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

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, D-xrd, rho, D-h, 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.