The design of nanoparticles (NPs) and nanomaterials that can induce specific structural transitions in nucleic acids (NA) is important for nanotechnology applications including gene delivery and nanoelectronics. NP biocompatibility and efficacy is determined by geometry, charge, and surface chemistry. Advancing NPs to the clinic requires optimization, which is prohibitively expensive, and a mechanistic understanding of NP-NA interactions, which remains unknown. This project will advance tailored NP gene delivery by a multiscale optimization employing all-atom molecular dynamics (MD) simulations, leveraging machine learning algorithms and employing dissipative particle dynamics (DPD) simulations.

In this talk, I will discuss how we employed atomistic molecular dynamics simulations to understand the binding of nucleic acids to monolayer-protected gold nanoparticles. Results from these simulations were analyzed to determine modes of DNA and RNA bending with nanoparticles. These results allowed us to determine the training data for machine learning algorithms and design novel ligands capable of controlled wrapping of NA around NP. The information from MD simulations was used to parameterize and developed a DPD-based model, which allows for large-scale simulations of self-assembling polyelectrolytes materials and their morphological response to the changes in salt concentration and applied this method for the prediction of responsive morphologies of DNA-based micelles and gels. Our findings are useful for designing gene delivery systems with enhanced biocompatibility and selectivity.