Parallelized gene cluster editing illuminates mechanisms of epoxyketone proteasome inhibitor biosynthesis.

Advances in DNA sequencing technology and bioinformatics have revealed the enormous potential of microbes to produce structurally complex specialized metabolites with diverse uses in medicine and agriculture. However, these molecules typically require structural modification to optimize them for application, which can be difficult using synthetic chemistry. Bioengineering offers a complementary approach to structural modification but is often hampered by genetic intractability and requires a thorough understanding of biosynthetic gene function. Expression of specialized metabolite biosynthetic gene clusters (BGCs) in heterologous hosts can surmount these problems. However, current approaches to BGC cloning and manipulation are inefficient, lack fidelity, and can be prohibitively expensive. Here, we report a yeast-based platform that exploits transformation-associated recombination (TAR) for high efficiency capture and parallelized manipulation of BGCs. As a proof of concept, we clone, heterologously express and genetically analyze BGCs for the structurally related nonribosomal peptides eponemycin and TMC-86A, clarifying remaining ambiguities in the biosynthesis of these important proteasome inhibitors. Our results show that the eponemycin BGC also directs the production of TMC-86A and reveal contrasting mechanisms for initiating the assembly of these two metabolites. Moreover, our data shed light on the mechanisms for biosynthesis and incorporation of 4,5-dehydro-l-leucine (dhL), an unusual nonproteinogenic amino acid incorporated into both TMC-86A and eponemycin.

In vitro elucidation of the crucial but complex oxidative tailoring steps in rufomycin biosynthesis enables one pot conversion of rufomycin B to rufomycin C.

The antimycobacterial peptides, rufomycins, have their antibiotic activity conferred by oxidative tailoring of the cyclic peptide. Here we elucidate the roles of cytochrome P450s RufS and RufM in regioselective epoxidation and alkyl oxidation respectively and demonstrate how RufM and RufS create a complex product profile dependent on redox partner availability. Finally, we report the in vitro one pot conversion of rufomycin B to rufomycin C.

A computational framework to explore large-scale biosynthetic diversity.

Genome mining has become a key technology to exploit natural product diversity. Although 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. In the present study, a streamlined computational workflow is provided, consisting of two new software tools: the 'biosynthetic gene similarity clustering and prospecting engine' (BiG-SCAPE), which facilitates fast and interactive sequence similarity network analysis of biosynthetic gene clusters and gene cluster families; and the 'core analysis of syntenic orthologues to prioritize natural product gene clusters' (CORASON), which elucidates phylogenetic relationships within and across these families. BiG-SCAPE is validated by correlating its output to metabolomic data across 363 actinobacterial strains and the discovery potential of CORASON is demonstrated by comprehensively mapping biosynthetic diversity across a range of detoxin/rimosamide-related gene cluster families, culminating in the characterization of seven detoxin analogues.

NeuRiPP: Neural network identification of RiPP precursor peptides.

Significant progress has been made in the past few years on the computational identification of biosynthetic gene clusters (BGCs) that encode ribosomally synthesized and post-translationally modified peptides (RiPPs). This is done by identifying both RiPP tailoring enzymes (RTEs) and RiPP precursor peptides (PPs). However, identification of PPs, particularly for novel RiPP classes remains challenging. To address this, machine learning has been used to accurately identify PP sequences. Current machine learning tools have limitations, since they are specific to the RiPPclass they are trained for and are context-dependent, requiring information about the surrounding genetic environment of the putative PP sequences. NeuRiPP overcomes these limitations. It does this by leveraging the rich data set of high-confidence putative PP sequences from existing programs, along with experimentally verified PPs from RiPP databases. NeuRiPP uses neural network archictectures that are suitable for peptide classification with weights trained on PP datasets. It is able to identify known PP sequences, and sequences that are likely PPs. When tested on existing RiPP BGC datasets, NeuRiPP was able to identify PP sequences in significantly more putative RiPP clusters than current tools while maintaining the same HMM hit accuracy. Finally, NeuRiPP was able to successfully identify PP sequences from novel RiPP classes that were recently characterized experimentally, highlighting its utility in complementing existing bioinformatics tools.

Structural basis for chain release from the enacyloxin polyketide synthase.

Modular polyketide synthases and non-ribosomal peptide synthetases are molecular assembly lines that consist of several multienzyme subunits that undergo dynamic self-assembly to form a functional megacomplex. N- and C-terminal docking domains are usually responsible for mediating the interactions between subunits. Here we show that communication between two non-ribosomal peptide synthetase subunits responsible for chain release from the enacyloxin polyketide synthase, which assembles an antibiotic with promising activity against Acinetobacter baumannii, is mediated by an intrinsically disordered short linear motif and a ß-hairpin docking domain. The structures, interactions and dynamics of these subunits were characterized using several complementary biophysical techniques to provide extensive insights into binding and catalysis. Bioinformatics analyses reveal that short linear motif/ß-hairpin docking domain pairs mediate subunit interactions in numerous non-ribosomal peptide and hybrid polyketide-non-ribosomal peptide synthetases, including those responsible for assembling several important drugs. Short linear motifs and ß-hairpin docking domains from heterologous systems are shown to interact productively, highlighting the potential of such interfaces as tools for biosynthetic engineering.

Pentamycin Biosynthesis in Philippine Streptomyces sp. S816: Cytochrome P450-Catalyzed Installation of the C-14 Hydroxyl Group.

Pentamycin is a polyene antibiotic, registered in Switzerland for the treatment of vaginal candidiasis, trichomoniasis, and mixed infections. Chemical instability has hindered its widespread application and development as a drug. Here, we report the identification of Streptomyces sp. S816, isolated from Philippine mangrove soil, as a pentamycin producer. Genome sequence analysis identified the putative pentamycin biosynthetic gene cluster, which shows a high degree of similarity to the gene cluster responsible for filipin III biosynthesis. The ptnJ gene, which is absent from the filipin III biosynthetic gene cluster, was shown to encode a cytochrome P450 capable of converting filipin III to pentamycin. This confirms that the cluster directs pentamycin biosynthesis, paving the way for biosynthetic engineering approaches to the production of pentamycin analogues. Several other Streptomyces genomes were found to contain ptnJ orthologues clustered with genes encoding polyketide synthases that appear to have similar architectures to those responsible for the assembly of filipin III and pentamycin, suggesting pentamycin production may be common in Streptomyces species.

The antiSMASH database version 2: a comprehensive resource on secondary metabolite biosynthetic gene clusters.

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/.

Rieske non-heme iron-dependent oxygenases catalyse diverse reactions in natural product biosynthesis.

Covering: up to the end of 2017 The roles played by Rieske non-heme iron-dependent oxygenases in natural product biosynthesis are reviewed, with particular focus on experimentally characterised examples. Enzymes belonging to this class are known to catalyse a range of transformations, including oxidative carbocyclisation, N-oxygenation, C-hydroxylation and C-C desaturation. Examples of such enzymes that have yet to be experimentally investigated are also briefly described and their likely functions are discussed.

antiSMASH 4.0-improvements in chemistry prediction and gene cluster boundary identification.

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 '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.

Engineering Transcriptional Regulator Effector Specificity Using Computational Design and In Vitro Rapid Prototyping: Developing a Vanillin Sensor.

The pursuit of circuits and metabolic pathways of increasing complexity and robustness in synthetic biology will require engineering new regulatory tools. Feedback control based on relevant molecules, including toxic intermediates and environmental signals, would enable genetic circuits to react appropriately to changing conditions. In this work, variants of qacR, a tetR family repressor, were generated by computational protein design and screened in a cell-free transcription-translation (TX-TL) system for responsiveness to a new targeted effector. The modified repressors target vanillin, a growth-inhibiting small molecule found in lignocellulosic hydrolysates and other industrial processes. Promising candidates from the in vitro screen were further characterized in vitro and in vivo in a gene circuit. The screen yielded two qacR mutants that respond to vanillin both in vitro and in vivo. While the mutants exhibit some toxicity to cells, presumably due to off-target effects, they are prime starting points for directed evolution toward vanillin sensors with the specifications required for use in a dynamic control loop. We believe this process, a combination of the generation of variants coupled with in vitro screening, can serve as a framework for designing new sensors for other target compounds.

Design and implementation of a biomolecular concentration tracker.

As a field, synthetic biology strives to engineer increasingly complex artificial systems in living cells. Active feedback in closed loop systems offers a dynamic and adaptive way to ensure constant relative activity independent of intrinsic and extrinsic noise. In this work, we use synthetic protein scaffolds as a modular and tunable mechanism for concentration tracking through negative feedback. Input to the circuit initiates scaffold production, leading to colocalization of a two-component system and resulting in the production of an inhibitory antiscaffold protein. Using a combination of modeling and experimental work, we show that the biomolecular concentration tracker circuit achieves dynamic protein concentration tracking in Escherichia coli and that steady state outputs can be tuned.