Enzymatic carbon-fluorine bond cleavage by human gut microbes

• 成果类型: Article
• 署名作者: Probst, Silke I.; Felder, Florian D.; Poltorak, Victoria; Mewalal, Ritesh; Blaby, Ian K.; Robinson, Serina L.
• 署名单位: Swiss Federal Institutes of Technology Domain; Swiss Federal Institute of Aquatic Science & Technology (EAWAG); Swiss Federal Institutes of Technology Domain; ETH Zurich; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-13339
• DOI: 10.1073/pnas.2504122122
• 发表日期: 2025-06-17
• 关键词: protein; defluorination; 5-fluorouracil; metabolism; resistance; diversity; bacteria; enzymes; DESIGN; side

摘要

Fluorinated compounds are used for agrochemical, pharmaceutical, and numerous industrial applications, resulting in global contamination. In many molecules, fluorine is incorporated to enhance the half-life and improve bioavailability. Fluorinated compounds enter the human body through food, water, and xenobiotics including pharmaceuticals, exposing gut microbes to these substances. The human gut microbiota is known for its xenobiotic biotransformation capabilities, but it was not previously known whether gut microbial enzymes could break carbon-fluorine bonds, potentially altering the toxicity of these compounds. Here, through the development of a rapid, miniaturized fluoride detection assay for whole-cell screening, we identified active gut microbial defluorinases. We biochemically characterized enzymes from diverse human gut microbial classes including Clostridia, Bacilli, and Coriobacteriia, with the capacity to hydrolyze (di)fluorinated organic acids and a fluorinated amino acid. Whole-protein alanine scanning, molecular dynamics simulations, and chimeric protein design enabled the identification of a disordered C-terminal protein segment involved in defluorination activity. Domain swapping exclusively of the C-terminus conferred defluorination activity to a nondefluorinating dehalogenase. To advance our understanding of the structural and sequence differences between defluorinating and nondefluorinating dehalogenases, we trained machine learning models which identified protein termini as important features. Models trained on 41-amino acid segments from protein C termini alone predicted defluorination activity with 83% accuracy (compared to 95% accuracy based on full-length protein features). This work is relevant for therapeutic interventions and environmental and human health by uncovering specificity-determining signatures of fluorine biochemistry from the gut microbiome.