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

Enzymes that cleave ATP to activate carboxylic acids play essential roles in primary and secondary metabolism in all domains of life. Class I adenylate-forming enzymes share a conserved structural fold but act on a wide range of substrates to catalyze reactions involved in bioluminescence, nonribosomal peptide biosynthesis, fatty acid activation, and β-lactone formation. Despite their metabolic importance, the substrates and functions of the vast majority of adenylate-forming enzymes are unknown without tools available to accurately predict them. Given the crucial roles of adenylate-forming enzymes in biosynthesis, this also severely limits our ability to predict natural product structures from biosynthetic gene clusters. Here we used machine learning to predict adenylate-forming enzyme function and substrate specificity from protein sequences. We built a web-based predictive tool and used it to comprehensively map the biochemical diversity of adenylate-forming enzymes across >50,000 candidate biosynthetic gene clusters in bacterial, fungal, and plant genomes. Ancestral phylogenetic reconstruction and sequence similarity networking of enzymes from these clusters suggested divergent evolution of the adenylate-forming superfamily from a core enzyme scaffold most related to contemporary CoA ligases toward more specialized functions including β-lactone synthetases. Our classifier predicted β-lactone synthetases in uncharacterized biosynthetic gene clusters conserved in >90 different strains of Nocardia. To test our prediction, we purified a candidate β-lactone synthetase from Nocardia brasiliensis and reconstituted the biosynthetic pathway in vitro to link the gene cluster to the β-lactone natural product, nocardiolactone. We anticipate that our machine learning approach will aid in functional classification of enzymes and advance natural product discovery.

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

10.1101/856955 ◽

2019 ◽

Cited By ~ 2

Author(s):

Serina L. Robinson ◽

Barbara R. Terlouw ◽

Megan D. Smith ◽

Sacha J. Pidot ◽

Tim P. Stinear ◽

...

Keyword(s):

Machine Learning ◽

Natural Product ◽

Sequence Similarity ◽

Global Analysis ◽

Gene Clusters ◽

Acid Activation ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Predictive Tool ◽

Wide Range

ABSTRACTEnzymes that cleave ATP to activate carboxylic acids play essential roles in primary and secondary metabolism in all domains of life. Class I adenylate-forming enzymes share a conserved structural fold but act on a wide range of substrates to catalyze reactions involved in bioluminescence, nonribosomal peptide biosynthesis, fatty acid activation, and β-lactone formation. Despite their metabolic importance, the substrates and catalytic functions of the vast majority of adenylate-forming enzymes are unknown without tools available to accurately predict them. Given the crucial roles of adenylate-forming enzymes in biosynthesis, this also severely limits our ability to predict natural product structures from biosynthetic gene clusters. Here we used machine learning to predict adenylate-forming enzyme function and substrate specificity from protein sequence. We built a web-based predictive tool and used it to comprehensively map the biochemical diversity of adenylate-forming enzymes across >50,000 candidate biosynthetic gene clusters in bacterial, fungal, and plant genomes. Ancestral enzyme reconstruction and sequence similarity networking revealed a ‘hub’ topology suggesting radial divergence of the adenylate-forming superfamily from a core enzyme scaffold most related to contemporary aryl-CoA ligases. Our classifier also predicted β-lactone synthetases in novel biosynthetic gene clusters conserved across >90 different strains of Nocardia. To test our computational predictions, we purified a candidate β-lactone synthetase from Nocardia brasiliensis and reconstituted the biosynthetic pathway in vitro to link the gene cluster to the β-lactone natural product, nocardiolactone. We anticipate our machine learning approach will aid in functional classification of enzymes and advance natural product discovery.

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Discovery and characterisation of an amidine-containing ribosomally-synthesised peptide that is widely distributed in nature

Chemical Science ◽

10.1039/d1sc01456k ◽

2021 ◽

Author(s):

Alicia H Russell ◽

Natalia Miguel Vior ◽

Edward Steven Hems ◽

Rodney Lacret ◽

Andrew William Truman

Keyword(s):

Natural Product ◽

Genome Mining ◽

Gene Clusters ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Wide Range ◽

Modified Peptides

Ribosomally synthesised and post-translationally modified peptides (RiPPs) are a structurally diverse class of natural product with a wide range of bioactivities. Genome mining for RiPP biosynthetic gene clusters (BGCs) is...

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Understanding and manipulating antibiotic production in actinomycetes

Biochemical Society Transactions ◽

10.1042/bst20130214 ◽

2013 ◽

Vol 41 (6) ◽

pp. 1355-1364 ◽

Cited By ~ 29

Author(s):

Mervyn J. Bibb

Keyword(s):

Natural Product ◽

Biological Activities ◽

Antibiotic Production ◽

Gene Clusters ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Natural Product Biosynthesis ◽

Genomic Technologies ◽

Wide Range ◽

Recent Developments

Actinomycetes are prolific producers of natural products with a wide range of biological activities. Many of the compounds that they make (and derivatives thereof) are used extensively in medicine, most notably as clinically important antibiotics, and in agriculture. Moreover, these organisms remain a source of novel and potentially useful molecules, but maximizing their biosynthetic potential requires a better understanding of natural product biosynthesis. Recent developments in genome sequencing have greatly facilitated the identification of natural product biosynthetic gene clusters. In the present article, I summarize the recent contributions of our laboratory in applying genomic technologies to better understand and manipulate natural product biosynthesis in a range of different actinomycetes.

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Mining of Cyanobacterial Genomes Indicates That Plasmids Are Involved in the Production of Natural Products

10.21203/rs.3.rs-121751/v1 ◽

2020 ◽

Author(s):

Rafael Popin ◽

Danillo Alvarenga ◽

Raquel Castelo-Branco ◽

David Fewer ◽

Kaarina Sivonen

Keyword(s):

Natural Products ◽

Natural Product ◽

Biological Activities ◽

Gene Clusters ◽

Biosynthetic Pathways ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Chemical Structures ◽

Wide Range ◽

Genes Encoding

Abstract Background Microbial natural products have unique chemical structures and diverse biological activities. Cyanobacteria commonly possess a wide range of biosynthetic gene clusters to produce natural products. Several studies have mapped the distribution of natural product biosynthetic gene clusters in cyanobacterial genomes. However, little attention has been paid to natural product biosynthesis in plasmids. Some genes encoding cyanobacterial natural product biosynthetic pathways are believed to be dispersed by plasmids through horizontal gene transfer. Thus, we examined complete cyanobacterial genomes to assess if plasmids are involved in the production and dissemination of natural products by cyanobacteria.Results The 185 analyzed genomes possessed 1 to 42 gene clusters and an average of 10. In total, 1816 biosynthetic gene clusters were found. Approximately 95% of these clusters were present in chromosomes. The remaining 5% were present in plasmids, from which homologs of the biosynthetic pathways for aeruginosin, anabaenopeptin, ambiguine, cryptophycin, hassallidin, geosmin, and microcystin were manually curated. The cryptophycin pathway was previously described as active while the other gene cluster include all genes for biosynthesis. Approximately 12% of the 424 analyzed cyanobacterial plasmids contained homologs of genes involved in conjugation. Large plasmids, previously named as “chromids”, were also observed to be widespread in cyanobacteria. Sixteen cryptic natural product biosynthetic gene clusters and geosmin biosynthetic gene clusters were located in those mobile plasmids.Conclusion Homologues of genes involved in the production of toxins, protease inhibitors, odorous compounds, antimicrobials, antitumorals, and other unidentified natural products are located in cyanobacterial plasmids. Some of these plasmids are predicted to be conjugative. The present study provides in silico evidence that plasmids are involved in the distribution of natural product biosynthetic pathways in cyanobacteria.

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Mining metagenomes for natural product biosynthetic gene clusters: unlocking new potential with ultrafast techniques

10.1101/2021.01.20.427441 ◽

2021 ◽

Author(s):

Emiliano Pereira-Flores ◽

Marnix Medema ◽

Pier Luigi Buttigieg ◽

Peter Meinicke ◽

Frank Oliver Glöckner ◽

...

Keyword(s):

Natural Products ◽

Natural Product ◽

Human Microbiome ◽

Gene Clusters ◽

Human Microbiome Project ◽

Biosynthetic Gene Cluster ◽

Metagenomic Data ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Wide Range

Microorganisms produce an immense variety of natural products through the expression of Biosynthetic Gene Clusters (BGCs): physically clustered genes that encode the enzymes of a specialized metabolic pathway. These natural products cover a wide range of chemical classes (e.g., aminoglycosides, lantibiotics, nonribosomal peptides, oligosaccharides, polyketides, terpenes) that are highly valuable for industrial and medical applications1. Metagenomics, as a culture-independent approach, has greatly enhanced our ability to survey the functional potential of microorganisms and is growing in popularity for the mining of BGCs. However, to effectively exploit metagenomic data to this end, it will be crucial to more efficiently identify these genomic elements in highly complex and ever-increasing volumes of data2. Here, we address this challenge by developing the ultrafast Biosynthetic Gene cluster MEtagenomic eXploration toolbox (BiG-MEx). BiG-MEx rapidly identifies a broad range of BGC protein domains, assess their diversity and novelty, and predicts the abundance profile of natural product BGC classes in metagenomic data. We show the advantages of BiG-MEx compared to standard BGC-mining approaches, and use it to explore the BGC domain and class composition of samples in the TARA Oceans3 and Human Microbiome Project datasets4. In these analyses, we demonstrate BiG-MEx’s applicability to study the distribution, diversity, and ecological roles of BGCs in metagenomic data, and guide the exploration of natural products with clinical applications.

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