Global analysis of adenylate-forming enzymes reveals β-lactone biosynthesis pathway in pathogenic<i>Nocardia</i>

Robinson, Serina L.; Terlouw, Barbara R.; Smith, Megan D.; Pidot, Sacha J.; Stinear, Timothy P.; Medema, Marnix H.; Wackett, Lawrence P.

doi:10.1101/856955

Cited by 2 publications

(1 citation statement)

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“…1 ) is a fatty acid derivative with similarity to lipstatin that has been isolated from different Nocardia strains ( 29 ). Recently, Robinson and coworkers reported on the identification of the nocardiolactone gene cluster featuring a β-lactone synthetase ( 30 ). β-Lactone-containing natural products are potent inhibitors of diverse hydrolytic enzymes, and their biosynthesis has drawn a lot of attention ( 31 – 33 ).…”

Section: Resultsmentioning

confidence: 99%

Comparative Genomics and Metabolomics in the Genus Nocardia

et al. 2020

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Using automated genome analysis tools, it is often unclear to what degree genetic variability in homologous biosynthetic pathways relates to structural variation. This hampers strain prioritization and compound identification and can lead to overinterpretation of chemical diversity. Here, we assessed the metabolic potential of Nocardia, an underinvestigated actinobacterial genus that is known to comprise opportunistic human pathogens. Our analysis revealed a plethora of putative biosynthetic gene clusters of various classes, including polyketide, nonribosomal peptide, and terpenoid pathways. Furthermore, we used the highly conserved biosynthetic pathway for nocobactin-like siderophores to investigate how gene cluster differences correlate to structural differences in the produced compounds. Sequence similarity networks generated by BiG-SCAPE (Biosynthetic Gene Similarity Clustering and Prospecting Engine) showed the presence of several distinct gene cluster families. Metabolic profiling of selected Nocardia strains using liquid chromatography-mass spectrometry (LC-MS) metabolomics data, nuclear magnetic resonance (NMR) spectroscopy, and GNPS (Global Natural Product Social molecular networking) revealed that nocobactin-like biosynthetic gene cluster (BGC) families above a BiG-SCAPE threshold of 70% can be assigned to distinct structural types of nocobactin-like siderophores. IMPORTANCE Our work emphasizes that Nocardia represent a prolific source for natural products rivaling better-characterized genera such as Streptomyces or Amycolatopsis. Furthermore, we showed that large-scale analysis of biosynthetic gene clusters using similarity networks with high stringency allows the distinction and prediction of natural product structural variations. This will facilitate future genomics-driven drug discovery campaigns.

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Section: Resultsmentioning

confidence: 99%

Comparative Genomics and Metabolomics in the Genus Nocardia

et al. 2020

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Machine learning-based prediction of activity and substrate specificity for OleA enzymes in the thiolase superfamily

et al. 2020

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Enzymes in the thiolase superfamily catalyze carbon-carbon bond formation for the biosynthesis of polyhydroxyalkanoate storage molecules, membrane lipids, and bioactive secondary metabolites. Natural and engineered thiolases have applications in synthetic biology for the production of high-value compounds, including personal care products and therapeutics. A fundamental understanding of thiolase substrate specificity is lacking, particularly within the OleA protein family. The ability to predict substrates from sequence would advance (meta)genome mining efforts to identify active thiolases for the production of desired metabolites. To gain a deeper understanding of substrate scope within the OleA family, we measured the activity of 73 diverse bacterial thiolases with a library of 15 p-nitrophenyl ester substrates to build a training set of 1,095 unique enzyme-substrate pairs. We then used machine learning to predict thiolase substrate specificity from physicochemical and structural features. The area under the receiver operating characteristic curve was 0.89 for random forest classification of enzyme activity and our regression model had a test set root mean square error of 0.22 (R2 = 0.75) to quantitatively predict enzyme activity levels. Substrate aromaticity, oxygen content, and molecular connectivity were the strongest predictors of enzyme-substrate pairing. Key amino acid residues A173, I284, V287, T292, and I316 in the X. campestris OleA crystal structure lining the substrate binding pockets were important for thiolase substrate specificity and are attractive targets for future protein engineering studies. The predictive framework described here is generalizable and demonstrates how machine learning can be used to quantitatively understand and predict enzyme substrate specificity.

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Global analysis of adenylate-forming enzymes reveals β-lactone biosynthesis pathway in pathogenicNocardia

Cited by 2 publications

References 55 publications

Comparative Genomics and Metabolomics in the Genus Nocardia

Comparative Genomics and Metabolomics in the Genus Nocardia

Machine learning-based prediction of activity and substrate specificity for OleA enzymes in the thiolase superfamily

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