Precursor peptide-targeted mining of more than one hundred thousand genomes expands the lanthipeptide natural product family

Walker, Mark C.; Mitchell, Douglas A.; Donk, Wilfred A. van der

doi:10.1101/2020.03.13.990614

Cited by 16 publications

(32 citation statements)

References 85 publications

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“…(B) Model validation of precision mode for select RiPP classes. Four populous classes of RiPPs were selected for thorough model validation, using the most recent published data sets of predicted BGCs for sactipeptides, ranthipeptides, lanthipeptides, and thiopeptides ( 24 , 25 , 32 ). These classes were chosen because of the high quality of the published data set used for comparison.…”

Section: Resultsmentioning

confidence: 99%

“…The generated pHMMs were considered valid if an hmmsearch of the UniProtKB database ( 30 ) with a bit score cutoff of 25 gave only hits within BGCs with architectures similar to those of the target class. In addition, characterized data sets of RiPP proteins (e.g., lanthipeptides [ 31 , 32 ], lasso peptides [ 22 , 23 ], and sactipeptides [ 24 ]) were used to test auxiliary models using hmmscan analysis. Models giving few or no hits were considered to have acceptably low false-positive rates.…”

Section: Resultsmentioning

confidence: 99%

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RRE-Finder: a Genome-Mining Tool for Class-Independent RiPP Discovery

et al. 2020

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Many ribosomally synthesized and posttranslationally modified peptide classes (RiPPs) are reliant on a domain called the RiPP recognition element (RRE). The RRE binds specifically to a precursor peptide and directs the posttranslational modification enzymes to their substrates. Given its prevalence across various types of RiPP biosynthetic gene clusters (BGCs), the RRE could theoretically be used as a bioinformatic handle to identify novel classes of RiPPs. In addition, due to the high affinity and specificity of most RRE-precursor peptide complexes, a thorough understanding of the RRE domain could be exploited for biotechnological applications. However, sequence divergence of RREs across RiPP classes has precluded automated identification based solely on sequence similarity. Here, we introduce RRE-Finder, a new tool for identifying RRE domains with high sensitivity. RRE-Finder can be used in precision mode to confidently identify RREs in a class-specific manner or in exploratory mode to assist in the discovery of novel RiPP classes. RRE-Finder operating in precision mode on the UniProtKB protein database retrieved ∼25,000 high-confidence RREs spanning all characterized RRE-dependent RiPP classes, as well as several yet-uncharacterized RiPP classes that require future experimental confirmation. Finally, RRE-Finder was used in precision mode to explore a possible evolutionary origin of the RRE domain. The results suggest RREs originated from a co-opted DNA-binding transcriptional regulator domain. Altogether, RRE-Finder provides a powerful new method to probe RiPP biosynthetic diversity and delivers a rich data set of RRE sequences that will provide a foundation for deeper biochemical studies into this intriguing and versatile protein domain. IMPORTANCE Bioinformatics-powered discovery of novel ribosomal natural products (RiPPs) has historically been hindered by the lack of a common genetic feature across RiPP classes. Herein, we introduce RRE-Finder, a method for identifying RRE domains, which are present in a majority of prokaryotic RiPP biosynthetic gene clusters (BGCs). RRE-Finder identifies RRE domains 3,000 times faster than current methods, which rely on time-consuming secondary structure prediction. Depending on user goals, RRE-Finder can operate in precision mode to accurately identify RREs present in known RiPP classes or in exploratory mode to assist with novel RiPP discovery. Employing RRE-Finder on the UniProtKB database revealed several high-confidence RREs in novel RiPP-like clusters, suggesting that many new RiPP classes remain to be discovered.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

RRE-Finder: a Genome-Mining Tool for Class-Independent RiPP Discovery

et al. 2020

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“…counting the actual number of biosynthetic-pfam hits rather than using a boolean absence/presence value). Alternatively, large BiG-SLiCE GCFs can be analyzed in more detail using BiG-SCAPE or using protein sequence similarity networks [97] (which can, for example, be very powerful for analyzing RiPP precursor peptide variation [98][99][100]).…”

Section: Charting a Global Map Of Bgc Diversitymentioning

confidence: 99%

BiG-SLiCE: A Highly Scalable Tool Maps the Diversity of 1.2 Million Biosynthetic Gene Clusters

Kautsar

Hooft

Ridder

et al. 2020

Preprint

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Genome mining for Biosynthetic Gene Clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools suffer from a bottleneck caused by the expensive network-based approach used to group these BGCs into Gene Cluster Families (GCFs). Here, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes (MAGs) within ten days on a typical 36-cores CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a "query mode" that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration. BiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global, searchable interconnected network of BGCs. As more genomes get sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-lab/bigslice.

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“…In addition, characterized datasets of RiPP proteins (e.g. lanthipeptides 32,33 , lasso peptides 23,24 , and sactipeptides 25 ) were used to test auxiliary models using HMMscan analysis.…”

Section: Development Of Rre-findermentioning

confidence: 99%

RRE-Finder: A Genome-Mining Tool for Class-Independent RiPP Discovery

Kloosterman

Shelton

Wezel

et al. 2020

Preprint

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Nearly half of the classes of natural products known as ribosomally synthesized and post-translationally modified peptides (RiPPs) are reliant on a protein domain called the RiPP recognition element (RRE) for peptide maturation. The RRE binds specifically to a linear precursor peptide and directs the posttranslational modification enzymes to their substrate. Given its prevalence across various types of RiPP biosynthetic gene clusters (BGCs), the RRE could theoretically be used as a bioinformatic handle to identify novel classes of RiPPs. In addition, due to the high affinity and specificity of most RRE:precursor peptide complexes, a thorough understanding of the RRE domain could be exploited for biotechnological applications. However, sequence divergence of the RRE domain across RiPP classes has precluded automated identification of RREs based solely on sequence similarity. Here, we introduce RRE-Finder, a novel tool for identifying RRE domains with high sensitivity. RRE-Finder can be used in "precision" mode to confidently identify RREs in a class-specific manner or in "exploratory" mode, which was designed to assist in the discovery of novel RiPP classes. RRE-Finder operating in precision mode on the UniProtKB protein database retrieved over 30,000 high-confidence RREs spanning all characterized RRE-dependent RiPP classes, as well as several yet-uncharacterized RiPP, putatively novel gene cluster architectures that will require future experimental work. Finally, RRE-Finder was used in precision mode to explore a possible evolutionary origin of the RRE domain. Altogether, RRE-Finder provides a powerful new method to probe RiPP biosynthetic diversity and delivers a rich dataset of RRE sequences that will provide a foundation for deeper biochemical studies into this intriguing and versatile protein domain.

show abstract

Precursor peptide-targeted mining of more than one hundred thousand genomes expands the lanthipeptide natural product family

Cited by 16 publications

References 85 publications

RRE-Finder: a Genome-Mining Tool for Class-Independent RiPP Discovery

RRE-Finder: a Genome-Mining Tool for Class-Independent RiPP Discovery

BiG-SLiCE: A Highly Scalable Tool Maps the Diversity of 1.2 Million Biosynthetic Gene Clusters

RRE-Finder: A Genome-Mining Tool for Class-Independent RiPP Discovery

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