(543c) Molecular and Data-Centric Modeling of Nanoparticle Interactions with Biological Interfaces

The properties of soft materials typically emerge from the interplay of molecular interactions, thermal fluctuations, and entropy. These factors are difficult to interrogate experimentally or predict from chemical structure alone. Molecular-scale simulations of soft materials also face challenges associated with modeling long timescale processes and navigating large design spaces. In this talk, I will describe our efforts to address these challenges by utilizing molecular dynamics simulations, enhanced sampling
techniques, and data-centric analysis to guide the design of nanoparticles (NPs) functionalized with organic, small-molecule ligands. NPs are versatile soft materials for biomedical applications, including targeted delivery, biosensing, and photothermal therapy, because ligands can be selected to tailor interactions at the nano-bio interface. However, subtle differences in NP composition (e.g., ligand selection and core size) can trigger large changes in macroscopic behavior (e.g., cellular uptake) that
cannot be predicted a priori. I will discuss our efforts to model interactions between small (less than 10 nm in diameter) NPs and model cell membranes. These simulations, and complementary experiments, reveal how systematic variations to ligand properties influence the minimum free energy pathways underlying membrane adsorption. I will also discuss the parameterization of quantitative structure-activity relationship models for NP cellular uptake using features obtained from high-throughput simulations. This work highlights our approach to derive chemically specific design guidelines for NPs with desired nano-bio interactions.