Professor Qiang Cui is among the 26 recent faculty winners of the Vilas Associates competition at the University of Wisconsin-Madison. The Vilas award provides flexible research support for two years, and recipients are chosen on the basis of a competitive proposal for new or ongoing research of the highest quality and significance.
Cui’s award will allow his research group to test out a completely new area of research. They will work to design a new sampling method for molecular dynamic (MD) simulations of the interactions between biomolecules (e.g. proteins and cell membrane) and nanomaterials. The group’s other ongoing research projects include the application of MD simulations to understand enzyme catalysis, conformational transitions of proteins, and the biophysics of membrane-bound proteins.
Nanomaterials are commonly encountered throughout people’s daily lives, and there is currently a large body of literature on the biological impacts of nanomaterials on different organisms. Cui wants to know more than what’s toxic and what’s not toxic, but rather to understand at the molecular level how the nanomaterials are interacting with living systems.
“The specific material that we’re using as our model is titanium oxide. It’s being used in a lot of places such as sunscreen, paint, cosmetic materials.” Cui says, “With a fundamental understanding of how nanomaterials interact with proteins and cells, we can propose approaches to develop safer nanotechnology.”
This undertaking is not easy; there are many types of potential interactions between biomolecules and nanomaterials. To overcome these challenges, Cui draws inspiration from both biophysics and materials science to develop a new approach for robust and efficient simulations.
Cui relates his research to the efforts at the Nanoscale Science and Engineering Center, funded by the National Science Foundation, to determine the environmental health and safety implications of nanotechnology.
“They are doing a lot of interesting spectroscopic measurements to characterize the interactions,” Cui says, “ but you always want to have a microscopic description that can help you interpret the data. That’s where we come in.”