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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/.

Drug discovery is usually focused on a single protein target; in this process, existing compounds that bind to related proteins are often ignored. We describe ProBiS plugin, extension of our earlier ProBiS-ligands approach, which for a given protein structure allows prediction of its binding sites and, for each binding site, the ligands from similar binding sites in the Protein Data Bank. We developed a new database of precalculated binding site comparisons of about 290000 proteins to allow fast prediction of binding sites in existing proteins. The plugin enables advanced viewing of predicted binding sites, ligands’ poses, and their interactions in three-dimensional graphics. Using the InhA query protein, an enoyl reductase enzyme in the Mycobacterium tuberculosis fatty acid biosynthesis pathway, we predicted its possible ligands and assessed their inhibitory activity experimentally. This resulted in three previously unrecognized inhibitors with novel scaffolds, demonstrating the plugin’s utility in the early drug discovery process.

We report a metabolomic analysis of Streptomyces sp. ID38640, a soil isolate that produces the bacterial RNA polymerase inhibitor pseudouridimycin. The analysis was performed on the wild type, on three newly constructed and seven previously reported mutant strains disabled in different genes required for pseudouridimycin biosynthesis. The results indicate that Streptomyces sp. ID38640 is able to produce, in addition to lydicamycins and deferroxiamines, as previously reported, also the lassopeptide ulleungdin, the non-ribosomal peptide antipain and the osmoprotectant ectoine. The corresponding biosynthetic gene clusters were readily identified in the strain genome. We also detected the known compound pyridindolol, for which we propose a previously unreported biosynthetic gene cluster, as well as three families of unknown metabolites. Remarkably, the levels of most metabolites varied strongly in the different mutant strains, an observation that enabled detection of metabolites unnoticed in the wild type. Systematic investigation of the accumulated metabolites in the ten different pum mutants identified shed further light on pseudouridimycin biosynthesis. We also show that several Streptomyces strains, able to produce pseudouridimycin, have distinct genetic relationship and metabolic profile with ID38640.

Despite an excellent track record, microbial drug discovery suffers from high rates of rediscovery. Better workflows for the rapid investigation of complex extracts are needed to increase throughput and to allow early prioritization of samples. In addition, systematic characterization of poorly explored strains is seldomly performed. Here, we report a metabolomic study of 72 isolates belonging to the rare actinomycete genus Planomonospora , using a workflow of commonly used open access tools to investigate its secondary metabolites. The results reveal a correlation of chemical diversity and strain phylogeny, with classes of metabolites exclusive to certain phylogroups. We were able to identify previously reported Planomonospora metabolites, including the ureylene-containing oligopeptide antipain, the thiopeptide siomycin including new congeners, and the ribosomally synthesized peptides sphaericin and lantibiotic 97518. In addition, we found that Planomonospora strains can produce the siderophore desferrioxamine or a salinichelin-like peptide. Analysis of the genomes of three newly sequenced strains led to the detection of 59 gene cluster families, of which three were connected to products found by LC-MS/MS profiling. This study demonstrates the value of metabolomic studies to investigate poorly explored taxa and provides a first picture of the biosynthetic capabilities of the genus Planomonospora .

Both the soil bacterium Chromobacterium vaccinii and the bacterial endosymbiont Candidatus Burkholderia crenata of the plant Ardisia crenata are producers of FR900359 (FR). This cyclic depsipeptide is a potent and selective Gq protein inhibitor used extensively to investigate the intracellular signaling of G protein coupled receptors (GPCRs). In this study, the metabolomes of both FR producers were investigated and compared using feature-based molecular networking (FBMN). As a result, 30 previously unknown FR derivatives were identified, one-third being unique to C. vaccinii. Guided by MS, a novel FR derivative, FR-6 (compound 1), was isolated, and its structure unambiguously established. In a whole-cell biosensing assay based on detection of dynamic mass redistribution (DMR) as readout for Gq inhibition, FR-6 suppressed Gq signaling with micromolar potency (pIC50 = 5.56). This functional activity was confirmed in radioligand binding assays (pK i = 7.50). This work demonstrates the power of molecular networking, guiding the way to a novel Gq-inhibiting FR derivative and underlining the potency of FR as a Gq inhibitor.

Despite an excellent track record, microbial drug discovery suffers from high rates of re-discovery. Better workflows for the rapid investigation of complex extracts are needed to increase throughput and allow early prioritization of samples. In addition, systematic characterization of poorly explored strains is seldomly performed. Here, we report a metabolomic study of 72 isolates belonging to the rare actinomycete genus Planomonospora, using a workflow of open access tools to investigate its secondary metabolites. The results reveal a correlation of chemical diversity and strain phylogeny, with classes of metabolites exclusive to certain phylogroups. We were able to identify previously reported Planomonospora metabolites, including the ureylene-containing oligopeptide antipain, the thiopeptide siomycin including new congeners and the ribosomally synthesized peptides sphaericin and lantibiotic 97518. In addition, we found that Planomonospora strains can produce the siderophore desferrioxamine or a salinichelin-like peptide. Analysis of the genomes of three newly sequenced strains led to the detection of 47 gene cluster families, of which several were connected to products found by LC-MS/MS profiling. This study demonstrates the value of metabolomic studies to investigate poorly explored taxa and provides a first picture of the biosynthetic capabilities of the genus Planomonospora.

Natural products have provided many molecules to treat and prevent illnesses in humans, animals and plants. While only a small fraction of the existing microbial diversity has been explored for bioactive metabolites, tens of thousands of molecules have been reported in the literature over the past 80 years. Thus, the main challenge in microbial metabolite screening is to avoid the re-discovery of known metabolites in a cost-effective manner. In this perspective, we report and discuss different approaches used in our laboratory over the past few years, ranging from bioactivity-based screening to looking for metabolic rarity in different datasets to deeply investigating a single Streptomyces strain. Our results show that it is possible to find novel chemistry through a limited screening effort, provided that appropriate selection criteria are in place.