We are primarily interested in antigen presentation by major histocompatibility complex molecules, their recognition by T cell receptors, and the design and engineering of novel therapeutics based on T cell-mediated immunity. Our approach integrates structural biology, protein biophysics, computational biology, and molecular immunology.
Most cells in the body express class I or class II major histocompatibility complex proteins (MHC), or MHC proteins, which bind and “present” peptides derived from intracellular or extracellular proteins. Recognition of a peptide/MHC complex by a T cell receptor (TCR) on the surface of a helper or cytotoxic T cell stimulates a T cell-mediated immune response. While best recognized for its role in the immune response to viruses, T cell mediated immunity also plays a key role in the immune response to other pathogens, in transplant rejection, autoimmunity, and cancer.
Many projects in the lab are centered on the structural, biophysical, and immunological principles of TCR recognition of peptide/MHC complexes. The TCR-pMHC interaction is one of the most complex protein-ligand interactions known to biology. We aim to understand the complexities from a physical perspective, relying heavily on protein crystallography and experimental and computational biophysics, but also an increasing amount of in vitro and in vivo immunology. Our overall aims are to understand how TCR recognition influences immunity in health and disease.
As we gain insight into TCR recognition of peptide/MHC, we are using this knowledge to engineer TCRs with improved recognition properties with the goal of developing novel therapeutics. Other projects are centered on understanding how recognition is communicated across the cell membrane. Here, we aim to gain a deeper understanding of the molecular changes that occur upon binding and how these influence protein architecture, motion, and connections with cell signaling units.
We have a special interest in the immune response to cancer. There is a close connection between cellular immunity and cancer, and some of the earliest cancer treatments of the modern era focused on eliciting or enhancing anti-cancer immune responses (the Cancer Research Institute has an excellent primer on cancer and the immune system). We study the development and enhancement of personalized cancer vaccines as well as sophisticated approaches that involve the creation of genetically engineered immune systems for cancer patients. In these areas, we leverage our understanding of the structural and biophysical underpinnings of TCR recognition of peptide/MHC in order to help drive advance in cancer immunology.
Current projects are centered around:
- Improving our understanding of the range of targets a given TCR will recognize and relating these to structural and physical principles of TCR-peptide/MHC interfaces. This work involves immunological and computational screening, followed by structural and biophysical interrogation.
- Developing new ways to model and predict TCR targets from structural principles. This work involves protein modeling, coupled with advanced sampling and machine learning approaches to optimize prediction algorithms.
- Engineering TCRs to improve on-target specificity and reduce off-target recognition in new TCR-based cell therapies. This work couples protein design strategies with immunological testing and screening.
- Understanding the mechanism of force-dependent “catch bonds” in immune receptors. This work involves computational simulations followed by biophysical and immunological exploration of simulation-derived hypotheses.
- Gaining insight into autoimmunity and transplant rejection, particularly the role of prior exposure to pathogens such as viruses and the increased risk of autoimmunity and organ rejection.
- Understanding and predicting the immunogenicity of neoantigens present in cancer genomes for the development of personalized cancer vaccines. This work involves structural biology, high throughput structural modeling, and the development of novel scoring and prediction functions using machine learning techniques.
- Studying the role of dynamic allostery in TCR signaling. As with catch bonds, this project couples molecular dynamics simulations with biophysical and immunological testing of hypotheses.