Expansion of RiPP biosynthetic space through integration of pan-genomics and machine learning uncovers a novel class of lanthipeptides

Microbial natural products constitute a wide variety of chemical compounds, many which can have antibiotic, antiviral, or anticancer properties that make them interesting for clinical purposes. Natural product classes include polyketides (PKs), nonribosomal peptides (NRPs), and ribosomally synthesized and post-translationally modified peptides (RiPPs). While variants of biosynthetic gene clusters (BGCs) for known classes of natural products are easy to identify in genome sequences, BGCs for new compound classes escape attention. In particular, evidence is accumulating that for RiPPs, subclasses known thus far may only represent the tip of an iceberg. Here, we present decRiPPter (Data-driven Exploratory Class-independent RiPP TrackER), a RiPP genome mining algorithm aimed at the discovery of novel RiPP classes. DecRiPPter combines a Support Vector Machine (SVM) that identifies candidate RiPP precursors with pan-genomic analyses to identify which of these are encoded within operon-like structures that are part of the accessory genome of a genus. Subsequently, it prioritizes such regions based on the presence of new enzymology and based on patterns of gene cluster and precursor peptide conservation across species. We then applied decRiPPter to mine 1,295 Streptomyces genomes, which led to the identification of 42 new candidate RiPP families that could not be found by existing programs. One of these was studied further and elucidated as a representative of a novel subfamily of lanthipeptides, which we designate class V. The 2D structure of the new RiPP, which we name pristinin A3 (1), was solved using nuclear magnetic resonance (NMR), tandem mass spectrometry (MS/MS) data, and chemical labeling. Two previously unidentified modifying enzymes are proposed to create the hallmark lanthionine bridges. Taken together, our work highlights how novel natural product families can be discovered by methods going beyond sequence similarity searches to integrate multiple pathway discovery criteria.

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Integration of machine learning and pan-genomics expands the biosynthetic landscape of RiPP natural products

10.1101/2020.05.19.104752 ◽

2020 ◽

Cited By ~ 1

Author(s):

Alexander M. Kloosterman ◽

Peter Cimermancic ◽

Somayah S. Elsayed ◽

Chao Du ◽

Michalis Hadjithomas ◽

...

Keyword(s):

Natural Products ◽

Sequence Similarity ◽

Genome Mining ◽

Gene Clusters ◽

Data Availability ◽

Training Data ◽

Support Vector ◽

Product Families ◽

Link Type ◽

Clinical Drugs

AbstractMost clinical drugs are based on microbial natural products, with compound classes including polyketides (PKS), non-ribosomal peptides (NRPS), fluoroquinones and ribosomally synthesized and post-translationally modified peptides (RiPPs). While variants of biosynthetic gene clusters (BGCs) for known classes of natural products are easy to identify in genome sequences, BGCs for new compound classes escape attention. In particular, evidence is accumulating that for RiPPs, subclasses known thus far may only represent the tip of an iceberg. Here, we present decRiPPter (Data-driven Exploratory Class-independent RiPP TrackER), a RiPP genome mining algorithm aimed at the discovery of novel RiPP classes. DecRiPPter combines a Support Vector Machine (SVM) that identifies candidate RiPP precursors with pan-genomic analyses to identify which of these are encoded within operon-like structures that are part of the accessory genome of a genus. Subsequently, it prioritizes such regions based on the presence of new enzymology and based on patterns of gene cluster and precursor peptide conservation across species. We then applied decRiPPter to mine 1,295 Streptomyces genomes, which led to the identification of 42 new candidate RiPP families that could not be found by existing programs. One of these was studied further and elucidated as a novel subfamily of lanthipeptides, designated Class V. Two previously unidentified modifying enzymes are proposed to create the hallmark lanthionine bridges. Taken together, our work highlights how novel natural product families can be discovered by methods going beyond sequence similarity searches to integrate multiple pathway discovery criteria.Code and data availabilityThe source code of DecRiPPter is freely available online at https://github.com/Alexamk/decRiPPter. Results of the data analysis are available online at http://www.bioinformatics.nl/~medem005/decRiPPter_strict/index.html and http://www.bioinformatics.nl/~medem005/decRiPPter_mild/index.html (for the strict and mild filters, respectively). All training data and code used to generate these, as well as outputs of the data analyses, are available on Zenodo at doi:10.5281/zenodo.3834818.

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The Tripod for Bacterial Natural Product Discovery: Genome Mining, Silent Pathway Induction, and Mass Spectrometry-Based Molecular Networking

mSystems ◽

10.1128/msystems.00160-17 ◽

2018 ◽

Vol 3 (2) ◽

Cited By ~ 14

Author(s):

Daniela B. B. Trivella ◽

Rafael de Felicio

Keyword(s):

Mass Spectrometry ◽

Natural Products ◽

Natural Product ◽

Biosynthetic Pathway ◽

Genome Mining ◽

Chemical Compounds ◽

Gene Clusters ◽

Tandem Mass ◽

Molecular Networking ◽

Natural Product Discovery

ABSTRACT Natural products are the richest source of chemical compounds for drug discovery. Particularly, bacterial secondary metabolites are in the spotlight due to advances in genome sequencing and mining, as well as for the potential of biosynthetic pathway manipulation to awake silent (cryptic) gene clusters under laboratory cultivation. Further progress in compound detection, such as the development of the tandem mass spectrometry (MS/MS) molecular networking approach, has contributed to the discovery of novel bacterial natural products. The latter can be applied directly to bacterial crude extracts for identifying and dereplicating known compounds, therefore assisting the prioritization of extracts containing novel natural products, for example. In our opinion, these three approaches—genome mining, silent pathway induction, and MS-based molecular networking—compose the tripod for modern bacterial natural product discovery and will be discussed in this perspective.

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Expanded Natural Product Diversity Revealed by Analysis of Lanthipeptide-Like Gene Clusters in Actinobacteria

Applied and Environmental Microbiology ◽

10.1128/aem.00635-15 ◽

2015 ◽

Vol 81 (13) ◽

pp. 4339-4350 ◽

Cited By ~ 42

Author(s):

Qi Zhang ◽

James R. Doroghazi ◽

Xiling Zhao ◽

Mark C. Walker ◽

Wilfred A. van der Donk

Keyword(s):

Natural Products ◽

Natural Product ◽

Gene Cluster ◽

Posttranslational Modifications ◽

Large Scale ◽

Biological Activities ◽

Gene Clusters ◽

Chemical Diversity ◽

Content Type ◽

Modified Peptides

ABSTRACTLanthionine-containing peptides (lanthipeptides) are a rapidly growing family of polycyclic peptide natural products belonging to the large class of ribosomally synthesized and posttranslationally modified peptides (RiPPs). Lanthipeptides are widely distributed in taxonomically distant species, and their currently known biosynthetic systems and biological activities are diverse. Building on the recent natural product gene cluster family (GCF) project, we report here large-scale analysis of lanthipeptide-like biosynthetic gene clusters fromActinobacteria. Our analysis suggests that lanthipeptide biosynthetic pathways, and by extrapolation the natural products themselves, are much more diverse than currently appreciated and contain many different posttranslational modifications. Furthermore, lanthionine synthetases are much more diverse in sequence and domain topology than currently characterized systems, and they are used by the biosynthetic machineries for natural products other than lanthipeptides. The gene cluster families described here significantly expand the chemical diversity and biosynthetic repertoire of lanthionine-related natural products. Biosynthesis of these novel natural products likely involves unusual and unprecedented biochemistries, as illustrated by several examples discussed in this study. In addition, class IV lanthipeptide gene clusters are shown not to be silent, setting the stage to investigate their biological activities.

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Recent development of antiSMASH and other computational approaches to mine secondary metabolite biosynthetic gene clusters

Briefings in Bioinformatics ◽

10.1093/bib/bbx146 ◽

2017 ◽

Vol 20 (4) ◽

pp. 1103-1113 ◽

Cited By ~ 37

Author(s):

Kai Blin ◽

Hyun Uk Kim ◽

Marnix H Medema ◽

Tilmann Weber

Keyword(s):

Natural Products ◽

Small Molecules ◽

Sequence Similarity ◽

Genome Mining ◽

Gene Clusters ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Rule Based ◽

Chemical Structures ◽

Annotation Quality

Abstract Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats such as rule-based BGC detection, sequence and annotation quality and cluster boundary prediction, which all have to be considered while planning for, performing and analyzing the results of genome mining studies.

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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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A Multi-Omics Characterization of the Natural Product Potential of Tropical Filamentous Marine Cyanobacteria

Marine Drugs ◽

10.3390/md19010020 ◽

2021 ◽

Vol 19 (1) ◽

pp. 20

Author(s):

Tiago Leão ◽

Mingxun Wang ◽

Nathan Moss ◽

Ricardo da Silva ◽

Jon Sanders ◽

...

Keyword(s):

Natural Products ◽

Natural Product ◽

Genome Mining ◽

Gene Clusters ◽

Microbial Interactions ◽

Marine Cyanobacteria ◽

Pharmaceutical Drugs ◽

Genes Encoding ◽

Microbial Natural Products

Microbial natural products are important for the understanding of microbial interactions, chemical defense and communication, and have also served as an inspirational source for numerous pharmaceutical drugs. Tropical marine cyanobacteria have been highlighted as a great source of new natural products, however, few reports have appeared wherein a multi-omics approach has been used to study their natural products potential (i.e., reports are often focused on an individual natural product and its biosynthesis). This study focuses on describing the natural product genetic potential as well as the expressed natural product molecules in benthic tropical cyanobacteria. We collected from several sites around the world and sequenced the genomes of 24 tropical filamentous marine cyanobacteria. The informatics program antiSMASH was used to annotate the major classes of gene clusters. BiG-SCAPE phylum-wide analysis revealed the most promising strains for natural product discovery among these cyanobacteria. LCMS/MS-based metabolomics highlighted the most abundant molecules and molecular classes among 10 of these marine cyanobacterial samples. We observed that despite many genes encoding for peptidic natural products, peptides were not as abundant as lipids and lipopeptides in the chemical extracts. Our results highlight a number of highly interesting biosynthetic gene clusters for genome mining among these cyanobacterial samples.

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

10.1101/2020.05.04.076059 ◽

2020 ◽

Author(s):

Alicia H. Russell ◽

Natalia M. Vior ◽

Edward S. Hems ◽

Rodney Lacret ◽

Andrew W. Truman

Keyword(s):

Natural Product ◽

Genome Mining ◽

Gene Clusters ◽

Model Organisms ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Post Translational Modifications ◽

Streptomyces Albus ◽

Modified Peptides ◽

Mining Tool

ABSTRACTRibosomally synthesised and post-translationally modified peptides (RiPPs) are a structurally diverse class of natural product with a range of bioactivities. Genome mining for RiPP biosynthetic gene clusters (BGCs) is often hampered by poor detection of the short precursor peptides that are ultimately modified into the final molecule. Here, we utilise a previously described genome mining tool, RiPPER, to identify novel RiPP precursor peptides near YcaO-domain proteins, enzymes that catalyse various RiPP post-translational modifications including heterocyclisation and thioamidation. Using this dataset, we identified a novel, diverse and highly conserved family of RiPP BGCs spanning over 230 species of Actinobacteria and Firmicutes. A representative BGC from Streptomyces albus J1074 was characterised, leading to the discovery of streptamidine, a novel-amidine containing RiPP. This highlights the breadth of unexplored natural products with structurally rare features, even in model organisms.

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Coupling Mass Spectral and Genomic Information to Improve Bacterial Natural Product Discovery Workflows

Marine Drugs ◽

10.3390/md19030142 ◽

2021 ◽

Vol 19 (3) ◽

pp. 142 ◽

Cited By ~ 1

Author(s):

Max Crüsemann

Keyword(s):

Mass Spectrometry ◽

Natural Products ◽

Natural Product ◽

Large Scale ◽

Genome Mining ◽

Structural Diversity ◽

Gene Clusters ◽

Genomic Information ◽

Mass Spectral ◽

Natural Product Discovery

Bacterial natural products possess potent bioactivities and high structural diversity and are typically encoded in biosynthetic gene clusters. Traditional natural product discovery approaches rely on UV- and bioassay-guided fractionation and are limited in terms of dereplication. Recent advances in mass spectrometry, sequencing and bioinformatics have led to large-scale accumulation of genomic and mass spectral data that is increasingly used for signature-based or correlation-based mass spectrometry genome mining approaches that enable rapid linking of metabolomic and genomic information to accelerate and rationalize natural product discovery. In this mini-review, these approaches are presented, and discovery examples provided. Finally, future opportunities and challenges for paired omics-based natural products discovery workflows are discussed.

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Synthetic Biology Advanced Natural Product Discovery

Metabolites ◽

10.3390/metabo11110785 ◽

2021 ◽

Vol 11 (11) ◽

pp. 785

Author(s):

Junyang Wang ◽

Jens Nielsen ◽

Zihe Liu

Keyword(s):

Natural Products ◽

Synthetic Biology ◽

Natural Product ◽

Rapid Development ◽

Genome Mining ◽

Gene Clusters ◽

Future Research ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Natural Product Discovery

A wide variety of bacteria, fungi and plants can produce bioactive secondary metabolites, which are often referred to as natural products. With the rapid development of DNA sequencing technology and bioinformatics, a large number of putative biosynthetic gene clusters have been reported. However, only a limited number of natural products have been discovered, as most biosynthetic gene clusters are not expressed or are expressed at extremely low levels under conventional laboratory conditions. With the rapid development of synthetic biology, advanced genome mining and engineering strategies have been reported and they provide new opportunities for discovery of natural products. This review discusses advances in recent years that can accelerate the design, build, test, and learn (DBTL) cycle of natural product discovery, and prospects trends and key challenges for future research directions.

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Identification of a Tambjamine Gene Cluster in Streptomyces Reveals Convergent Evolution of the Biosynthetic Pathway

10.26434/chemrxiv.12899264 ◽

2020 ◽

Author(s):

Neil L Grenade ◽

Dragos S. Chiriac ◽

Graeme W. Howe ◽

Avena Ross

Keyword(s):

Natural Products ◽

De Novo ◽

Sequence Similarity ◽

Genome Mining ◽

Gene Clusters ◽

Primary Metabolism ◽

Biosynthetic Gene Clusters ◽

Microbial Genomes ◽

Sequence Similarity Networks ◽

Related Compounds

Bacterial natural products are an immensely valuable source of therapeutics. As modern DNA sequencing efforts provide increasing numbers of microbial genomes, it is clear that the molecules produced by most natural product biosynthetic gene clusters (BGCs) remain unknown. Genome mining makes use of bioinformatic techniques to elucidate the natural products produced by these “orphan” BGCs. Here, we report the use of sequence similarity networks (SSNs) and genome neighborhood networks (GNNs) to identify an orphan BGC that is responsible for the production of the antitumor tambjamine BE-18591 in Streptomyces albus NRRL B-2362. Although BE-18591 is a close structural analogue of tambjamine YP1 produced by Pseudoalteromonas tunicata, the biosynthetic routes to produce these molecules differ significantly. Notably, the C12-alkylamine tail that is appended onto the bipyrrole core of tambjamine YP1 is derived from fatty acids siphoned from the primary metabolism of the pseudoalteromonad, whilst the S. albus NRRL B-2362 BGC encodes a dedicated system for the de novo biosynthesis of the alkylamine portion of tambjamine BE-18591. These remarkably different biosynthetic strategies represent a striking example of convergent BGC evolution, with selective pressure for the production of tambjamines seemingly leading to the emergence of separate biosynthetic pathways in pseudoalteromonads and streptomycetes that ultimately produce closely related compounds

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