Mapping and Engineering Structural Coupling Networks that Control Enzyme Catalysis

Enzymes are efficient, low energy catalysts. They operate at room temperature and often have very high yields. They are also incredibly versatile and can be used to produce drugs, chemicals, and structural materials. Unfortunately, they are often much slower than most industrial catalysts. The highly beneficial properties are tied to the flexibility of their structure, which allows substrates and products to access the reactive site on the enzyme. This project will use a combination of experiments and modeling to uncover the functional relationships between different parts of an enzyme's structure and its effectiveness as a catalyst. The model enzyme will be a fluorinase. It catalyzes the formation of carbon-fluorine bonds from inorganic fluoride. The project will also train students in biophysical chemistry, mass spectrometry, computation, and data science. K–12 outreach and public activities will highlight research and biotechnology careers. The central hypothesis is that catalytic performance is governed by coordinated communication among distal regions, subunit interfaces, and the active site. Time-resolved hydrogen–deuterium exchange mass spectrometry will map mutation- and substrate-induced changes in protein structural dynamics. Enzyme kinetics will quantify their effects on catalytic efficiency. Molecular dynamics simulations and residue-network analysis will provide an atomistic view of correlated motions. This may identify pathways that transmit structural signals across the enzyme. These mechanistic insights will guide combinatorial mutagenesis and high-throughput screening of selected motifs and interface regions. This should generate sequence–activity datasets for machine learning-assisted optimization. Iterative cycles of computational prediction and experimental validation will allow the models to improve over time and prioritize variants that increase catalytic activity. In parallel, genome mining will identify previously uncharacterized fluorinases with distinct sequence and structural features, which will be evaluated for activity, substrate preference, stability, and alternative structural strategies for enzymatic fluorination. Together, these studies will define quantitative relationships among sequence, structural coupling, and function and establish general principles for predictive enzyme engineering. 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.