Bioactive Synthetic-Bioinformatic Natural Product Cyclic Peptides Inspired by Nonribosomal Peptide Synthetase Gene Clusters from the Human Microbiome

Discovery of naturally inspired antimicrobial peptides using deep learning

Non-ribosomal peptides (NRPs) are promising lead compounds for novel antibiotics. Bioinformatic mining of silent microbial NRPS gene clusters provide crucial insights for the discovery and de novo design of bioactive peptides. Here, we describe the efficient discovery and antibacterial evaluation of novel peptides inspired by metabolite scaffolds encoded by NRPS gene clusters from 216,408 bacterial genomes. In total, 335,024 NRPS gene clusters were identified and dereplicated, yielding 328 unique peptide scaffolds. Using deep learning-based scoring, five antimicrobial peptide candidates (P1-P5) were synthesized via solid-phase chemical synthesis. Among them, peptide P2 exhibited potent antibacterial activity with MIC50 values of 1-2 μM against two pathogenic strains. Subsequent amino acid optimization guided by deep learning algorithms produced P2.2, a derivative with significantly enhanced antibacterial activity. Mechanistic studies revealed that P2.2 disrupts bacterial membranes and increases permeability by modulating proteins involved in the type VI and III secretion systems. Furthermore, P2.2 demonstrated synergistic effects when combined with conventional antibiotics and exhibited reduced hemolytic activity, improving its therapeutic potential. These findings underscore the immense potential of deep learning to accelerate the discovery of naturally inspired antimicrobial peptides from silent biosynthetic gene clusters.

Large-scale biosynthetic analysis of human microbiomes reveals diverse protective ribosomal peptides

The human microbiome produces diverse metabolites that influence host health, yet the chemical landscape of ribosomally synthesized and post-translationally modified peptides (RiPPs)-a versatile class of bioactive compounds-remains underexplored. Here, we conduct a large-scale biosynthetic analysis of 306,481 microbial genomes from human-associated microbiomes, uncovering a broad array of yet-to-be-discovered RiPPs. These RiPPs are distributed across various body sites but show a specific enrichment in the gut and oral microbiome. Big data omics analysis reveals that numerous RiPP families are inversely related to various diseases, suggesting their potential protective effects on health. For a proof of principle study, we apply the synthetic-bioinformatic natural product (syn-BNP) approach to RiPPs and chemically synthesize nine autoinducing peptides (AIPs) for in vitro and ex vivo assay. Our findings reveal that five AIPs effectively inhibit the biofilm formation of disease-associated pathogens. Furthermore, when ex vivo testing gut microbiota from mice with inflammatory bowel disease, we observe that two AIPs can regulate the microbial community and reduce harmful species. These findings highlight the vast potential of human microbial RiPPs in regulating microbial communities and maintaining human health, emphasizing their potential for therapeutic development.

Synthetic-bioinformatic natural product-inspired peptides

Covering: 2016 to 2024Natural products, particularly cyclic peptides, are a promising source of bioactive compounds. Nonribosomal peptide synthetases (NRPSs) play a key role in biosynthesizing these compounds, which include antibiotic and anticancer agents, immunosuppressants, and others. Traditional methods of discovering natural products have limitations including cryptic biosynthetic gene clusters (BGCs), low titers, and currently unculturable organisms. This has prompted the exploration of alternative approaches. Synthetic-bioinformatic natural products (syn-BNPs) are one such alternative that utilizes bioinformatics techniques to predict nonribosomal peptides (NRPs) followed by chemical synthesis of the predicted peptides. This approach has shown promise, resulting in the discovery of a variety of bioactive compounds including peptides with antibacterial, antifungal, anticancer, and proteasome-stimulating activities. Despite the success of this approach, challenges remain especially in the accurate prediction of fatty acid incorporation, tailoring enzyme modifications, and peptide release mechanisms. Further work in these areas will enable the discovery of many bioactive peptides that are currently inaccessible.

Chemical structure metagenomics of microbial natural products: surveying nonribosomal peptides and beyond

Bioactivity-guided fractionation (BGF) has historically been a fruitful natural product discovery workflow. However, it is plagued by increasing rediscovery rates in recent years and new methods capable of exploring the natural product chemical space more broadly and more efficiently is in urgent need. Chemical structure metagenomics as one such method is the theme of this Perspective. It emphasizes a chemical-structure-centered viewpoint toward natural product research. Key to chemical structure metagenomics is the ability to predict the structure of a natural product based on its biosynthetic gene sequences, which facilitated the discovery of numerous new bioactive molecules and helped uncover oversampled/underexplored niches of decades of BGF based discovery. While microbial nonribosomal peptides have been the focus of chemical structure metagenomics efforts thus far, it is in principle applicable to other natural product families. The future outlook of this new approach will also be discussed.

Chemical genetic approaches to dissect microbiota mechanisms in health and disease

Advances in genomics, proteomics, and metabolomics have revealed associations between specific microbiota species in health and disease. However, the precise mechanism(s) of action for many microbiota species and molecules have not been fully elucidated, limiting the development of microbiota-based diagnostics and therapeutics. In this Review, we highlight innovative chemical and genetic approaches that are enabling the dissection of microbiota mechanisms and providing causation in health and disease. Although specific microbiota molecules and mechanisms have begun to emerge, new approaches are still needed to go beyond phenotypic associations and translate microbiota discoveries into actionable targets and therapeutic leads to prevent and treat diseases.

Discovery of a family of menaquinone-targeting cyclic lipodepsipeptides for multidrug-resistant Gram-positive pathogens

Menaquinone (MK) in bacterial membrane is an attractive target for the development of novel therapeutic agents. Mining the untapped chemical diversity encoded by Gram-negative bacteria presents an opportunity to identify additional MK-binding antibiotics (MBAs). By MK-binding motif searching of bioinformatically predicted linear non-ribosomal peptides from 14,298 sequenced genomes of 45 underexplored Gram-negative bacterial genera, here we identify a novel MBA structural family, including silvmeb and pseudomeb, using structure prediction-guided chemical synthesis. Both MBAs show rapid bacteriolysis by MK-dependent membrane depolarization to achieve their potent activities against a panel of Gram-positive pathogens. Furthermore, both MBAs are proven to be effective against methicillin-resistant Staphylococcus aureus in a murine peritonitis-sepsis model. Our findings suggest that MBAs are a kind of structurally diverse and still underexplored antibacterial lipodepsipeptide class. The interrogation of underexplored bacterial taxa using synthetic bioinformatic natural product methods is an appealing strategy for discovering novel biomedically relevant agents to confront the crisis of antimicrobial resistance.

Medium-sized peptides from microbial sources with potential for antibacterial drug development

Covering: 1993 to the end of 2022As the rapid development of antibiotic resistance shrinks the number of clinically available antibiotics, there is an urgent need for novel options to fill the existing antibiotic pipeline. In recent years, antimicrobial peptides have attracted increased interest due to their impressive broad-spectrum antimicrobial activity and low probability of antibiotic resistance. However, macromolecular antimicrobial peptides of plant and animal origin face obstacles in antibiotic development because of their extremely short elimination half-life and poor chemical stability. Herein, we focus on medium-sized antibacterial peptides (MAPs) of microbial origin with molecular weights below 2000 Da. The low molecular weight is not sufficient to form complex protein conformations and is also associated to a better chemical stability and easier modifications. Microbially-produced peptides are often composed of a variety of non-protein amino acids and terminal modifications, which contribute to improving the elimination half-life of compounds. Therefore, MAPs have great potential for drug discovery and are likely to become key players in the development of next-generation antibiotics. In this review, we provide a detailed exploration of the modes of action demonstrated by 45 MAPs and offer a concise summary of the structure-activity relationships observed in these MAPs.

Discovery and Synthesis of a Gram-Negative-Active Cationic Lipopeptide Antibiotic Inspired by Primary Sequences from Underexplored Gram-Negative Bacteria

The emergence of multidrug-resistant Gram-negative pathogens poses a serious threat to global health. Gram-negative bacteria have become increasingly recognized as underexplored sources of Gram-negative-active cationic lipopeptide (CLP) antibiotics. We systematically screened 8982 sequenced genomes from 42 underexplored Gram-negative bacterial genera and identified eight potential CLP biosynthetic gene clusters. Their predicted products were rapidly accessed by solid-phase total synthesis, which led to the novel antibiotic chospeptin with good activities against clinically isolated colistin-resistant Gram-negative pathogens.

Recent advances in the culture-independent discovery of natural products using metagenomic approaches

Natural products derived from bacterial sources have long been pivotal in the discovery of drug leads. However, the cultivation of only about 1% of bacteria in laboratory settings has left a significant portion of biosynthetic diversity hidden within the genomes of uncultured bacteria. Advances in sequencing technologies now enable the exploration of genetic material from these metagenomes through culture-independent methods. This approach involves extracting genetic sequences from environmental DNA and applying a hybrid methodology that combines functional screening, sequence tag-based homology screening, and bioinformatic-assisted chemical synthesis. Through this process, numerous valuable natural products have been identified and synthesized from previously uncharted metagenomic territories. This paper provides an overview of the recent advancements in the utilization of culture-independent techniques for the discovery of novel biosynthetic gene clusters and bioactive small molecules within metagenomic libraries.

Present and future outlooks on environmental DNA-based methods for antibiotic discovery

Novel antibiotics are in constant demand to combat a global increase in antibiotic-resistant infections. Bacterial natural products have been a long-standing source of antibiotic compounds, and metagenomic mining of environmental DNA (eDNA) has increasingly provided new antibiotic leads. The metagenomic small-molecule discovery pipeline can be divided into three main steps: surveying eDNA, retrieving a sequence of interest, and accessing the encoded natural product. Improvements in sequencing technology, bioinformatic algorithms, and methods for converting biosynthetic gene clusters into small molecules are steadily increasing our ability to discover metagenomically encoded antibiotics. We predict that, over the next decade, ongoing technological improvements will dramatically increase the rate at which antibiotics are discovered from metagenomes.

Trichoderma agriamazonicum sp. nov. (Hypocreaceae), a new ally in the control of phytopathogens

The genus Trichoderma comprises more than 500 valid species and is commonly used in agriculture for the control of plant diseases. In the present study, a Trichoderma species isolated from Scleronema micranthum (Malvaceae) has been extensively characterized and the morphological and phylogenetic data support the proposition of a new fungal species herein named Trichoderma agriamazonicum. This species inhibited the mycelial growth of all the nine phytopathogens tested both by mycoparasitism and by the production of VOCs, with a highlight for the inhibition of Corynespora cassiicola and Colletotrichum spp. The VOCs produced by T. agriamazonicum were able to control Capsicum chinense fruit rot caused by Colletotrichum scovillei and no symptoms were observed after seven days of phytopathogen inoculation. GC-MS revealed the production of mainly 6-amyl-α-pyrone, 1-octen-3-ol and 3-octanone during interaction with C. scovillei in C. chinense fruit. The HLPC-MS/MS analysis allowed us to annotate trikoningin KBII, hypocrenone C, 5-hydroxy-de-O-methyllasiodiplodin and unprecedented 7-mer peptaibols and lipopeptaibols. Comparative genomic analysis of five related Trichoderma species reveals a high number of proteins shared only with T. koningiopsis, mainly the enzymes related to oxidative stress. Regarding the CAZyme composition, T. agriamazonicum is most closely related to T. atroviride. A high protein copy number related to lignin and chitin degradation is observed for all Trichoderma spp. analyzed, while the presence of licheninase GH12 was observed only in T. agriamazonicum. Genome mining analysis identified 33 biosynthetic gene clusters (BGCs) of which 27 are new or uncharacterized, and the main BGCs are related to the production of polyketides. These results demonstrate the potential of this newly described species for agriculture and biotechnology.

Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides

The traditional natural product discovery approach has accessed only a fraction of the chemical diversity in nature. The use of bioinformatic tools to interpret the instructions encoded in microbial biosynthetic genes has the potential to circumvent the existing methodological bottlenecks and greatly expand the scope of discovery. Structural prediction algorithms for nonribosomal peptides (NRPs), the largest family of microbial natural products, lie at the heart of this new approach. To understand the scope and limitation of the existing prediction algorithms, we evaluated their performances on NRP synthetase biosynthetic gene clusters. Our systematic analysis shows that the NRP biosynthetic landscape is uneven. Phenylglycine and its derivatives as a group of NRP building blocks (BBs), for example, have been oversampled, reflecting an extensive historical interest in the glycopeptide antibiotics family. In contrast, the benzoyl BB, including 2,3-dihydroxybenzoate (DHB), has been the most underexplored, hinting at the possibility of a reservoir of as yet unknown DHB containing NRPs with functional roles other than a siderophore. Our results also suggest that there is still vast unexplored biosynthetic diversity in nature, and the analysis presented herein shall help guide and strategize future natural product discovery campaigns. We also discuss possible ways bioinformaticians and biochemists could work together to improve the existing prediction algorithms.

Next-generation synthetic biology approaches for the accelerated discovery of microbial natural products

Microbial natural products (NPs) and their derivates have been widely used in health care and agriculture during the past few decades. Although large-scale bacterial or fungal (meta)genomic mining has revealed the tremendous biosynthetic potentials to produce novel small molecules, there remains a lack of universal approaches to link NP biosynthetic gene clusters (BGCs) to their associated products at a large scale and speed. In the last ten years, a series of emerging technologies have been established alongside the developments in synthetic biology to engineer cryptic metabolite BGCs and edit host genomes. Diverse computational tools, such as antiSMASH and PRISM, have also been simultaneously developed to rapidly identify BGCs and predict the chemical structures of their products. This review discusses the recent developments and trends pertaining to the accelerated discovery of microbial NPs driven by a wide variety of next-generation synthetic biology approaches, with an emphasis on the in situ activation of silent BGCs at scale, the direct cloning or refactoring of BGCs of interest for heterologous expression, and the synthetic-bioinformatic natural products (syn-BNP) approach for the guided rapid access of bioactive non-ribosomal peptides.

Identification of PKS Gene Clusters from Metagenomic Libraries Using a Next-Generation Sequencing Approach

Microbial secondary metabolites have been an important source of bioactive compounds with diverse applications from medicine to agriculture, noticeably those encoded by polyketide synthase (PKS) clusters due to their astounding chemical diversity. While most discovered compounds originate from culturable microorganisms, yet-to-be cultured microbes represent a reservoir of previously inaccessible compounds. The advent and development of metagenomics have allowed not only the characterization of these microorganisms but also their metabolic potential, making viable the prospection of environmental PKS for natural product discovery.Study of environmental PKSs often relies on the construction of metagenomic libraries and their mining, with clones containing PKS clusters identified via amplification of conserved domains and then screened for an activity of interest. Compounds produced by clones exhibiting the desired bioactivity can be isolated and characterized. However, these approaches can be less sensitive and biased against more divergent clusters, in addition to precluding the use of bioinformatics for cluster characterization prior to expression. While direct shotgun sequencing of metagenomes has identified and profiled a great number of PKSs from different environments and yet-to-be cultured microorganisms, it does not lend itself well to heterologous expression, the cruxes of natural product discovery.Here, we describe a strategy for sequencing entire metagenomic libraries while maintaining correspondence between sequence and clone, allowing the full characterization and annotation of all clusters present in a library using bioinformatic tools and then seamlessly passing clones of interest for activity screening through heterologous expression. Once a library is sequenced, the methods herein can be adapted for the mining of any biosynthetic gene cluster of interest within a metagenomic library.

Harnessing Rare Actinomycete Interactions and Intrinsic Antimicrobial Resistance Enables Discovery of an Unusual Metabolic Inhibitor

Bacterial natural products have historically been a deep source of new medicines, but their slowed discovery in recent decades has put a premium on developing strategies that enhance the likelihood of capturing novel compounds. Here, we used a straightforward approach that capitalizes on the interactive ecology of “rare” actinomycetes. Specifically, we screened for interactions that triggered the production of antimicrobials that inhibited the growth of a bacterial strain with exceptionally diverse natural antimicrobial resistance. This strategy led to the discovery of a family of antimicrobials we term the dynaplanins. Heterologous expression enabled identification of the dynaplanin biosynthetic gene cluster, which was missed by typical algorithms for natural product gene cluster detection. Genome sequencing of partially resistant mutants revealed a 2-oxo acid dehydrogenase E2 subunit as the likely molecular target of the dynaplanins, and this finding was supported by computational modeling of the dynaplanin scaffold within the active site of this enzyme. Thus, this simple strategy, which leverages microbial interactions and natural antibiotic resistance, can enable discovery of molecules with unique antimicrobial activity. In addition, these results indicate that primary metabolism may be a direct target for inhibition via chemical interference in competitive microbial interactions.

Microbial metabolites: cause or consequence in gastrointestinal disease?

Systems biology studies have established that changes in gastrointestinal microbiome composition and function can adversely impact host physiology. Notable diseases synonymously associated with dysbiosis include inflammatory bowel diseases, cancer, metabolic disorders, and opportunistic and recurrent pathogen infections. However, there is a scarcity of mechanistic data that advances our understanding of taxonomic correlations with pathophysiological host-microbiome interactions. Generally, to survive a hostile gut environment, microbes are highly metabolically active and produce trans-kingdom signaling molecules to interact with competing microorganisms and the host. These specialized metabolites likely play important homeostatic roles, and identifying disease-specific taxa and their effector pathways can provide better strategies for diagnosis, treatment, and prevention, as well as the discovery of innovative therapeutics. The signaling role of microbial biotransformation products such as bile acids, short-chain fatty acids, polysaccharides, and dietary tryptophan is increasingly recognized, but little is known about the identity and function of metabolites that are synthesized by microbial biosynthetic gene clusters, including ribosomally synthesized and posttranslationally modified peptides (RiPPs), nonribosomal peptides (NRPs), polyketides (PKs), PK-NRP hybrids, and terpenes. Here we consider how bioactive natural products directly encoded by the human microbiome can contribute to the pathophysiology of gastrointestinal disease, cancer, autoimmune, antimicrobial-resistant bacterial and viral infections (including COVID-19). We also present strategies used to discover these compounds and the biological activities they exhibit, with consideration of therapeutic interventions that could emerge from understanding molecular causation in gut microbiome research.

Bioinformatic prospecting and synthesis of a bifunctional lipopeptide antibiotic that evades resistance

Emerging resistance to currently used antibiotics is a global public health crisis. Because most of the biosynthetic capacity within the bacterial kingdom has remained silent in previous antibiotic discovery efforts, uncharacterized biosynthetic gene clusters found in bacterial genome-sequencing studies remain an appealing source of antibiotics with distinctive modes of action. Here, we report the discovery of a naturally inspired lipopeptide antibiotic called cilagicin, which we chemically synthesized on the basis of a detailed bioinformatic analysis of the cil biosynthetic gene cluster. Cilagicin's ability to sequester two distinct, indispensable undecaprenyl phosphates used in cell wall biosynthesis, together with the absence of detectable resistance in laboratory tests and among multidrug-resistant clinical isolates, makes it an appealing candidate for combating antibiotic-resistant pathogens.

Identification of antimicrobial peptides from the human gut microbiome using deep learning

The human gut microbiome encodes a large variety of antimicrobial peptides (AMPs), but the short lengths of AMPs pose a challenge for computational prediction. Here we combined multiple natural language processing neural network models, including LSTM, Attention and BERT, to form a unified pipeline for candidate AMP identification from human gut microbiome data. Of 2,349 sequences identified as candidate AMPs, 216 were chemically synthesized, with 181 showing antimicrobial activity (a positive rate of >83%). Most of these peptides have less than 40% sequence homology to AMPs in the training set. Further characterization of the 11 most potent AMPs showed high efficacy against antibiotic-resistant, Gram-negative pathogens and demonstrated significant efficacy in lowering bacterial load by more than tenfold against a mouse model of bacterial lung infection. Our study showcases the potential of machine learning approaches for mining functional peptides from metagenome data and accelerating the discovery of promising AMP candidate molecules for in-depth investigations.

A naturally inspired antibiotic to target multidrug-resistant pathogens

Gram-negative bacteria are responsible for an increasing number of deaths caused by antibiotic-resistant infections^1,2. The bacterial natural product colistin is considered the last line of defence against a number of Gram-negative pathogens. The recent global spread of the plasmid-borne mobilized colistin-resistance gene mcr-1 (phosphoethanolamine transferase) threatens the usefulness of colistin³. Bacteria-derived antibiotics often appear in nature as collections of similar structures that are encoded by evolutionarily related biosynthetic gene clusters. This structural diversity is, at least in part, expected to be a response to the development of natural resistance, which often mechanistically mimics clinical resistance. Here we propose that a solution to mcr-1-mediated resistance might have evolved among naturally occurring colistin congeners. Bioinformatic analysis of sequenced bacterial genomes identified a biosynthetic gene cluster that was predicted to encode a structurally divergent colistin congener. Chemical synthesis of this structure produced macolacin, which is active against Gram-negative pathogens expressing mcr-1 and intrinsically resistant pathogens with chromosomally encoded phosphoethanolamine transferase genes. These Gram-negative bacteria include extensively drug-resistant Acinetobacter baumannii and intrinsically colistin-resistant Neisseria gonorrhoeae, which, owing to a lack of effective treatment options, are considered among the highest level threat pathogens⁴. In a mouse neutropenic infection model, a biphenyl analogue of macolacin proved to be effective against extensively drug-resistant A. baumannii with colistin-resistance, thus providing a naturally inspired and easily produced therapeutic lead for overcoming colistin-resistant pathogens.

Identification of structurally diverse menaquinone-binding antibiotics with in vivo activity against multidrug-resistant pathogens

The emergence of multidrug-resistant bacteria poses a threat to global health and necessitates the development of additional in vivo active antibiotics with diverse modes of action. Directly targeting menaquinone (MK), which plays an important role in bacterial electron transport, is an appealing, yet underexplored, mode of action due to a dearth of MK-binding molecules. Here we combine sequence-based metagenomic mining with a motif search of bioinformatically predicted natural product structures to identify six biosynthetic gene clusters that we predicted encode MK-binding antibiotics (MBAs). Their predicted products (MBA1-6) were rapidly accessed using a synthetic bioinformatic natural product approach, which relies on bioinformatic structure prediction followed by chemical synthesis. Among these six structurally diverse MBAs, four make up two new MBA structural families. The most potent member of each new family (MBA3, MBA6) proved effective at treating methicillin-resistant Staphylococcus aureus infection in a murine peritonitis-sepsis model. The only conserved feature present in all MBAs is the sequence 'GXLXXXW', which we propose represents a minimum MK-binding motif. Notably, we found that a subset of MBAs were active against Mycobacterium tuberculosis both in vitro and in macrophages. Our findings suggest that naturally occurring MBAs are a structurally diverse and untapped class of mechanistically interesting, in vivo active antibiotics.

Synthetic Natural Product Inspired Cyclic Peptides

Natural products are a bountiful source of bioactive molecules. Unfortunately, discovery of novel bioactive natural products is challenging due to cryptic biosynthetic gene clusters, low titers, and arduous purifications. Herein, we describe SNaPP (Synthetic Natural Product Inspired Cyclic Peptides), a method for identifying NP-inspired bioactive peptides. SNaPP expedites bioactive molecule discovery by combining bioinformatics predictions of nonribosomal peptide synthetases with chemical synthesis of the predicted natural products (pNPs). SNaPP utilizes a recently discovered cyclase, the penicillin binding protein-like cyclase, as the lynchpin for the development of a library of head-to-tail cyclic peptide pNPs. Analysis of 500 biosynthetic gene clusters allowed for identification of 131 novel pNPs. Fifty-one diverse pNPs were synthesized using solid phase peptide synthesis and solution-phase cyclization. Antibacterial testing revealed 14 pNPs with antibiotic activity, including activity against multidrug-resistant Gram-negative bacteria. Overall, SNaPP demonstrates the power of combining bioinformatics predictions with chemical synthesis to accelerate the discovery of bioactive molecules.

Genome mining methods to discover bioactive natural products

Covering: 2016 to 2021With genetic information available for hundreds of thousands of organisms in publicly accessible databases, scientists have an unprecedented opportunity to meticulously survey the diversity and inner workings of life. The natural product research community has harnessed this breadth of sequence information to mine microbes, plants, and animals for biosynthetic enzymes capable of producing bioactive compounds. Several orthogonal genome mining strategies have been developed in recent years to target specific chemical features or biological properties of bioactive molecules using biosynthetic, resistance, or transporter proteins. These "biosynthetic hooks" allow researchers to query for biosynthetic gene clusters with a high probability of encoding previously undiscovered, bioactive compounds. This review highlights recent case studies that feature orthogonal approaches that exploit genomic information to specifically discover bioactive natural products and their gene clusters.

Metagenomic sequencing-driven multidisciplinary approaches to shed light on the untapped microbial natural products

The advantage of metagenomics over the culture-based natural product (NP) discovery pipeline is the ability to access the biosynthetic potential of uncultivable microbes. Advances in DNA sequencing are revolutionizing conventional metagenomics approaches for microbial NP discovery. The genomes of (in)cultivable bugs can be resolved straightforwardly from environmental samples, enabling in situ prediction of biosynthetic gene clusters (BGCs). The predicted chemical diversities could be realized not only by heterologous expression of gene clusters originating from DNA synthesis or direct cloning, but also potentially by bioinformatic-directed organic synthesis or chemoenzymatic total synthesis. In this review, we suggest that metagenomic sequencing in tandem with multidisciplinary approaches will form a versatile platform to shed light on a plethora of microbial 'dark matter'.

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.

Mutanobactin D from the Human Microbiome: Total Synthesis, Configurational Assignment, and Biological Evaluation

Mutanobactin D is a non-ribosomal, cyclic peptide isolated from Streptococcus mutans and shows activity reducing yeast-to-hyphae transition as well as biofilm formation of the pathogenic yeast Candida albicans. We report the first total synthesis of this natural product, which relies on enantioselective, zinc-mediated 1,3-dipolar cycloaddition and a sequence of cascading reactions, providing the key lipidated γ-amino acid found in mutanobactin D. The synthesis enables configurational assignment, determination of the dominant solution-state structure, and studies to assess the stability of the lipopeptide substructure found in the natural product. The information stored in the fingerprint region of the IR spectra in combination with quantum chemical calculations proved key to distinguishing between epimers of the α-substituted β-keto amide. Synthetic mutanobactin D drives discovery and analysis of its effect on growth of other members of the human oral consortium. Our results showcase how total synthesis is central for elucidating the complex network of interspecies communications of human colonizers.

Biocatalytic synthesis of peptidic natural products and related analogues

Peptidic natural products (PNPs) represent a rich source of lead compounds for the discovery and development of therapeutic agents for the treatment of a variety of diseases. However, the chemical synthesis of PNPs with diverse modifications for drug research is often faced with significant challenges, including the unavailability of constituent nonproteinogenic amino acids, inefficient cyclization protocols, and poor compatibility with other functional groups. Advances in the understanding of PNP biosynthesis and biocatalysis provide a promising, sustainable alternative for the synthesis of these compounds and their analogues. Here we discuss current progress in using native and engineered biosynthetic enzymes for the production of both ribosomally and nonribosomally synthesized peptides. In addition, we highlight new in vitro and in vivo approaches for the generation and screening of PNP libraries.

Structure Prediction and Synthesis of Pyridine-Based Macrocyclic Peptide Natural Products

The structural and functional characterization of natural products is vastly outpaced by the bioinformatic identification of biosynthetic gene clusters (BGCs) that encode such molecules. Uniting our knowledge of bioinformatics and enzymology to predict and synthetically access natural products is an effective platform for investigating cryptic/silent BGCs. We report the identification, biosynthesis, and total synthesis of a minimalistic class of ribosomally synthesized and post-translationally modified peptides (RiPPs) with the responsible BGCs encoding a subset of enzymes known from thiopeptide biosynthesis. On the basis of the BGC content, these RiPPs were predicted to undergo enzymatic dehydration of serine followed by [4+2]-cycloaddition to produce a trisubstituted, pyridine-based macrocycle. These RiPPs, termed "pyritides", thus contain the same six-membered, nitrogenous heterocycle that defines the thiopeptide RiPP class but lack the ubiquitous thiazole/thiazoline heterocycles, suggesting that thiopeptides should be reclassified as a more elaborate subclass of the pyritides. One pyritide product was obtained using an 11-step synthesis, and the structure verified by an orthogonal chemoenzymatic route using the precursor peptide and cognate pyridine synthase. This work exemplifies complementary bioinformatics, enzymology, and synthesis to characterize a minimalistic yet structurally intriguing scaffold that, unlike most thiopeptides, lacks growth-suppressive activity toward Gram-positive bacteria.

Natural product discovery through microbial genome mining

The advent of the genomic era has opened up enormous possibilities for the discovery of new natural products. Also known as specialized metabolites, these compounds produced by bacteria, fungi, and plants have long been sought for their bioactive properties. Innovations in both DNA sequencing technologies and bioinformatics now allow the wealth of sequence data to be mined at both the genome and metagenome levels for new specialized metabolites. However, a key problem that remains is rapidly and efficiently linking these identified genes to their corresponding compounds. Within this review, we provide specific examples of studies that have used the power of genomic or metagenomic data to overcome these problems and identify new small molecules and their biosynthetic pathways.

Synthetic-Bioinformatic Natural Product Antibiotics with Diverse Modes of Action

Bacterial natural products have inspired the development of numerous antibiotics in use today. As resistance to existing antibiotics has become more prevalent, new antibiotic lead structures and activities are desperately needed. An increasing number of natural product biosynthetic gene clusters, to which no known molecules can be assigned, are found in genome and metagenome sequencing data. Here we access structural information encoded in this underexploited resource using a synthetic-bioinformatic natural product (syn-BNP) approach, which relies on bioinformatic algorithms followed by chemical synthesis to predict and then produce small molecules inspired by biosynthetic gene clusters. In total, 157 syn-BNP cyclic peptides inspired by 96 nonribosomal peptide synthetase gene clusters were synthesized and screened for antibacterial activity. This yielded nine antibiotics with activities against ESKAPE pathogens as well as Mycobacterium tuberculosis. Not only are antibiotic-resistant pathogens susceptible to many of these syn-BNP antibiotics, but they were also unable to develop resistance to these antibiotics in laboratory experiments. Characterized modes of action for these antibiotics include cell lysis, membrane depolarization, inhibition of cell wall biosynthesis, and ClpP protease dysregulation. Increasingly refined syn-BNP-based explorations of biosynthetic gene clusters should allow for more rapid identification of evolutionarily inspired bioactive small molecules, in particular antibiotics with diverse mechanism of actions that could help confront the imminent crisis of antimicrobial resistance.