Genome mining has become a key technology to exploit natural product diversity. While initially performed on a single-genome basis, the process is now being scaled up to mine entire genera, strain collections and microbiomes. However, no bioinformatic framework is currently available for effectively analyzing datasets of this size and complexity. Here, we provide a streamlined computational workflow consisting of two new software tools: The 'Biosynthetic Gene Similarity Clustering And Prospecting Engine' (BiG-SCAPE) facilitates fast and interactive sequence similarity network analysis of biosynthetic gene clusters and gene cluster families. 'CORe Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

Actinobacteria encode a wealth of natural product biosynthetic gene clusters (NPGCs), whose systematic study is complicated by numerous repetitive motifs. By combining several metrics we developed a method for global classification of these gene clusters into families (GCFs) and analyzed the biosynthetic capacity of Actinobacteria in 830 genome sequences, including 344 obtained for this project. The GCF network, comprised of 11,422 gene clusters grouped into 4,122 GCFs, was validated in hundreds of strains by correlating confident mass spectrometric detection of known small molecules with the presence/absence of their established biosynthetic gene clusters. The method also linked previously unassigned GCFs to known natural products, an approach that will enable de novo, bioassay-free discovery of novel natural products using large data sets. Extrapolation from the 830-genome dataset reveals that Actinobacteria encode hundreds of thousands of future drug leads, while the strong correlation between phylogeny and GCFs frames a roadmap to efficiently access them.

For more than half a century the pharmaceutical industry has sifted through natural products produced by microbes, uncovering new scaffolds and fashioning them into a broad range of vital drugs. We sought a strategy to reinvigorate the discovery of natural products with distinctive structures using bacterial genome sequencing combined with metabolomics. By correlating genetic content from 178 actinomycete genomes with mass spectrometry-enabled analyses of their exported metabolomes, we paired new secondary metabolites with their biosynthetic gene clusters. We report the use of this new approach to isolate and characterize tambromycin, a new chlorinated natural product, composed of several nonstandard amino acid monomeric units, including a unique pyrrolidine-containing amino acid we name tambroline. Tambromycin shows antiproliferative activity against cancerous human B- and T-cell lines. The discovery of tambromycin via large-scale correlation of gene clusters with metabolites (a.k.a. metabologenomics) illuminates a path for structure-based discovery of natural products at a sharply increased rate.

Genome sequencing has revealed that a far greater number of natural product biosynthetic pathways exist than there are known natural products. To access these molecules directly and deterministically, a new generation of heterologous expression methods is needed. Cell-free protein synthesis has not previously been used to study nonribosomal peptide biosynthesis, and provides a tunable platform with advantages over conventional methods for protein expression. Here, we demonstrate the use of cell-free protein synthesis to biosynthesize a cyclic dipeptide with correct absolute stereochemistry. From a single-pot reaction, we measured the expression of two nonribosomal peptide synthetases larger than 100 kDa, and detected high-level production of a diketopiperazine. Using quantitative LC–MS and synthetically prepared standard, we observed production of this metabolite at levels higher than previously reported from cell-based recombinant expression, approximately 12 mg/L. Overall, this work represents a first step to apply cell-free protein synthesis to discover and characterize new natural products.

BackgroundWith thousands of fungal genomes being sequenced, each genome containing up to 70 secondary metabolite (SM) clusters 30–80 kb in size, breakthrough techniques are needed to characterize this SM wealth.ResultsHere we describe a novel system-level methodology for unbiased cloning of intact large SM clusters from a single fungal genome for one-step transformation and expression in a model host. All 56 intact SM clusters from Aspergillus terreus were individually captured in self-replicating fungal artificial chromosomes (FACs) containing both the E. coli F replicon and an Aspergillus autonomously replicating sequence (AMA1). Candidate FACs were successfully shuttled between E. coli and the heterologous expression host A. nidulans. As proof-of-concept, an A. nidulans FAC strain was characterized in a novel liquid chromatography-high resolution mass spectrometry (LC-HRMS) and data analysis pipeline, leading to the discovery of the A. terreus astechrome biosynthetic machinery.ConclusionThe method we present can be used to capture the entire set of intact SM gene clusters and/or pathways from fungal species for heterologous expression in A. nidulans and natural product discovery.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1561-x) contains supplementary material, which is available to authorized users.