antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation.

Microorganisms produce small bioactive compounds as part of their secondary or specialised metabolism. Often, such metabolites have antimicrobial, anticancer, antifungal, antiviral or other bio-activities and thus play an important role for applications in medicine and agriculture. In the past decade, genome mining has become a widely-used method to explore, access, and analyse the available biodiversity of these compounds. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free to use web server and as a standalone tool under an OSI-approved open source licence. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in archaea, bacteria, and fungi. Here, we present the updated version 7 of antiSMASH. antiSMASH 7 increases the number of supported cluster types from 71 to 81, as well as containing improvements in the areas of chemical structure prediction, enzymatic assembly-line visualisation and gene cluster regulation.

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.

CAGECAT: The CompArative GEne Cluster Analysis Toolbox for rapid search and visualisation of homologous gene clusters.

BACKGROUND: Co-localized sets of genes that encode specialized functions are common across microbial genomes and occur in genomes of larger eukaryotes as well. Important examples include Biosynthetic Gene Clusters (BGCs) that produce specialized metabolites with medicinal, agricultural, and industrial value (e.g. antimicrobials). Comparative analysis of BGCs can aid in the discovery of novel metabolites by highlighting distribution and identifying variants in public genomes. Unfortunately, gene-cluster-level homology detection remains inaccessible, time-consuming and difficult to interpret. RESULTS: The comparative gene cluster analysis toolbox (CAGECAT) is a rapid and user-friendly platform to mitigate difficulties in comparative analysis of whole gene clusters. The software provides homology searches and downstream analyses without the need for command-line or programming expertise. By leveraging remote BLAST databases, which always provide up-to-date results, CAGECAT can yield relevant matches that aid in the comparison, taxonomic distribution, or evolution of an unknown query. The service is extensible and interoperable and implements the cblaster and clinker pipelines to perform homology search, filtering, gene neighbourhood estimation, and dynamic visualisation of resulting variant BGCs. With the visualisation module, publication-quality figures can be customized directly from a web-browser, which greatly accelerates their interpretation via informative overlays to identify conserved genes in a BGC query. CONCLUSION: Overall, CAGECAT is an extensible software that can be interfaced via a standard web-browser for whole region homology searches and comparison on continually updated genomes from NCBI. The public web server and installable docker image are open source and freely available without registration at: https://cagecat.bioinformatics.nl .

ARTS 2.0: feature updates and expansion of the Antibiotic Resistant Target Seeker for comparative genome mining.

Multi-drug resistant pathogens have become a major threat to human health and new antibiotics are urgently needed. Most antibiotics are derived from secondary metabolites produced by bacteria. In order to avoid suicide, these bacteria usually encode resistance genes, in some cases within the biosynthetic gene cluster (BGC) of the respective antibiotic compound. Modern genome mining tools enable researchers to computationally detect and predict BGCs that encode the biosynthesis of secondary metabolites. The major challenge now is the prioritization of the most promising BGCs encoding antibiotics with novel modes of action. A recently developed target-directed genome mining approach allows researchers to predict the mode of action of the encoded compound of an uncharacterized BGC based on the presence of resistant target genes. In 2017, we introduced the 'Antibiotic Resistant Target Seeker' (ARTS). ARTS allows for specific and efficient genome mining for antibiotics with interesting and novel targets by rapidly linking housekeeping and known resistance genes to BGC proximity, duplication and horizontal gene transfer (HGT) events. Here, we present ARTS 2.0 available at http://arts.ziemertlab.com. ARTS 2.0 now includes options for automated target directed genome mining in all bacterial taxa as well as metagenomic data. Furthermore, it enables comparison of similar BGCs from different genomes and their putative resistance genes.

MIBiG 2.0: a repository for biosynthetic gene clusters of known function.

Fueled by the explosion of (meta)genomic data, genome mining of specialized metabolites has become a major technology for drug discovery and studying microbiome ecology. In these efforts, computational tools like antiSMASH have played a central role through the analysis of Biosynthetic Gene Clusters (BGCs). Thousands of candidate BGCs from microbial genomes have been identified and stored in public databases. Interpreting the function and novelty of these predicted BGCs requires comparison with a well-documented set of BGCs of known function. The MIBiG (Minimum Information about a Biosynthetic Gene Cluster) Data Standard and Repository was established in 2015 to enable curation and storage of known BGCs. Here, we present MIBiG 2.0, which encompasses major updates to the schema, the data, and the online repository itself. Over the past five years, 851 new BGCs have been added. Additionally, we performed extensive manual data curation of all entries to improve the annotation quality of our repository. We also redesigned the data schema to ensure the compliance of future annotations. Finally, we improved the user experience by adding new features such as query searches and a statistics page, and enabled direct link-outs to chemical structure databases. The repository is accessible online at https://mibig.secondarymetabolites.org/.

Recent highlights of biosynthetic studies on marine natural products.

Marine bacteria are excellent yet often underexplored sources of structurally unique bioactive natural products. In this review we cover the diversity of marine bacterial biomolecules and highlight recent studies on structurally novel natural products. We include different compound classes and discuss the latest progress related to their biosynthetic pathway analysis and engineering: examples range from fatty acids over terpenes to PKS, NRPS and hybrid PKS-NRPS biomolecules.

AutoMLST: an automated web server for generating multi-locus species trees highlighting natural product potential.

Understanding the evolutionary background of a bacterial isolate has applications for a wide range of research. However generating an accurate species phylogeny remains challenging. Reliance on 16S rDNA for species identification currently remains popular. Unfortunately, this widespread method suffers from low resolution at the species level due to high sequence conservation. Currently, there is now a wealth of genomic data that can be used to yield more accurate species designations via modern phylogenetic methods and multiple genetic loci. However, these often require extensive expertise and time. The Automated Multi-Locus Species Tree (autoMLST) was thus developed to provide a rapid 'one-click' pipeline to simplify this workflow at: https://automlst.ziemertlab.com. This server utilizes Multi-Locus Sequence Analysis (MLSA) to produce high-resolution species trees; this does not preform multi-locus sequence typing (MLST), a related classification method. The resulting phylogenetic tree also includes helpful annotations, such as species clade designations and secondary metabolite counts to aid natural product prospecting. Distinct from currently available web-interfaces, autoMLST can automate selection of reference genomes and out-group organisms based on one or more query genomes. This enables a wide range of researchers to perform rigorous phylogenetic analyses more rapidly compared to manual MLSA workflows.

Mining bacterial genomes to reveal secret synergy.

The medical treatment of infectious diseases often requires combination therapies that blend two molecules to enhance drug efficacy. Nature does the same. In a new article, Mrak et al. identify and functionally characterize natural products from Streptomyces rapamycinicus that show synergistic antifungal activity with the well-known immunosuppressant metabolite rapamycin, produced by the same strain. The genomic co-association of the two biosynthetic gene clusters paves the way toward new strategies to discover synergistic pairs of antibiotics through large-scale genome mining.

Applied evolution: phylogeny-based approaches in natural products research.

Covering: up to 2019Phylogenetic methods become increasingly important in natural product research. The growing amount of genetic data available today is enabling us to infer the evolutionary history of secondary metabolite gene clusters and their encoded compounds. We are starting to understand patterns and mechanisms of how the enormous diversity of chemical compounds produced by nature has evolved and are able to use phylogenetic inference to facilitate functional predictions of involved enzymes. In this review, we highlight how phylogenetic methods can aid natural product discovery and predictions and demonstrate several examples how these have been used in the past. We are featuring a number of easy to use tools that aid tree building and analysis and are providing a short overview how to create and interpret a phylogenetic tree.

Computer-aided re-engineering of nonribosomal peptide and polyketide biosynthetic assembly lines.

Covering: 2014 to 2019Nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs) have been the subject of engineering efforts for multiple decades. Their modular assembly line architecture potentially allows unlocking vast chemical space for biosynthesis. However, attempts thus far are often met with mixed success, due to limited molecular compatibility of the parts used for engineering. Now, new engineering strategies, increases in genomic data, and improved computational tools provide more opportunities for major progress. In this review we highlight some of the challenges and progressive strategies for the re-design of NRPSs & type I PKSs and survey useful computational tools and approaches to attain the ultimate goal of semi-automated and design-based engineering of novel peptide and polyketide products.

The Antibiotic Resistant Target Seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery.

With the rise of multi-drug resistant pathogens and the decline in number of potential new antibiotics in development there is a fervent need to reinvigorate the natural products discovery pipeline. Most antibiotics are derived from secondary metabolites produced by microorganisms and plants. To avoid suicide, an antibiotic producer harbors resistance genes often found within the same biosynthetic gene cluster (BGC) responsible for manufacturing the antibiotic. Existing mining tools are excellent at detecting BGCs or resistant genes in general, but provide little help in prioritizing and identifying gene clusters for compounds active against specific and novel targets. Here we introduce the 'Antibiotic Resistant Target Seeker' (ARTS) available at https://arts.ziemertlab.com. ARTS allows for specific and efficient genome mining for antibiotics with interesting and novel targets. The aim of this web server is to automate the screening of large amounts of sequence data and to focus on the most promising strains that produce antibiotics with new modes of action. ARTS integrates target directed genome mining methods, antibiotic gene cluster predictions and 'essential gene screening' to provide an interactive page for rapid identification of known and putative targets in BGCs.

Comparative genomics reveals phylogenetic distribution patterns of secondary metabolites in Amycolatopsis species.

BACKGROUND: Genome mining tools have enabled us to predict biosynthetic gene clusters that might encode compounds with valuable functions for industrial and medical applications. With the continuously increasing number of genomes sequenced, we are confronted with an overwhelming number of predicted clusters. In order to guide the effective prioritization of biosynthetic gene clusters towards finding the most promising compounds, knowledge about diversity, phylogenetic relationships and distribution patterns of biosynthetic gene clusters is necessary. RESULTS: Here, we provide a comprehensive analysis of the model actinobacterial genus Amycolatopsis and its potential for the production of secondary metabolites. A phylogenetic characterization, together with a pan-genome analysis showed that within this highly diverse genus, four major lineages could be distinguished which differed in their potential to produce secondary metabolites. Furthermore, we were able to distinguish gene cluster families whose distribution correlated with phylogeny, indicating that vertical gene transfer plays a major role in the evolution of secondary metabolite gene clusters. Still, the vast majority of the diverse biosynthetic gene clusters were derived from clusters unique to the genus, and also unique in comparison to a database of known compounds. Our study on the locations of biosynthetic gene clusters in the genomes of Amycolatopsis' strains showed that clusters acquired by horizontal gene transfer tend to be incorporated into non-conserved regions of the genome thereby allowing us to distinguish core and hypervariable regions in Amycolatopsis genomes. CONCLUSIONS: Using a comparative genomics approach, it was possible to determine the potential of the genus Amycolatopsis to produce a huge diversity of secondary metabolites. Furthermore, the analysis demonstrates that horizontal and vertical gene transfer play an important role in the acquisition and maintenance of valuable secondary metabolites. Our results cast light on the interconnections between secondary metabolite gene clusters and provide a way to prioritize biosynthetic pathways in the search and discovery of novel compounds.

An integrated semiconductor device enabling non-optical genome sequencing.

The seminal importance of DNA sequencing to the life sciences, biotechnology and medicine has driven the search for more scalable and lower-cost solutions. Here we describe a DNA sequencing technology in which scalable, low-cost semiconductor manufacturing techniques are used to make an integrated circuit able to directly perform non-optical DNA sequencing of genomes. Sequence data are obtained by directly sensing the ions produced by template-directed DNA polymerase synthesis using all-natural nucleotides on this massively parallel semiconductor-sensing device or ion chip. The ion chip contains ion-sensitive, field-effect transistor-based sensors in perfect register with 1.2 million wells, which provide confinement and allow parallel, simultaneous detection of independent sequencing reactions. Use of the most widely used technology for constructing integrated circuits, the complementary metal-oxide semiconductor (CMOS) process, allows for low-cost, large-scale production and scaling of the device to higher densities and larger array sizes. We show the performance of the system by sequencing three bacterial genomes, its robustness and scalability by producing ion chips with up to 10 times as many sensors and sequencing a human genome.

The evolution of genome mining in microbes - a review.

Covering: 2006 to 2016The computational mining of genomes has become an important part in the discovery of novel natural products as drug leads. Thousands of bacterial genome sequences are publically available these days containing an even larger number and diversity of secondary metabolite gene clusters that await linkage to their encoded natural products. With the development of high-throughput sequencing methods and the wealth of DNA data available, a variety of genome mining methods and tools have been developed to guide discovery and characterisation of these compounds. This article reviews the development of these computational approaches during the last decade and shows how the revolution of next generation sequencing methods has led to an evolution of various genome mining approaches, techniques and tools. After a short introduction and brief overview of important milestones, this article will focus on the different approaches of mining genomes for secondary metabolites, from detecting biosynthetic genes to resistance based methods and "evo-mining" strategies including a short evaluation of the impact of the development of genome mining methods and tools on the field of natural products and microbial ecology.

Sequencing rare marine actinomycete genomes reveals high density of unique natural product biosynthetic gene clusters.

Traditional natural product discovery methods have nearly exhausted the accessible diversity of microbial chemicals, making new sources and techniques paramount in the search for new molecules. Marine actinomycete bacteria have recently come into the spotlight as fruitful producers of structurally diverse secondary metabolites, and remain relatively untapped. In this study, we sequenced 21 marine-derived actinomycete strains, rarely studied for their secondary metabolite potential and under-represented in current genomic databases. We found that genome size and phylogeny were good predictors of biosynthetic gene cluster diversity, with larger genomes rivalling the well-known marine producers in the Streptomyces and Salinispora genera. Genomes in the Micrococcineae suborder, however, had consistently the lowest number of biosynthetic gene clusters. By networking individual gene clusters into gene cluster families, we were able to computationally estimate the degree of novelty each genus contributed to the current sequence databases. Based on the similarity measures between all actinobacteria in the Joint Genome Institute's Atlas of Biosynthetic gene Clusters database, rare marine genera show a high degree of novelty and diversity, with Corynebacterium, Gordonia, Nocardiopsis, Saccharomonospora and Pseudonocardia genera representing the highest gene cluster diversity. This research validates that rare marine actinomycetes are important candidates for exploration, as they are relatively unstudied, and their relatives are historically rich in secondary metabolites.

Evolution-inspired engineering of nonribosomal peptide synthetases.

Many clinically used drugs are derived from or inspired by bacterial natural products that often are produced through nonribosomal peptide synthetases (NRPSs), megasynthetases that activate and join individual amino acids in an assembly line fashion. In this work, we describe a detailed phylogenetic analysis of several bacterial NRPSs that led to the identification of yet undescribed recombination sites within the thiolation (T) domain that can be used for NRPS engineering. We then developed an evolution-inspired "eXchange Unit between T domains" (XUT) approach, which allows the assembly of NRPS fragments over a broad range of GC contents, protein similarities, and extender unit specificities, as demonstrated for the specific production of a proteasome inhibitor designed and assembled from five different NRPS fragments.

A biaryl-linked tripeptide from Planomonospora reveals a widespread class of minimal RiPP gene clusters.

Microbial natural products impress by their bioactivity, structural diversity, and ingenious biosynthesis. While screening the less exploited actinobacterial genus Planomonospora, two cyclopeptides were discovered, featuring an unusual Tyr-His biaryl bridging across a tripeptide scaffold, with the sequences N-acetyl-Tyr-Tyr-His and N-acetyl-Tyr-Phe-His. Planomonospora genomes pointed toward a ribosomal synthesis of the cyclopeptide from a pentapeptide precursor encoded by 18-bp bytA, to our knowledge the smallest coding gene ever reported. Closely linked to bytA is bytO, encoding a cytochrome P450 monooxygenase likely responsible for biaryl installment. In Streptomyces, the bytAO segment was sufficient to direct production of the crosslinked N-acetylated Tyr-Tyr-His tripeptide. Bioinformatic analysis of related cytochrome P450 monooxygenases indicated that they constitute a widespread family of enzymes, and the corresponding genes are closely linked to 5-amino acid coding sequences in approximately 200 (actino)bacterial genomes, all with potential for biaryl linkage between amino acids 1 and 3. We propose the named biarylitides this family of RiPPs.

The Surfactin-Like Lipopeptides From Bacillus spp.: Natural Biodiversity and Synthetic Biology for a Broader Application Range.

Surfactin is a lipoheptapeptide produced by several Bacillus species and identified for the first time in 1969. At first, the biosynthesis of this remarkable biosurfactant was described in this review. The peptide moiety of the surfactin is synthesized using huge multienzymatic proteins called NonRibosomal Peptide Synthetases. This mechanism is responsible for the peptide biodiversity of the members of the surfactin family. In addition, on the fatty acid side, fifteen different isoforms (from C12 to C17) can be incorporated so increasing the number of the surfactin-like biomolecules. The review also highlights the last development in metabolic modeling and engineering and in synthetic biology to direct surfactin biosynthesis but also to generate novel derivatives. This large set of different biomolecules leads to a broad spectrum of physico-chemical properties and biological activities. The last parts of the review summarized the numerous studies related to the production processes optimization as well as the approaches developed to increase the surfactin productivity of Bacillus cells taking into account the different steps of its biosynthesis from gene transcription to surfactin degradation in the culture medium.

Assessing the Efficiency of Cultivation Techniques To Recover Natural Product Biosynthetic Gene Populations from Sediment.

Despite decades of cultivating microorganisms for use in drug discovery, few attempts have been made to measure the extent to which common cultivation techniques have accessed existing chemical space. Metagenomic studies have shown that cultivable bacteria represent a fraction of those that exist in the environment, and that uncultivated populations in sediment have genes that encode for a high diversity of novel natural product (NP) biosynthetic enzymes. Quantifying these genes in both sediment and cultivatable bacterial populations allows us to assess how much diversity is present on nutrient agar and is critical to guiding the trajectory of future NP discovery platforms. Herein, we employed next-generation amplicon sequencing to assess the NP biosynthetic gene populations present in two Lake Huron sediment samples, and compared these with populations from their corresponding cultivatable bacteria. We highlight three findings from our study: (1) after cultivation, we recovered between 7.7% and 23% of three common types of NP biosynthetic genes from the original sediment population; (2) between 76.3% and 91.5% of measured NP biosynthetic genes from nutrient agar have yet to be characterized in known biosynthetic gene cluster databases, indicating that readily cultivatable bacteria harbor the potential to produce new NPs; and (3) even though the predominant taxa present on nutrient media represented some of the major producers of bacterial NPs, the sediment harbored a significantly greater pool of NP biosynthetic genes that could be mined for structural novelty, and these likely belong to taxa that typically have not been represented in microbial drug discovery libraries.

Complete Genome Sequence of Streptomyces sp. Strain BSE7F, a Bali Mangrove Sediment Actinobacterium with Antimicrobial Activities.

The strain Streptomyces sp. BSE7F, a novel Streptomyces strain isolated from Indonesian mangrove sediment, displays antimicrobial activities against Gram-positive bacteria, Gram-negative bacteria, and yeast. Bioinformatic analysis of the genome sequence revealed the occurrence of 22 biosynthetic gene clusters disclosing the secondary metabolite capacity of strain BSE7F.