Mapping and engineering structural coupling networks that control enzyme catalysis

The National Science Foundation Division of Chemical, Bioengineering, Environmental, and Transport Systems awarded Tulane University $439,707 on November 1, 2026, under the Engineering program (CFDA 47.041) to map structural coupling networks that control enzyme catalysis and improve understanding of enzyme function for industrial applications.

The project uses combined experimental and computational approaches to uncover functional relationships between enzyme structure and catalytic performance, with fluorinase as the model enzyme. Work includes time-resolved hydrogen-deuterium exchange mass spectrometry to map mutation- and substrate-induced changes in protein structural dynamics, enzyme kinetics to quantify effects on catalytic efficiency, molecular dynamics simulations and residue-network analysis to identify signal-transmission pathways across the enzyme, and iterative cycles of computational prediction and experimental validation guided by combinatorial mutagenesis and high-throughput screening. Machine learning-assisted optimization will generate sequence-activity datasets to improve catalytic variants. The project also trains students in biophysical chemistry, mass spectrometry, computation, and data science, and delivers K–12 outreach and public activities highlighting research and biotechnology careers.

Work is performed in New Orleans, Louisiana, through October 31, 2029.

Abstract

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.- subawards are not planned for this award.