Computational Analysis of 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 , ρ, D_h , PdI and Zeta potential. Therefore, in this presentation, according to the characteristics of four different NPs and their corresponding quantifications of nine corona proteins taken from a experimental measurements, 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.
Dr. Ali Ramazani is a Research Scientist at the Massachusetts Institute of Technology (MIT). His primary research focuses on the development of a fundamental, integrated and quantitative multi-scale materials modeling approaches (DFT, MD, and micromechanics) in combination with machine learning (ML) and artificial intelligence (AI) methodologies to design novel materials with exceptional properties for a wide variety of applications including energy, electronic, biomedical, automotive and aircraft structures.