人类肠道微生物的酶促碳氟键裂解

IF 9.1 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Silke I. Probst, Florian D. Felder, Victoria Poltorak, Ritesh Mewalal, Ian K. Blaby, Serina L. Robinson
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引用次数: 0

摘要

含氟化合物用于农化、制药和许多工业应用,造成全球污染。在许多分子中,氟被加入以提高半衰期和提高生物利用度。含氟化合物通过食物、水和包括药物在内的外来生物进入人体,使肠道微生物暴露于这些物质之下。人类肠道微生物群以其异种生物转化能力而闻名,但以前不知道肠道微生物酶是否可以破坏碳氟键,从而潜在地改变这些化合物的毒性。在这里,通过开发一种快速、小型化的全细胞筛选氟化物检测方法,我们确定了活跃的肠道微生物去氟酶。我们对来自不同人类肠道微生物类别的酶进行了生化表征,包括梭状芽孢杆菌、芽孢杆菌和科里杆菌,这些酶具有水解(二)氟化有机酸和氟化氨基酸的能力。全蛋白丙氨酸扫描、分子动力学模拟和嵌合蛋白设计能够识别参与脱氟活性的无序c端蛋白片段。仅c端交换结构域使非脱氟脱卤酶具有脱氟活性。为了进一步了解除氟和非除氟脱卤酶之间的结构和序列差异,我们训练了机器学习模型,将蛋白质末端识别为重要特征。仅对蛋白质C末端的41个氨基酸片段进行训练的模型预测除氟活性的准确率为83%(相比之下,基于全长蛋白质特征的准确率为95%)。这项工作通过揭示肠道微生物组中氟生物化学的特异性决定特征,与治疗干预以及环境和人类健康相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enzymatic carbon–fluorine bond cleavage by human gut microbes
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.
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来源期刊
CiteScore
19.00
自引率
0.90%
发文量
3575
审稿时长
2.5 months
期刊介绍: The Proceedings of the National Academy of Sciences (PNAS), a peer-reviewed journal of the National Academy of Sciences (NAS), serves as an authoritative source for high-impact, original research across the biological, physical, and social sciences. With a global scope, the journal welcomes submissions from researchers worldwide, making it an inclusive platform for advancing scientific knowledge.
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