CBE & MSE: In Silico Design of Materials for Gene Therapy

Abstract: Gene therapy holds the promise of treatment for numerous diseases including many types of cancer, cardiovascular diseases and genetic disorders. Even though methods for gene delivery have been an active research area since early 90s, no gene therapeutic agents have been FDA approved for use in humans. The progress in gene therapy has been hindered by lack of safe, predictable and reliable methods for packaging, delivery and transport of genetic material.

Efficient wrapping or packaging of DNA is critical to enabling gene delivery, where nucleic acids are transported across cell membranes with the help of transfection vectors such as proteins, cationic dendrimers or nanoparticles. Because DNA/RNA transfection is dependent on the size, shape and surface properties of the DNA/RNA-vector complex, control over assembly structure is critical for creating effective transfection agents. Evolving nanomaterials to the clinic requires optimization, which is prohibitively expensive, and a mechanistic understanding of carriers-NA interactions, which remains unknown. Our group attempts to advance tailored materials 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’ll discuss two avenues for designing nanomaterials for gene delivery: the design of ligand functionalized inorganic nanoparticles and self-assembling DNA-based nanomaterials. 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 develop 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 we applied this method for the prediction of responsive morphologies of DNA-based micelles and gels. Our results will enable design of more efficient gene delivery systems with enhanced biocompatibility and selectivity.

Bio: Yaroslava G. Yingling is a professor of materials science and engineering at North Carolina State University. She received her university diploma in computer science and engineering from St. Petersburg State Technical University of Russia in 1996 and her doctorate in materials engineering and high performance computing from the Pennsylvania State University in 2002. She carried out postdoctoral research at Penn State University's Department of Chemistry and the National Institutes of Health National Cancer Institute prior to joining North Carolina State University in 2007. She received the National Science Foundation CAREER award (2012), American Chemical Society Open Eye Young Investigator Award (2012) and was named a NCSU University Faculty Scholar in 2014. Research interests in Yingling’s group are focused on the development of soft materials informatics, advanced computational models and novel algorithms for multiscale molecular modeling of soft and biological materials and aim to provide a fundamental understanding of the structure-property relations of a variety of soft materials systems that are formed through the process of self-assembly,

Host: Associate Professor Alon Gorodetsky