifications of nine corona proteins taken from a study by Konduru et al., we computationally analyze the effect of the characteristics of NPs, and accordingly present a computational model to predict the quantification of the formed corona proteins around the NPs. For this, a multiple linear regression model is developed to investigate the effect of selective physicochemical characteristics of NPs on the protein corona formation. This model could be used as a predictive model in addition to the computational models to determine the percentage of proteins interacting with NPs.