(142b) Decoding Common Features of Protein-Nanoparticle Interactions

Nanoparticles have been recognized as key to revolutionizing the healthcare industry. However, excitement about the applications envisioned for nanoparticles is tempered by concerns about their potential toxicity. Both healthcare applications and potential toxicity require knowledge of nanoparticleâ??s interactions with proteins. Our goal is to discover the mechanisms that govern protein-nanoparticle interactions and to develop a tool that can predict interaction strengths based on knowledge solely of the proteinâ??s sequence and the nanoparticleâ??s chemicophysical properties. We calculate the interaction energies between 2315 proteins and three bare gold nanoparticles (diameter = 1.0, 2.0 and 4.0 nm) and identify the binding sites using computer simulations. This allows us to rank the protein-nanoparticle interaction strengths, explore the chemicophyiscal properties of the binding sites, and develop models that can predict nanoparticle-protein interaction energies. Our results show that glutamic acid and lysine have the highest chance to appear on nanoparticle binding sites. We develop several models that can qualitatively predict the interaction energy.