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Secondary metabolites produced by bacteria and fungi are an important source of antimicrobials and other bioactive compounds. In recent years, genome mining has seen broad applications in identifying and characterizing new compounds as well as in metabolic engineering. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' (https://antismash.secondarymetabolites.org) has assisted researchers in this, both as a web server and a standalone tool. It has established itself as the most widely used tool for identifying and analysing biosynthetic gene clusters (BGCs) in bacterial and fungal genome sequences. Here, we present an entirely redesigned and extended version 5 of antiSMASH. antiSMASH 5 adds detection rules for clusters encoding the biosynthesis of acyl-amino acids, β-lactones, fungal RiPPs, RaS-RiPPs, polybrominated diphenyl ethers, C-nucleosides, PPY-like ketones and lipolanthines. For type II polyketide synthase-encoding gene clusters, antiSMASH 5 now offers more detailed predictions. The HTML output visualization has been redesigned to improve the navigation and visual representation of annotations. We have again improved the runtime of analysis steps, making it possible to deliver comprehensive annotations for bacterial genomes within a few minutes. A new output file in the standard JavaScript object notation (JSON) format is aimed at downstream tools that process antiSMASH results programmatically.

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/.

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.

Natural products originating from microorganisms are frequently used in antimicrobial and anticancer drugs, pesticides, herbicides or fungicides. In the last years, the increasing availability of microbial genome data has made it possible to access the wealth of biosynthetic clusters responsible for the production of these compounds by genome mining. antiSMASH is one of the most popular tools in this field. The antiSMASH database provides pre-computed antiSMASH results for many publicly available microbial genomes and allows for advanced cross-genome searches. The current version 2 of the antiSMASH database contains annotations for 6200 full bacterial genomes and 18,576 bacterial draft genomes and is available at https://antismash-db.secondarymetabolites.org/.

Strain ATCC 31962 was formerly taxonomically classified as Empedobacter haloabium and reported to be the producer of the lipopeptide antibiotic empedopeptin. Here, we report the draft genome sequence of ATCC 31962, which encodes regions that suggest a distinct biosynthetic capacity and suggests its taxonomic reclassification.

For a safe and sustainable environment, effective microbes as biocontrol agents are in high demand. We have isolated a new Bacillus velezensis strain DTU001, investigated its antifungal spectrum, sequenced its genome, and uncovered the production of lipopeptides in HPLC-HRMS analysis. To test the antifungal efficacy, extracts of B. velezensis DTU001 was tested against a range of twenty human or plant pathogenic fungi. We demonstrate that inhibitory potential of B. velezensis DTU001 against selected fungi is superior in comparison to single lipopeptide, either iturin or fengycin. The isolate showed analogous biofilm formation to other closely related Bacilli. To further support the biocontrol properties of the isolate, coculture with Candida albicans demonstrated that B. velezensis DTU001 exhibited excellent antiproliferation effect against C. albicans. In summary, the described isolate is a potential antifungal agent with a broad antifungal spectrum that might assist our aims to avoid hazardous pathogenic fungi and provide alternative to toxicity caused by chemicals.

Kirromycin is the main product of the soil-dwelling Streptomyces collinus Tü 365. The elucidation of the biosynthetic pathway revealed that the antibiotic is synthesised via a unique combination of trans-/cis-AT type I polyketide synthases and non-ribosomal peptide synthetases (PKS I/NRPS). This was the first example of an assembly line integrating the three biosynthetic principles in one pathway. However, information about other enzymes involved in kirromycin biosynthesis remained scarce. In this study, genes encoding tailoring enzymes KirM, KirHVI, KirOI, and KirOII, and the putative crotonyl-CoA reductase/carboxylase KirN were deleted, complemented, and the emerged products analysed by HPLC-HRMS and MS/MS. Derivatives were identified in mutants ΔkirM, ΔkirHVI, ΔkirOI, and ΔkirOII. The products of ΔkirOI, ΔkirOII, and kirHVI were subjected to 2D-NMR for structure elucidation. Our results enabled functional assignment of those enzymes, demonstrating their involvement in kirromycin tailoring. In the ΔkirN mutant, the production of kirromycin was significantly decreased. The obtained data enabled us to clarify the putative roles of the studied enzymes, ultimately allowing us to fill many of the missing gaps in the biosynthesis of the complex antibiotic. Furthermore, this collection of mutants can serve as a toolbox for generation of new kirromycins.

The structural repertoire of bioactive naphthacene quinones is expanded by engineering Streptomyces albus to express the lysolipin minimal polyketide synthase II (PKS II) genes from Streptomyces tendae Tü 4042 (llpD-F) with the corresponding cyclase genes llpCI-CIII. Fermentation of the recombinant strain revealed the two new polyaromatic tridecaketides lysoquinone-TH1 (7, identified) and TH2 (8, postulated structure) as engineered congeners of the dodecaketide lysolipin (1). The chemical structure of 7, a benzo[a]naphthacene-8,13-dione, was elucidated by NMR and HR-MS and confirmed by feeding experiments with [1,2-13C2]-labeled acetate. Lysoquinone-TH1 (7) is a pentangular polyphenol and one example of such rare extended polyaromatic systems of the benz[a]napthacene quinone type produced by the expression of a minimal PKS II in combination with cyclases in an artificial system. While the natural product lysolipin (1) has antimicrobial activity in nM-range, lysoquinone-TH1 (7) showed only minor potency as inhibitor of Gram-positive microorganisms. The bioactivity profiling of lysoquinone-TH1 (7) revealed inhibitory activity towards phosphodiesterase 4 (PDE4), an important target for the treatment in human health like asthma or chronic obstructive pulmonary disease (COPD). These results underline the availability of pentangular polyphenolic structural skeletons from biosynthetic engineering in the search of new chemical entities in drug discovery

Patterns in biological sequences frequently signify interesting features in the underlying molecule. Many tools exist to search for well-known patterns. Less support is available for exploratory analysis, where no well-defined patterns are known yet. PatScanUI (https://patscan.secondarymetabolites.org/) provides a highly interactive web interface to the powerful generic pattern search tool PatScan. The complex PatScan-patterns are created in a drag-and-drop aware interface allowing researchers to do rapid prototyping of the often complicated patterns useful to identifying features of interest.

Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the production of such compounds. Since 2011, the â € antibiotics and secondary metabolite analysis shell - antiSMASH' has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally synthesized and post-translationally modified peptides cluster products, reporting of sequence similarity to proteins encoded in experimentally characterized gene clusters on a per-protein basis and a domain-level alignment tool for comparative analysis of trans-AT polyketide synthase assembly line architectures. Additionally, several usability features have been updated and improved. Together, these improvements make antiSMASH up-to-date with the latest developments in natural product research and will further facilitate computational genome mining for the discovery of novel bioactive molecules.

Ferredoxins are required to supply electrons to the cytochrome P450 enzymes involved in cross-linking reactions during the biosynthesis of the glycopeptide antibiotics balhimycin and vancomycin. However, the biosynthetic gene clusters for these antibiotics contain no ferredoxin- or ferredoxin reductase-like genes. In a search for potential ferredoxin partners for these P450s, here, we report an in silico analysis of the draft genome sequence of the balhimycin producer Amycolatopsis balhimycina, which revealed 11 putative Fe-S-containing ferredoxin genes. We show that two members (balFd-V and balFd-VII), produced as native-like holo-[3Fe-4S] ferredoxins in Escherichia coli, could supply electrons to the P450 OxyB (CYP165B) from both A. balhimycina and the vancomycin producer Amycolatopsis orientalis, and support in vitro turnover of peptidyl carrier protein-bound peptide substrates into monocyclic cross-linked products. These results show that ferredoxins encoded in the antibiotic-producing strain can act in a degenerate manner in supporting the catalytic functions of glycopeptide biosynthetic P450 enzymes from the same as well as heterologous gene clusters

Streptomycetes are known for their inherent ability to produce pharmaceutically relevant secondary metabolites. Discovery of medically useful, yet novel compounds has become a great challenge due to frequent rediscovery of known compounds and a consequent decline in the number of relevant clinical trials in the last decades. A paradigm shift took place when the first whole genome sequences of streptomycetes became available, from which silent or "cryptic" biosynthetic gene clusters (BGCs) were discovered. Cryptic BGCs reveal a so far untapped potential of the microorganisms for the production of novel compounds, which has spurred new efforts in understanding the complex regulation between primary and secondary metabolism. This new trend has been accompanied with development of new computational resources (genome and compound mining tools), generation of various high-quality omics data, establishment of molecular tools and other strain engineering strategies. They all come together to enable systems metabolic engineering of streptomycetes, allowing more systematic and efficient strain development. In this review, we present recent progresses within systems metabolic engineering of streptomycetes for uncovering their hidden potential to produce novel compounds and for the improved production of secondary metabolites.

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.

Secondary metabolites produced by microorganisms are the main source of bioactive compounds that are in use as antimicrobial and anticancer drugs, fungicides, herbicides and pesticides. In the last decade, the increasing availability of microbial genomes has established genome mining as a very important method for the identification of their biosynthetic gene clusters (BGCs). One of the most popular tools for this task is antiSMASH. However, so far, antiSMASH is limited to de novo computing results for user-submitted genomes and only partially connects these with BGCs from other organisms. Therefore, we developed the antiSMASH database, a simple but highly useful new resource to browse antiSMASH-annotated BGCs in the currently 3907 bacterial genomes in the database and perform advanced search queries combining multiple search criteria. antiSMASH-DB is available at http://antismash-db.secondarymetabolites.org/.

CRISPR/Cas9-based genome editing has been one of the major achievements of molecular biology, allowing the targeted engineering of a wide range of genomes. The system originally evolved in prokaryotes as an adaptive immune system against bacteriophage infections. It now sees widespread application in genome engineering workflows, especially using the Streptococcus pyogenes endonuclease Cas9. To utilize Cas9, so-called single guide RNAs (sgRNAs) need to be designed for each target gene. While there are many tools available to design sgRNAs for the popular model organisms, only few tools that allow designing sgRNAs for non-model organisms exist. Here, we present CRISPy-web (http://crispy.secondarymetabolites.org/), an easy to use web tool based on CRISPy to design sgRNAs for any user-provided microbial genome. CRISPy-web allows researchers to interactively select a region of their genome of interest to scan for possible sgRNAs. After checks for potential off-target matches, the resulting sgRNA sequences are displayed graphically and can be exported to text files. All steps and information are accessible from a web browser without the requirement to install and use command line scripts.