Conformational determinants of enzymatic per- and polyfluoroalkyl activation and selectivity

Objective

Per- and polyfluoroalkyl substances (PFAS) are persistent and pervasive environmental contaminants. Long-chain PFAS bioaccumulate in plants, humans, and wildlife, amplifying their toxicity and health risks, leading to increasing governmental regulations. Shorter-chain PFAS are being phased in by industry to replace longer-chain PFAS, but their toxicity is less known and they are harder to detect, highlighting the need for increased monitoring. The overall situation calls for rapid, accessible, and on-site methods to detect, differentiate, and break down diverse chain-length PFAS. To address this challenge, I propose to investigate ‘fluoro-selectivity’: the capacity of enzymes to differentiate between PFAS of varying chain-lengths and fluorinated versus non-fluorinated compounds. Strikingly, fluoro-selectivity already occurs in enzymes in nature. Here, I propose to use a combination of bioluminescence and machine learning to decipher the fluoro-selectivity code of natural enzymes that undergo conformational changes in response to different PFAS. In tandem, I will establish a new method using a fluoro-selective resistance mechanism to discover environmental enzymes capable of breaking down fluorinated compounds. Finally, I will take advantage of the close structural similarity between firefly luciferases - the enzymes that enable fireflies to glow - and natural PFAS-activating enzymes known as acyl-CoA ligases. Leveraging this evolutionary relationship, I will construct fluoro-selective, bioluminescent enzymes to enable the design of real-time, in situ, and chain length-specific PFAS biosensors. Mapping the first blueprint of enzymatic fluoro-selectivity will enable precision applications in environmental PFAS monitoring and remediation. More fundamentally, this work aims to reveal the underlying biochemical and evolutionary drivers of fluoro-selectivity from bacteria to fireflies, uncovering paths for the emergence of metabolic traits across the tree of life.