Our research falls into several main areas:
Infection by enveloped viruses. We study how viruses get into cells, and in particular the mechanisms of membrane fusion. We are also interested in how membrane composition affects viral infection, particularly the role of cholesterol.
Drug-resistant bacteria. We wish to understand, detect, and defeat extreme drug resistance in bacteria. A particular focus of study is carbapenem resistance in gram-negative bacteria called Enterobacteriaceae.
Advanced simulation methods. Molecular dynamics simulation is a powerful tool to understand atomic-level mechanism and detail for biomolecular processes. However, to understand complex biological processes such as those in infectious disease, we seek to estimate important reaction intermediates and rates rather than simply creating a “single-molecule movie”. Our group works both methods to accomplish this better for our target systems in infectious disease as well as fundamental methodology and simulation infrastructure to permit more flexible, powerful, and efficient simulation of molecular ensembles.
Physics of membrane interfaces. Viral entry (and many other important processes) occur at close membrane-membrane interfaces. We have used simulations to show how dynamics at either single or double membrane interfaces can be substantially non-ideal and may impact the process of membrane fusion.
Statistical learning. Almost every area of our research involves advanced machine learning or new statistical analysis methods. Examples include identifying mutations associated with increased drug resistance from genetic data, new sampling strategies to direct simulations and experiments of flexible protein-protein complexes, and information-theoretic approaches to predict residues important to beta-lactamase function.
Single-virus optical measurements. We use fluorescence microscopy and spectroscopy to study single-event kinetics of processes in viral entry. We combine these measurements with molecular dynamics simulations and machine learning to build physical models of viral entry.