PKMiner: a database for exploring type II polyketide synthases

Metabolic Engineering and Synthetic Biology Approaches for the Heterologous Production of Aromatic Polyketides

Polyketides are a diverse set of natural products with versatile applications as pharmaceuticals, nutraceuticals, and cosmetics, to name a few. Of several types of polyketides, aromatic polyketides comprising type II and III polyketides contain many chemicals important for human health such as antibiotics and anticancer agents. Most aromatic polyketides are produced from soil bacteria or plants, which are difficult to engineer and grow slowly in industrial settings. To this end, metabolic engineering and synthetic biology have been employed to efficiently engineer heterologous model microorganisms for enhanced production of important aromatic polyketides. In this review, we discuss the recent advancement in metabolic engineering and synthetic biology strategies for the production of type II and type III polyketides in model microorganisms. Future challenges and prospects of aromatic polyketide biosynthesis by synthetic biology and enzyme engineering approaches are also discussed.

The Natural Product Domain Seeker version 2 (NaPDoS2) webtool relates ketosynthase phylogeny to biosynthetic function

The Natural Product Domain Seeker (NaPDoS) webtool detects and classifies ketosynthase (KS) and condensation domains from genomic, metagenomic, and amplicon sequence data. Unlike other tools, a phylogeny-based classification scheme is used to make broader predictions about the polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) genes in which these domains are found. NaPDoS is particularly useful for the analysis of incomplete biosynthetic genes or gene clusters, as are often observed in poorly assembled genomes and metagenomes, or when loci are not clustered, as in eukaryotic genomes. To help support the growing interest in sequence-based analyses of natural product biosynthetic diversity, here we introduce version 2 of the webtool, NaPDoS2, available at http://napdos.ucsd.edu/napdos2. This update includes the addition of 1417 KS sequences, representing a major expansion of the taxonomic and functional diversity represented in the webtool database. The phylogeny-based KS classification scheme now recognizes 41 class and subclass assignments, including new type II PKS subclasses. Workflow modifications accelerate run times, allowing larger datasets to be analyzed. In addition, default parameters were established using statistical validation tests to maximize KS detection and classification accuracy while minimizing false positives. We further demonstrate the applications of NaPDoS2 to assess PKS biosynthetic potential using genomic, metagenomic, and PCR amplicon datasets. These examples illustrate how NaPDoS2 can be used to predict biosynthetic potential and detect genes involved in the biosynthesis of specific structure classes or new biosynthetic mechanisms.

Spectrum of deep learning algorithms in drug discovery

Deep learning (DL) algorithms are a subset of machine learning algorithms with the aim of modeling complex mapping between a set of elements and their classes. In parallel to the advance in revealing the molecular bases of diseases, a notable innovation has been undertaken to apply DL in data/libraries management, reaction optimizations, differentiating uncertainties, molecule constructions, creating metrics from qualitative results, and prediction of structures or interactions. From source identification to lead discovery and medicinal chemistry of the drug candidate, drug delivery, and modification, the challenges can be subjected to artificial intelligence algorithms to aid in the generation and interpretation of data. Discovery and design approach, both demand automation, large data management and data fusion by the advance in high-throughput mode. The application of DL can accelerate the exploration of drug mechanisms, finding novel indications for existing drugs (drug repositioning), drug development, and preclinical and clinical studies. The impact of DL in the workflow of drug discovery, design, and their complementary tools are highlighted in this review. Additionally, the type of DL algorithms used for this purpose, and their pros and cons along with the dominant directions of future research are presented.

Screening of Antibiotic Gene Clusters in Microorganisms Isolated from Wood

The biosphere of Earth is made up of a variety of ecosystems governed by complex biological interactions, some of them mediated by microbial bioactive secondary metabolites. These metabolites such as antibiotics (e.g., polyketides and nonribosomal peptides) have been receiving increasing attention, due to their multiple pharmaceutical uses. Besides, antibiotic resistance is on the rise, and it is currently regarded as one of the greatest threats to global human health. The screening of novel antimicrobial polyketides and nonribosomal peptides in poorly studied ecosystems is an interesting alternative to address the problem of antibiotic resistance. This chapter updates a molecular method to identify antibiotics gene clusters and their subsequent production and activity validation. On the one hand, a PCR method based on degenerated primers for nonribosomal peptide synthases (NRPS) and the polyketide synthases (PKS) genes is used as an initial fast screening. On the other hand, a bioassay-based method is the protocol selected for the production confirmation and antibacterial effect estimation. These methods are applied to screen Actinobacteria and Penicillium species as main antibiotic producers isolated from wood.

A Machine Learning Bioinformatics Method to Predict Biological Activity from Biosynthetic Gene Clusters

Research in natural products, the genetically encoded small molecules produced by organisms in an idiosyncratic fashion, deals with molecular structure, biosynthesis, and biological activity. Bioinformatics analyses of microbial genomes can successfully reveal the genetic instructions, biosynthetic gene clusters, that produce many natural products. Genes to molecule predictions made on biosynthetic gene clusters have revealed many important new structures. There is no comparable method for genes to biological activity predictions. To address this missing pathway, we developed a machine learning bioinformatics method for predicting a natural product's antibiotic activity directly from the sequence of its biosynthetic gene cluster. We trained commonly used machine learning classifiers to predict antibacterial or antifungal activity based on features of known natural product biosynthetic gene clusters. We have identified classifiers that can attain accuracies as high as 80% and that have enabled the identification of biosynthetic enzymes and their corresponding molecular features that are associated with antibiotic activity.

Harnessing the natural pool of polyketide and non-ribosomal peptide family: A route map towards novel drug development

Emergence of communicable and non-communicable diseases possess health challenge to millions of people worldwide and is a major threat to the economic and social development in the coming century. The occurrence of recent pandemic, SARS-CoV-2 caused by lethal severe acute respiratory syndrome coronavirus 2 is one such example. Rapid research and development of drugs for the treatment and management of these diseases has been an incredibly challenging task for the pharmaceutical industry. Although, substantial focus has been made in the discovery of therapeutic compounds from natural sources having significant medicinal potential, their synthesis has shown a slow progress. Hence, the discovery of new targets by the application of the latest biotechnological and synthetic biology approaches is very much the need of the hour. Polyketides (PKs) and non-ribosomal peptides (NRPs) found in bacteria, fungi and plants are a large diverse family of natural products synthesized by two classes of enzymes: polyketide synthases (PKS) and non-ribosomal peptide synthetases (NRPS). These enzymes possess immense biomedical potential due to their simple architecture, catalytic capacity, as well as diversity. With the advent of latest in-silico and in-vitro strategies, these enzymes and their related metabolic pathways, if targeted, can contribute highly towards the biosynthesis of an array of potentially natural drug leads that have antagonist effects on biopolymers associated with various human diseases. In the face of the rising threat from the multidrug-resistant pathogens, this will further open new avenues for the discovery of novel and improved drugs by combining the natural and the synthetic approaches. This review discusses the relevance of polyketides and non-ribosomal peptides and the improvement strategies for the development of their derivatives and scaffolds, and how they will be beneficial to the future bioprospecting and drug discovery.

DeepRiPP integrates multiomics data to automate discovery of novel ribosomally synthesized natural products

Microbial natural products represent a rich resource of evolved chemistry that forms the basis for the majority of pharmacotherapeutics. Ribosomally synthesized and posttranslationally modified peptides (RiPPs) are a particularly interesting class of natural products noted for their unique mode of biosynthesis and biological activities. Analyses of sequenced microbial genomes have revealed an enormous number of biosynthetic loci encoding RiPPs but whose products remain cryptic. In parallel, analyses of bacterial metabolomes typically assign chemical structures to only a minority of detected metabolites. Aligning these 2 disparate sources of data could provide a comprehensive strategy for natural product discovery. Here we present DeepRiPP, an integrated genomic and metabolomic platform that employs machine learning to automate the selective discovery and isolation of novel RiPPs. DeepRiPP includes 3 modules. The first, NLPPrecursor, identifies RiPPs independent of genomic context and neighboring biosynthetic genes. The second module, BARLEY, prioritizes loci that encode novel compounds, while the third, CLAMS, automates the isolation of their corresponding products from complex bacterial extracts. DeepRiPP pinpoints target metabolites using large-scale comparative metabolomics analysis across a database of 10,498 extracts generated from 463 strains. We apply the DeepRiPP platform to expand the landscape of novel RiPPs encoded within sequenced genomes and to discover 3 novel RiPPs, whose structures are exactly as predicted by our platform. By building on advances in machine learning technologies, DeepRiPP integrates genomic and metabolomic data to guide the isolation of novel RiPPs in an automated manner.

Exploration and genome mining of natural products from marine Streptomyces

Marine Streptomyces sp. are an important source of bioactive compounds owing to their unique habitats and metabolic pathways. Whole-genome sequencing and bioinformatics analyses have shown that the potential of synthesizing secondary metabolites from marine-derived Streptomyces has been substantially underestimated. Genome mining is an integrated strategy used to discover natural products based on gene cluster sequences and biosynthetic pathways. Its emergence has greatly enhanced the discovery of natural compounds from marine Streptomyces, thereby yielding a large number of bioactive molecules with novel structures and potent activities. In this review, we briefly summarize the current applications of genome mining in marine Streptomyces, such as bioinformatics-based optimization of culture conditions, ribosome engineering, control of regulatory networks, heterologous expression of biosynthetic gene cluster, and combinatorial biosynthesis of natural compounds. Furthermore, we discuss the factors hindering the utilization of marine-derived natural products and conclude with the prospects for this technique.

A metagenomic strategy for harnessing the chemical repertoire of the human microbiome

Extensive progress has been made in determining the effects of the microbiome on human physiology and disease, but the underlying molecules and mechanisms governing these effects remain largely unexplored. Here, we combine a new computational algorithm with synthetic biology to access biologically active small molecules encoded directly in human microbiome–derived metagenomic sequencing data. We discover that members of a clinically used class of molecules are widely encoded in the human microbiome and that they exert potent antibacterial activities against neighboring microbes, implying a possible role in niche competition and host defense. Our approach paves the way toward a systematic unveiling of the chemical repertoire encoded by the human microbiome and provides a generalizable platform for discovering molecular mediators of microbiome-host and microbiome-microbiome interactions.

PRISM 3: expanded prediction of natural product chemical structures from microbial genomes

Microbial natural products represent a rich resource of pharmaceutically and industrially important compounds. Genome sequencing has revealed that the majority of natural products remain undiscovered, and computational methods to connect biosynthetic gene clusters to their corresponding natural products therefore have the potential to revitalize natural product discovery. Previously, we described PRediction Informatics for Secondary Metabolomes (PRISM), a combinatorial approach to chemical structure prediction for genetically encoded nonribosomal peptides and type I and II polyketides. Here, we present a ground-up rewrite of the PRISM structure prediction algorithm to derive prediction of natural products arising from non-modular biosynthetic paradigms. Within this new version, PRISM 3, natural product scaffolds are modeled as chemical graphs, permitting structure prediction for aminocoumarins, antimetabolites, bisindoles and phosphonate natural products, and building upon the addition of ribosomally synthesized and post-translationally modified peptides. Further, with the addition of cluster detection for 11 new cluster types, PRISM 3 expands to detect 22 distinct natural product cluster types. Other major modifications to PRISM include improved sequence input and ORF detection, user-friendliness and output. Distribution of PRISM 3 over a 300-core server grid improves the speed and capacity of the web application. PRISM 3 is available at http://magarveylab.ca/prism/.

Within-Species Genomic Variation and Variable Patterns of Recombination in the Tetracycline Producer Streptomyces rimosus

Streptomyces rimosus is best known as the primary source of the tetracycline class of antibiotics, most notably oxytetracycline, which have been widely used against many gram-positive and gram-negative pathogens and protozoan parasites. However, despite the medical and agricultural importance of S. rimosus, little is known of its evolutionary history and genome dynamics. In this study, we aim to elucidate the pan-genome characteristics and phylogenetic relationships of 32 S. rimosus genomes. The S. rimosus pan-genome contains more than 22,000 orthologous gene clusters, and approximately 8.8% of these genes constitutes the core genome. A large part of the accessory genome is composed of 9,646 strain-specific genes. S. rimosus exhibits an open pan-genome (decay parameter α = 0.83) and high gene diversity between strains (genomic fluidity φ = 0.12). We also observed strain-level variation in the distribution and abundance of biosynthetic gene clusters (BGCs) and that each individual S. rimosus genome has a unique repertoire of BGCs. Lastly, we observed variation in recombination, with some strains donating or receiving DNA more often than others, strains that tend to frequently recombine with specific partners, genes that often experience recombination more than others, and variable sizes of recombined DNA sequences. We conclude that the high levels of inter-strain genomic variation in S. rimosus is partly explained by differences in recombination among strains. These results have important implications on current efforts for natural drug discovery, the ecological role of strain-level variation in microbial populations, and addressing the fundamental question of why microbes have pan-genomes.

Sequence-based classification of type II polyketide synthase biosynthetic gene clusters for antiSMASH

The software antiSMASH examines microbial genome data to identify and analyze biosynthetic gene clusters for a wide range of natural products. So far, type II polyketide synthase (PKS) gene clusters could only be identified, but no detailed predictions for type II PKS gene clusters could be provided. In this study, an antiSMASH module for analyzing type II PKS gene clusters has been developed. The module detects genes/proteins in the type II PKS gene cluster involved with polyketide biosynthesis and is able to make predictions about the aromatic polyketide product. Predictions include the putative starter unit, the number of malonyl elongations during polyketide biosynthesis, the putative class and the molecular weight of the product. Furthermore, putative cyclization patterns are predicted. The accuracy of the predictions generated with the new PKSII antiSMASH module was evaluated using a leave-one-out cross validation. The prediction module is available in antiSMASH version 5 at https://antismash.secondarymetabolites.org .

Post-PKS enzyme complexes

This review highlights the protein–protein interactions between type II post-PKS tailoring enzymes with an emphasis on gilvocarcin and mithramycin.

Biosynthetic Baeyer-Villiger Chemistry Enables Access to Two Anthracene Scaffolds from a Single Gene Cluster in Deep-Sea-Derived Streptomyces olivaceus SCSIO T05

Four known compounds, rishirilide B (1), rishirilide C (2), lupinacidin A (3), and galvaquinone B (4), representing two anthracene scaffolds typical of aromatic polyketides, were isolated from a culture of the deep-sea-derived Streptomyces olivaceus SCSIO T05. From the S. olivaceus producer was cloned and sequenced the rsd biosynthetic gene cluster (BGC) that drives rishirilide biosynthesis. The structural gene rsdK₂ inactivation and heterologous expression of the rsd BGC confirmed the single rsd BGC encodes construction of 1-4 and, thus, accounts for two anthracene scaffolds. Precursor incubation experiments with ¹³C-labeled acetate revealed that a Baeyer-Villiger-type rearrangement plays a central role in construction of 1-4. Two luciferase monooxygenase components, along with a reductase component, are presumably involved in the Baeyer-Villiger-type rearrangement reaction enabling access to the two anthracene scaffold variants. Engineering of the rsd BGC unveiled three SARP family transcriptional regulators, enhancing anthracene production. Inactivation of rsdR₄, a MarR family transcriptional regulator, failed to impact production of 1-4, although production of 3 was slightly improved; most importantly rsdR₄ inactivation led to the new adduct 6 in high titer. Notably, inactivation of rsdH, a putative amidohydrolase, substantially improved the overall titers of 1-4 by more than 4-fold.

Structural and genetic analysis of START superfamily protein MSMEG_0129 from Mycobacterium smegmatis

Mycobacterium tuberculosis is a notorious pathogen that continues to threaten human health. Rv0164, an antigen of both T- and B cells conserved across mycobacteria, and MSMEG_0129, its close homolog in Mycobacterium smegmatis, are predicted members of the START domain superfamily, but their molecular function is unknown. Here, gene knockout studies demonstrate MSMEG_0129 is essential for bacterial growth, suggesting Rv0164 may be a potential drug target. The MSMEG_0129 crystal structure determined at 1.95 Å reveals a fold similar to that in polyketide aromatase/cyclases ZhuI and TcmN from Streptomyces sp. Structural comparisons and docking simulations, however, infer that MSMEG_0129 and Rv0164 are unlikely to catalyze polyketide aromatization/cyclization, but probably play an irreplaceable role during mycobacterial growth, for example, in lipid transfer during cell envelope synthesis.

The Use of ClusterMine360 for the Analysis of Polyketide and Nonribosomal Peptide Biosynthetic Pathways

Polyketides and nonribosomal peptides constitute two large families of microbial natural products. Over the past 20 years a broad range of microbial polyketide and nonribosomal peptide biosynthetic pathways have been characterized leading to a surfeit of genetic data on polyketide and nonribosomal peptide biosynthesis. We developed the ClusterMine360 database, which stores the antiSMASH-based annotation of gene clusters in the NCBI database, linking the structure of the natural product to the biosynthetic gene cluster. This database is searchable and enables the user to access multiple sequence files for phylogenetic analysis of polyketide and nonribosomal peptide biosynthetic genes. Herein we describe how to add compound families and gene clusters to the database and search it using key words or structures to identify specific gene clusters. We also describe how to download multiple sequence files for specific catalytic domains from polyketide and nonribosomal peptide biosynthesis.

Genomic charting of ribosomally synthesized natural product chemical space facilitates targeted mining

Microbial natural products are an evolved resource of bioactive small molecules, which form the foundation of many modern therapeutic regimes. Ribosomally synthesized and posttranslationally modified peptides (RiPPs) represent a class of natural products which have attracted extensive interest for their diverse chemical structures and potent biological activities. Genome sequencing has revealed that the vast majority of genetically encoded natural products remain unknown. Many bioinformatic resources have therefore been developed to predict the chemical structures of natural products, particularly nonribosomal peptides and polyketides, from sequence data. However, the diversity and complexity of RiPPs have challenged systematic investigation of RiPP diversity, and consequently the vast majority of genetically encoded RiPPs remain chemical "dark matter." Here, we introduce an algorithm to catalog RiPP biosynthetic gene clusters and chart genetically encoded RiPP chemical space. A global analysis of 65,421 prokaryotic genomes revealed 30,261 RiPP clusters, encoding 2,231 unique products. We further leverage the structure predictions generated by our algorithm to facilitate the genome-guided discovery of a molecule from a rare family of RiPPs. Our results provide the systematic investigation of RiPP genetic and chemical space, revealing the widespread distribution of RiPP biosynthesis throughout the prokaryotic tree of life, and provide a platform for the targeted discovery of RiPPs based on genome sequencing.

A Validated HPTLC Densitometric Method for Simultaneous Determination of Evernic and Usnic Acids in Four Usnea Species and Comparison of Their Antioxidant Potential

A simple, sensitive and precise high-performance thin-layer chromatography method with densitometric detection was used for simultaneous determination of evernic (EV) and (+)-usnic acids (USN) in Usnea aciculifera (UA), U. ghattensis (UG), U. longissima (UL) and U. stigmatoides (US). This method was also validated according to the ICH guidelines. Separation and quantification was performed with the mobile phase toluene-1, 4-dioxane-formic acid (18:4.5:0.2, v/v/v) on silica gel 60F254 plates. The linearity for EV and USN was found in the 200-600 ng/band range. The limit of detection for EV and USN was 51.56 and 32.59 ng/band, while the limit of quantification was 156.23 and 98.76 ng/band, respectively. Intra- and interday precisions (n = 6) for EV and USN were 0.70-1.89 and 0.50-0.76 (%RSD), and 1.56-1.60 and 1.54-1.99 (%RSD), respectively. The mean percent recoveries were 99.66 and 99.87%, respectively, for EV and USN. However, USN was estimated in all four Usnea species but EV only in two species with varied quantity. Comparative antioxidant activity revealed that US is a better free radical scavenger in comparison with other three Usnea species. Furthermore, these results indicated that USN and EV are not solely responsible for antioxidant potential, but it may be due to synergistic effect.

Synthetic biology to access and expand nature's chemical diversity

Bacterial genomes encode the biosynthetic potential to produce hundreds of thousands of complex molecules with diverse applications, from medicine to agriculture and materials. Accessing these natural products promises to reinvigorate drug discovery pipelines and provide novel routes to synthesize complex chemicals. The pathways leading to the production of these molecules often comprise dozens of genes spanning large areas of the genome and are controlled by complex regulatory networks with some of the most interesting molecules being produced by non-model organisms. In this Review, we discuss how advances in synthetic biology--including novel DNA construction technologies, the use of genetic parts for the precise control of expression and for synthetic regulatory circuits--and multiplexed genome engineering can be used to optimize the design and synthesis of pathways that produce natural products.

In silico methods for linking genes and secondary metabolites: The way forward

In silico methods for linking genomic space to chemical space have played a crucial role in genomics driven discovery of new natural products as well as biosynthesis of altered natural products by engineering of biosynthetic pathways. Here we give an overview of available computational tools and then briefly describe a novel computational framework, namely retro-biosynthetic enumeration of biosynthetic reactions, which can add to the repertoire of computational tools available for connecting natural products to their biosynthetic gene clusters. Most of the currently available bioinformatics tools for analysis of secondary metabolite biosynthetic gene clusters utilize the “Genes to Metabolites” approach. In contrast to the “Genes to Metabolites” approach, the “Metabolites to Genes” or retro-biosynthetic approach would involve enumerating the various biochemical transformations or enzymatic reactions which would generate the given chemical moiety starting from a set of precursor molecules and identifying enzymatic domains which can potentially catalyze the enumerated biochemical transformations. In this article, we first give a brief overview of the presently available in silico tools and approaches for analysis of secondary metabolite biosynthetic pathways. We also discuss our preliminary work on development of algorithms for retro-biosynthetic enumeration of biochemical transformations to formulate a novel computational method for identifying genes associated with biosynthesis of a given polyketide or nonribosomal peptide.

Plant pathogenic anaerobic bacteria use aromatic polyketides to access aerobic territory

Around 25% of vegetable food is lost worldwide because of infectious plant diseases, including microbe-induced decay of harvested crops. In wet seasons and under humid storage conditions, potato tubers are readily infected and decomposed by anaerobic bacteria (Clostridium puniceum). We found that these anaerobic plant pathogens harbor a gene locus (type II polyketide synthase) to produce unusual polyketide metabolites (clostrubins) with dual functions. The clostrubins, which act as antibiotics against other microbial plant pathogens, enable the anaerobic bacteria to survive an oxygen-rich plant environment.

Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM)

Microbial natural products are an invaluable source of evolved bioactive small molecules and pharmaceutical agents. Next-generation and metagenomic sequencing indicates untapped genomic potential, yet high rediscovery rates of known metabolites increasingly frustrate conventional natural product screening programs. New methods to connect biosynthetic gene clusters to novel chemical scaffolds are therefore critical to enable the targeted discovery of genetically encoded natural products. Here, we present PRISM, a computational resource for the identification of biosynthetic gene clusters, prediction of genetically encoded nonribosomal peptides and type I and II polyketides, and bio- and cheminformatic dereplication of known natural products. PRISM implements novel algorithms which render it uniquely capable of predicting type II polyketides, deoxygenated sugars, and starter units, making it a comprehensive genome-guided chemical structure prediction engine. A library of 57 tailoring reactions is leveraged for combinatorial scaffold library generation when multiple potential substrates are consistent with biosynthetic logic. We compare the accuracy of PRISM to existing genomic analysis platforms. PRISM is an open-source, user-friendly web application available at http://magarveylab.ca/prism/.

Actinomycetes from the South China Sea sponges: isolation, diversity, and potential for aromatic polyketides discovery

Marine sponges often harbor dense and diverse microbial communities including actinobacteria. To date no comprehensive investigation has been performed on the culturable diversity of the actinomycetes associated with South China Sea sponges. Structurally novel aromatic polyketides were recently discovered from marine sponge-derived Streptomyces and Saccharopolyspora strains, suggesting that sponge-associated actinomycetes can serve as a new source of aromatic polyketides. In this study, a total of 77 actinomycete strains were isolated from 15 South China Sea sponge species. Phylogenetic characterization of the isolates based on 16S rRNA gene sequencing supported their assignment to 12 families and 20 genera, among which three rare genera (Marihabitans, Polymorphospora, and Streptomonospora) were isolated from marine sponges for the first time. Subsequently, β-ketoacyl synthase (KS_α) gene was used as marker for evaluating the potential of the actinomycete strains to produce aromatic polyketides. As a result, KS_α gene was detected in 35 isolates related to seven genera (Kocuria, Micromonospora, Nocardia, Nocardiopsis, Saccharopolyspora, Salinispora, and Streptomyces). Finally, 10 strains were selected for small-scale fermentation, and one angucycline compound was detected from the culture extract of Streptomyces anulatus strain S71. This study advanced our knowledge of the sponge-associated actinomycetes regarding their diversity and potential in producing aromatic polyketides.

Expanding our understanding of sequence-function relationships of type II polyketide biosynthetic gene clusters: bioinformatics-guided identification of Frankiamicin A from Frankia sp. EAN1pec

A large and rapidly increasing number of unstudied “orphan” natural product biosynthetic gene clusters are being uncovered in sequenced microbial genomes. An important goal of modern natural products research is to be able to accurately predict natural product structures and biosynthetic pathways from these gene cluster sequences. This requires both development of bioinformatic methods for global analysis of these gene clusters and experimental characterization of select products produced by gene clusters with divergent sequence characteristics. Here, we conduct global bioinformatic analysis of all available type II polyketide gene cluster sequences and identify a conserved set of gene clusters with unique ketosynthase α/β sequence characteristics in the genomes of Frankia species, a group of Actinobacteria with underexploited natural product biosynthetic potential. Through LC-MS profiling of extracts from several Frankia species grown under various conditions, we identified Frankia sp. EAN1pec as producing a compound with spectral characteristics consistent with the type II polyketide produced by this gene cluster. We isolated the compound, a pentangular polyketide which we named frankiamicin A, and elucidated its structure by NMR and labeled precursor feeding. We also propose biosynthetic and regulatory pathways for frankiamicin A based on comparative genomic analysis and literature precedent, and conduct bioactivity assays of the compound. Our findings provide new information linking this set of Frankia gene clusters with the compound they produce, and our approach has implications for accurate functional prediction of the many other type II polyketide clusters present in bacterial genomes.

A roadmap for natural product discovery based on large-scale genomics and metabolomics

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.

In silico tools for the analysis of antibiotic biosynthetic pathways

Natural products of bacteria and fungi are the most important source for antimicrobial drug leads. For decades, such compounds were exclusively found by chemical/bioactivity-guided screening approaches. The rapid progress in sequencing technologies only recently allowed the development of novel screening methods based on the genome sequences of potential producing organisms. The basic principle of such genome mining approaches is to identify genes, which are involved in the biosynthesis of such molecules, and to predict the products of the identified pathways. Thus, bioinformatics methods and tools are crucial for genome mining. In this review, a comprehensive overview is given on programs and databases for the identification and analysis of antibiotic biosynthesis gene clusters in genomic data.

Bioinformatics tools for genome mining of polyketide and non-ribosomal peptides

Microbial natural products have played a key role in the development of clinical agents in nearly all therapeutic areas. Recent advances in genome sequencing have revealed that there is an incredible wealth of new polyketide and non-ribosomal peptide natural product diversity to be mined from genetic data. The diversity and complexity of polyketide and non-ribosomal peptide biosynthesis has required the development of unique bioinformatics tools to identify, annotate, and predict the structures of these natural products from their biosynthetic gene clusters. This review highlights and evaluates web-based bioinformatics tools currently available to the natural product community for genome mining to discover new polyketides and non-ribosomal peptides.

Systems biology and biotechnology of Streptomyces species for the production of secondary metabolites

Streptomyces species continue to attract attention as a source of novel medicinal compounds. Despite a long history of studies on these microorganisms, they still have many biochemical mysteries to be elucidated. Investigations of novel secondary metabolites and their biosynthetic gene clusters have been more systematized with high-throughput techniques through inspections of correlations among components of the primary and secondary metabolisms at the genome scale. Moreover, up-to-date information on the genome of Streptomyces species with emphasis on their secondary metabolism has been collected in the form of databases and knowledgebases, providing predictive information and enabling one to explore experimentally unrecognized biological spaces of secondary metabolism. Herein, we review recent trends in the systems biology and biotechnology of Streptomyces species.

DoBISCUIT: a database of secondary metabolite biosynthetic gene clusters

This article introduces DoBISCUIT (Database of BIoSynthesis clusters CUrated and InTegrated, http://www.bio.nite.go.jp/pks/), a literature-based, manually curated database of gene clusters for secondary metabolite biosynthesis. Bacterial secondary metabolites often show pharmacologically important activities and can serve as lead compounds and/or candidates for drug development. Biosynthesis of each secondary metabolite is catalyzed by a number of enzymes, usually encoded by a gene cluster. Although many scientific papers describe such gene clusters, the gene information is not always described in a comprehensive manner and the related information is rarely integrated. DoBISCUIT integrates the latest literature information and provides standardized gene/module/domain descriptions related to the gene clusters.

ClusterMine360: a database of microbial PKS/NRPS biosynthesis

ClusterMine360 (http://www.clustermine360.ca/) is a database of microbial polyketide and non-ribosomal peptide gene clusters. It takes advantage of crowd-sourcing by allowing members of the community to make contributions while automation is used to help achieve high data consistency and quality. The database currently has >200 gene clusters from >185 compound families. It also features a unique sequence repository containing >10 000 polyketide synthase/non-ribosomal peptide synthetase domains. The sequences are filterable and downloadable as individual or multiple sequence FASTA files. We are confident that this database will be a useful resource for members of the polyketide synthases/non-ribosomal peptide synthetases research community, enabling them to keep up with the growing number of sequenced gene clusters and rapidly mine these clusters for functional information.