CAREER: The Mechanistic Roles of Receptor Oligomerization in Molecular Signaling

About This Grant

The Chemistry of Life Processes Program in the Chemistry Division is funding Dr. Colin Kinz-Thompson from Rutgers University-Newark to investigate how bacteria are able to accurately sense their surroundings and to develop machine learning tools for protein design. The proposed studies will experimentally determine to how accurately receptors can sense small molecules and whether this information sensing process can be reprogrammed. This work will allow the repurposing of receptors for new applications, such as improved monitoring and diagnostics in agriculture or environmental systems. The second objective is to develop new machine learning models that extract functional and evolutionary data to improve the engineering of new proteins. This project will serve as a training pipeline for undergraduate researchers into scientific research. In addition, a seminar series will be developed to help graduate students and postdoctoral scientists learn to use machine learning techniques. Finally, an undergraduate course will be developed for honors students at Rutgers focused on the intersection of machine learning and biomolecules. Altogether, this educational plan serves as a strong training pipeline to educate the next generation of STEM workers in machine learning and biochemical research. The objective of this research is to determine the mechanistic details and the thermodynamic forces that drive the function of methyl-accepting chemotaxis protein (MCP) receptors in chemosensory systems as dimers, and why MCP dimers further oligomerize into chemosensory arrays whose structure regulates chemosensory system responses. This research uses single-molecule fluorescence resonance energy transfer microscopy, native protein mass spectrometry, and free energy perturbation calculations to explore non-equilibrium kinetic models of free-energy transduction in transmembrane signaling, and a combination of fluorescence super-resolution microscopy and coarse-grained model building to resolve the structure and dynamics of these complexes in live cells. Finally, the interpretability and evolutionary information content of protein language models (PLM) will be explored by using a transformer-based PLM to successively optimize protein sequences as a function of model size, and then experimentally measuring how protein functionality changes along that optimization pathway by monitoring changes in fluorescence, antibiotic-resistance, and/or molecular dynamics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.