Co-occurrence of enzyme domains guides the discovery of an oxazolone synthetase

Non-enzymatic reactions in biogenesis of fungal natural products

Fungi have long been regarded as abundant sources of natural products (NPs) exhibiting significant biological activities. Decades of studies on the biosynthesis of fungal NPs revealed that most of the biosynthetic steps are catalyzed by sophisticated enzymes encoded in biosynthetic gene clusters, whereas some reactions proceed without enzymes. These non-enzymatic reactions complicate biosynthetic analysis of NPs and play important roles in diversifying the structure of the products. Therefore, knowledge on the non-enzymatic reactions is important for elucidating the biosynthetic mechanism. This review focuses on non-enzymatic reactions we recently encountered during biosynthetic studies of four types of NPs (viridicatins, Sch210972, lentopeptins, and lentofuranine).

Multi-omic analysis tools for microbial metabolites prediction

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

Structure-Based Design of Small Imine Reductase Panels for Target Substrates

Biocatalysis is important in the discovery, development, and manufacture of pharmaceuticals. However, the identification of enzymes for target transformations of interest requires major screening efforts. Here, we report a structure-based computational workflow to prioritize protein sequences by a score based on predicted activities on substrates, thereby reducing a resource-intensive laboratory-based biocatalyst screening. We selected imine reductases (IREDs) as a class of biocatalysts to illustrate the application of the computational workflow termed IREDFisher. Validation by using published data showed that IREDFisher can retrieve the best enzymes and increase the hit rate by identifying the top 20 ranked sequences. The power of IREDFisher is confirmed by computationally screening 1400 sequences for chosen reductive amination reactions with different levels of complexity. Highly active IREDs were identified by only testing 20 samples in vitro. Our speed test shows that it only takes 90 min to rank 85 sequences from user input and 30 min for the established IREDFisher database containing 591 IRED sequences. IREDFisher is available as a user-friendly web interface (https://enzymeevolver.com/IREDFisher). IREDFisher enables the rapid discovery of IREDs for applications in synthesis and directed evolution studies, with minimal time and resource expenditure. Future use of the workflow with other enzyme families could be implemented following the modification of the workflow scoring function.

Deciphering the biosynthesis of a novel lipid in Mycobacterium tuberculosis expands the known roles of the nitroreductase superfamily

Mycobacterium tuberculosis's (Mtb) success as a pathogen is due in part to its sophisticated lipid metabolic programs, both catabolic and biosynthetic. Several of Mtb lipids have specific roles in pathogenesis, but the identity and roles of many are unknown. Here, we demonstrated that the tyz gene cluster in Mtb, previously implicated in resistance to oxidative stress and survival in macrophages, encodes the biosynthesis of acyl-oxazolones. Heterologous expression of tyzA (Rv2336), tyzB (Rv2338c) and tyzC (Rv2337c) resulted in the biosynthesis of C12:0-tyrazolone as the predominant compound, and the C12:0-tyrazolone was identified in Mtb lipid extracts. TyzA catalyzed the N-acylation of l-amino acids, with highest specificity for l-Tyr and l-Phe and lauroyl-CoA (kcat/KM = 5.9 ± 0.8 × 103 M-1s-1). In cell extracts, TyzC, a flavin-dependent oxidase (FDO) of the nitroreductase (NTR) superfamily, catalyzed the O2-dependent desaturation of the N-acyl-L-Tyr produced by TyzA, while TyzB, a ThiF homolog, catalyzed its ATP-dependent cyclization. The substrate preference of TyzB and TyzC appear to determine the identity of the acyl-oxazolone. Phylogenetic analyses revealed that the NTR superfamily includes a large number of broadly distributed FDOs, including five in Mtb that likely catalyze the desaturation of lipid species. Finally, TCA1, a molecule with activity against drug-resistant and persistent tuberculosis, failed to inhibit the cyclization activity of TyzB, the proposed secondary target of TCA1. Overall, this study identifies a novel class of Mtb lipids, clarifies the role of a potential drug target, and expands our understanding of the NTR superfamily.

Bacterial natural product discovery by heterologous expression

Natural products have found important applications in the pharmaceutical and agricultural sectors. In bacteria, the genes that encode the biosynthesis of natural products are often colocalized in the genome, forming biosynthetic gene clusters. It has been predicted that only 3% of natural products encoded in bacterial genomes have been discovered thus far, in part because gene clusters may be poorly expressed under laboratory conditions. Heterologous expression can help convert bioinformatics predictions into products. However, challenges remain, such as gene cluster prioritization, cloning of the complete gene cluster, high level expression, product identification, and isolation of products in practical yields. Here we reviewed the literature from the past 5 years (January 2018 to June 2023) to identify studies that discovered natural products by heterologous expression. From the 50 studies identified, we present analyses of the rationale for gene cluster prioritization, cloning methods, biosynthetic class, source taxa, and host choice. Combined, the 50 studies led to the discovery of 63 new families of natural products, supporting heterologous expression as a promising way to access novel chemistry. However, the success rate of natural product detection varied from 11% to 32% based on four large-scale studies that were part of the reviewed literature. The low success rate makes it apparent that much remains to be improved. The potential reasons for failure and points to be considered to improve the chances of success are discussed.

ONE-SENTENCE SUMMARY

At least 63 new families of bacterial natural products were discovered using heterologous expression in the last 5 years, supporting heterologous expression as a promising way to access novel chemistry; however, the success rate is low (11-32%) making it apparent that much remains to be improved-we discuss the potential reasons for failure and points to be considered to improve the chances of success. BioRender was used to generate the graphical abstract figure.

Reactive Azlactone Intermediate Drives Fungal Secondary Metabolite Cross-Pathway Generation

Pathogenic fungi of Aspergillus section Fumigati are known to produce various secondary metabolites. A reported isolation of a compound with an atypical carbon skeleton called fumimycin from A. fumisynnematus prompted us to examine a related fungus, A. lentulus, for production of similar products. Here we report the isolation of fumimycin and a related new racemic compound we named lentofuranine. Detailed analyses revealed that both compounds were assembled by a nonenzymatic condensation of a polyketide intermediate from the terrein biosynthetic pathway and a highly reactive azlactone intermediate produced by an unrelated nonribosomal peptide synthetase carrying a terminal condensation-like domain. While highly reactive azlactone is commonly used in chemical synthesis, its production by a conventional non-metalloenzyme and employment as a biosynthetic pathway intermediate is unprecedented. The observed unusual carbon skeleton formation is likely due to the reactivity of azlactone. Our finding provides another example of a chemical principle being aptly exploited by a biological system.

Integrated Metabolomic-Genomic Workflows Accelerate Microbial Natural Product Discovery

The pairing of analytical chemistry with genomic techniques represents a new wave in natural product chemistry. With an increase in the availability of sequencing and assembly of microbial genomes, interrogation into the biosynthetic capability of producers with valuable secondary metabolites is possible. However, without the development of robust, accessible, and medium to high throughput tools, the bottleneck in pairing metabolic potential and compound isolation will continue. Several innovative approaches have proven useful in the nascent stages of microbial genome-informed drug discovery. Here, we consider a number of these approaches which have led to prioritization of strain targets and have mitigated rediscovery rates. Likewise, we discuss integration of principles of comparative evolutionary studies and retrobiosynthetic predictions to better understand biosynthetic mechanistic details and link genome sequence to structure. Lastly, we discuss advances in engineering, chemistry, and molecular networking and other computational approaches that are accelerating progress in the field of omic-informed natural product drug discovery. Together, these strategies enhance the synergy between cutting edge omics, chemical characterization, and computational technologies that pitch the discovery of natural products with pharmaceutical and other potential applications to the crest of the wave where progress is ripe for rapid advances.

Strategies to access biosynthetic novelty in bacterial genomes for drug discovery

Bacteria provide a rich source of natural products with potential therapeutic applications, such as novel antibiotic classes or anticancer drugs. Bioactivity-guided screening of bacterial extracts and characterization of biosynthetic pathways for drug discovery is now complemented by the availability of large (meta)genomic collections, placing researchers into the postgenomic, big-data era. The progress in next-generation sequencing and the rise of powerful computational tools provide unprecedented insights into unexplored taxa, ecological niches and 'biosynthetic dark matter', revealing diverse and chemically distinct natural products in previously unstudied bacteria. In this Review, we discuss such sources of new chemical entities and the implications for drug discovery with a particular focus on the strategies that have emerged in recent years to identify and access novelty.

Mining genomes to illuminate the specialized chemistry of life

All organisms produce specialized organic molecules, ranging from small volatile chemicals to large gene-encoded peptides, that have evolved to provide them with diverse cellular and ecological functions. As natural products, they are broadly applied in medicine, agriculture and nutrition. The rapid accumulation of genomic information has revealed that the metabolic capacity of virtually all organisms is vastly underappreciated. Pioneered mainly in bacteria and fungi, genome mining technologies are accelerating metabolite discovery. Recent efforts are now being expanded to all life forms, including protists, plants and animals, and new integrative omics technologies are enabling the increasingly effective mining of this molecular diversity.

Hot off the press

A personal selection of 32 recent papers is presented covering various aspects of current developments in bioorganic chemistry and novel natural products such as pyrasplorine A from Aspergillus versicolor.

Phylogenetic analysis of the salinipostin γ-butyrolactone gene cluster uncovers new potential for bacterial signalling-molecule diversity

Bacteria communicate by small-molecule chemicals that facilitate intra- and inter-species interactions. These extracellular signalling molecules mediate diverse processes including virulence, bioluminescence, biofilm formation, motility and specialized metabolism. The signalling molecules produced by members of the phylum Actinobacteria generally comprise γ-butyrolactones, γ-butenolides and furans. The best-known actinomycete γ-butyrolactone is A-factor, which triggers specialized metabolism and morphological differentiation in the genus Streptomyces . Salinipostins A–K are unique γ-butyrolactone molecules with rare phosphotriester moieties that were recently characterized from the marine actinomycete genus Salinispora . The production of these compounds has been linked to the nine-gene biosynthetic gene cluster (BGC) spt . Critical to salinipostin assembly is the γ-butyrolactone synthase encoded by spt9 . Here, we report the surprising distribution of spt9 homologues across 12 bacterial phyla, the majority of which are not known to produce γ-butyrolactones. Further analyses uncovered a large group of spt -like gene clusters outside of the genus Salinispora , suggesting the production of new salinipostin-like diversity. These gene clusters show evidence of horizontal transfer and location-specific recombination among Salinispora strains. The results suggest that γ-butyrolactone production may be more widespread than previously recognized. The identification of new γ-butyrolactone BGCs is the first step towards understanding the regulatory roles of the encoded small molecules in Actinobacteria.