Overview
Microbes are pervasive throughout our world, having beneficial and detrimental effects. Pathogens cause disease and the rise of antibiotic resistance is making treatment of bacterial infections increasingly complicated. Conversely, our microbiome has many health benefits and control of its composition and function is key to enabling new preventative and therapeutic applications. Advances in synthetic biology, genomics, and protein engineering are allowing us to capitalize on fundamental knowledge of host-microbe interactions to design microbes that can protect against infection and treat disease. Our work encompasses each of these areas, which are connected through their impact on and promise for human health and global sustainability. Current projects include:
Uncovering microcin diversity and functions
Microcins are potent and selective small protein antibacterials used in microbial competition. We are developing computational and experimental methods to uncover and characterize the diversity of microcins across Gram-negative bacteria. We explore their unique mechanisms of action, rare ability to translocate across bacterial membranes, host interactions, and potential to fight infection.
Designing bacteria-host interactions
Bacteria colonize diverse host niches and interact with endogenous microbes. We design probiotic bacteria to leverage these interactions and delivery therapeutic proteins and genes for treatment of infection, microbiome modulation, and control of host immunology and physiology.
Building bacterial display and secretion systems
The discovery and deployment of bioactive proteins requires advancement in computational and experimental screening. We develop bioinformatic tools to accelerate the discovery process and cell display and secretion systems to uncover and deliver natural and synthetic bioactive proteins.
Developing peptide and nanobody antibiotics
Peptides and antibodies provide diverse antibiotic scaffolds targeting essential bacterial processes. We use molecular screening and machine learning to speed the discovery of antibacterial variants and understand how their sequence controls their activity.
