Increased diversity of peptidic natural products revealed by modification-tolerant database search of mass spectra

Histidine-bridged cyclic peptide natural products: isolation, biosynthesis and synthetic studies

The histidine bridge is a rare and often overlooked structural motif in macrocyclic peptide natural products, yet there are several examples in nature of cyclic peptides bearing this moiety that exhibit potent biological activity. These interesting compounds have been the focus of several studies reporting their isolation, biosynthesis and chemical synthesis over the last four decades. This review summarises the findings on the structure, biological activity and, where possible, proposed biosynthesis and progress towards the synthesis of histidine-bridged cyclic peptides.

Interaction of Acinetobacter sp. RIT 592 induces the production of broad-spectrum antibiotics in Exiguobacterium sp. RIT 594

Antimicrobial resistance (AMR) is one of the most alarming global public health challenges of the 21st century. Over 3 million antimicrobial-resistant infections occur in the United States annually, with nearly 50,000 cases being fatal. Innovations in drug discovery methods and platforms are crucial to identify novel antibiotics to combat AMR. We present the isolation and characterization of potentially novel antibiotic lead compounds produced by the cross-feeding of two rhizosphere bacteria, Acinetobacter sp. RIT 592 and Exiguobacterium sp. RIT 594. We used solid-phase extraction (SPE) followed by liquid chromatography (LC) to enrich antibiotic extracts and subsequently mass spectrometry (MS) analysis of collected fractions for compound structure identification and characterization. The MS data were processed through the Global Natural Product Social Molecular Networking (GNPS) database. The supernatant from RIT 592 induced RIT 594 to produce a cocktail of antimicrobial compounds active against Gram-positive and negative bacteria. The GNPS analysis indicated compounds with known antimicrobial activity in the bioactive samples, including oligopeptides and their derivatives. This work emphasizes the utility of microbial community-based platforms to discover novel clinically relevant secondary metabolites. Future work includes further structural characterization and antibiotic activity evaluation of the individual compounds against pathogenic multidrug-resistant (MDR) bacteria.

Cellulamides: A New Family of Marine-Sourced Linear Peptides from the Underexplored Cellulosimicrobium Genus

Bioprospecting the secondary metabolism of underexplored Actinomycetota taxa is a prolific route to uncover novel chemistry. In this work, we report the isolation, structure elucidation, and bioactivity screening of cellulamides A and B (1 and 2), two novel linear peptides obtained from the culture of the macroalga-associated Cellulosimicrobium funkei CT-R177. The host of this microorganism, the Chlorophyta Codium tomentosum, was collected in the northern Portuguese coast and, in the scope of a bioprospecting study focused on its associated actinobacterial community, strain CT-R177 was isolated, taxonomically identified, and screened for the production of antimicrobial and anticancer compounds. Dereplication of a crude extract of this strain using LC-HRMS(/MS) analysis unveiled a putative novel natural product, cellulamide A (1), that was isolated following mass spectrometry-guided fractionation. An additional analog, cellulamide B (2) was obtained during the chromatographic process and chemically characterized. The chemical structures of the novel linear peptides, including their absolute configurations, were elucidated using a combination of HRMS, 1D/2D NMR spectroscopy, and Marfey's analysis. Cellulamide A (1) was subjected to a set of bioactivity screenings, but no significant biological activity was observed. The cellulamides represent the first family of natural products reported from the Actinomycetota genus Cellulosimicrobium, showcasing not only the potential of less-explored taxa but also of host-associated marine strains for novel chemistry discovery.

Decylprodigiosin: a new member of the prodigiosin family isolated from a seaweed-associated Streptomyces

Bioprospecting actinobacterial secondary metabolism from untapped marine sources may lead to the discovery of biotechnologically-relevant compounds. While studying the diversity and bioactive potential of Actinomycetota associated with Codium tomentosum, a green seaweed collected in the northern Portuguese cost, strain CT-F61, identified as Streptomyces violaceoruber, was isolated. Its extracts displayed a strong anticancer activity on breast carcinoma T-47D and colorectal carcinoma HCT116 cells, being effective as well against a panel of human and fish pathogenic bacteria. Following a bioactivity-guided isolation pipeline, a new analogue of the red-pigmented family of the antibiotics prodigiosins, decylprodigiosin (1), was identified and chemically characterized. Despite this family of natural products being well-known for a long time, we report a new analogue and the first evidence for prodigiosins being produced by a seaweed-associated actinomycete.

Genomic insights and anti-phytopathogenic potential of siderophore metabolome of endolithic Nocardia mangyaensis NH1

Actinobacteria are one of the predominant groups that successfully colonize and survive in various aquatic, terrestrial and rhizhospheric ecosystems. Among actinobacteria, Nocardia is one of the most important agricultural and industrial bacteria. Screening and isolation of Nocardia related bacteria from extreme habitats such as endolithic environments are beneficial for practical applications in agricultural and environmental biotechnology. In this work, bioinformatics analysis revealed that a novel strain Nocardia mangyaensis NH1 has the capacity to produce structurally varied bioactive compounds, which encoded by non-ribosomal peptide synthases (NRPS), polyketide synthase (PKS), and post-translationally modified peptides (RiPPs). Among NRPS, five gene clusters have a sequence homology with clusters encoding for siderophore synthesis. We also show that N. mangyaensis NH1 accumulates both catechol- and hydroxamate-type siderophores simultaneously under iron-deficient conditions. Untargeted LC-MS/MS analysis revealed a variety of metabolites, including siderophores, lipopeptides, cyclic peptides, and indole-3-acetic acid (IAA) in the culture medium of N. mangyaensis NH1 grown under iron deficiency. We demonstrate that four CAS (chrome azurol S)-positive fractions display variable affinity to metals, with a high Fe3+ chelating capability. Additionally, three of these fractions exhibit antioxidant activity. A combination of iron scavenging metabolites produced by N. mangyaensis NH1 showed antifungal activity against several plant pathogenic fungi. We have shown that the pure culture of N. mangyaensis NH1 and its metabolites have no adverse impact on Arabidopsis seedlings. The ability of N. mangyaensis NH1 to produce siderophores with antifungal, metal-chelating, and antioxidant properties, when supplemented with phytohormones, has the potential to improve the release of macro- and micronutrients, increase soil fertility, promote plant growth and development, and enable the production of biofertilizers across diverse soil systems.

Natural Products Dereplication: Databases and Analytical Methods

The development of efficient methods for dereplication has been critical in the re-emergence of the research in natural products as a source of drug leads. Current dereplication workflows rapidly identify already known bioactive secondary metabolites in the early stages of any drug discovery screening campaign based on natural extracts or enriched fractions. Two main factors have driven the evolution of natural products dereplication over the last decades. First, the availability of both commercial and public large databases of natural products containing the key annotations against which the biological and chemical data derived from the studied sample are searched for. Second, the considerable improvement achieved in analytical technologies (including instrumentation and software tools) employed to obtain robust and precise chemical information (particularly spectroscopic signatures) on the compounds present in the bioactive natural product samples. This chapter describes the main methods of dereplication, which rely on the combined use of large natural product databases and spectral libraries, alongside the information obtained from chromatographic, UV-Vis, MS, and NMR spectroscopic analyses of the samples of interest.

Metabolome-guided genome mining of RiPP natural products

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a chemically diverse class of metabolites. Many RiPPs show potent biological activities that make them attractive starting points for drug development. A promising approach for the discovery of new classes of RiPPs is genome mining. However, the accuracy of genome mining is hampered by the lack of signature genes shared across different RiPP classes. One way to reduce false-positive predictions is by complementing genomic information with metabolomics data. In recent years, several new approaches addressing such integrative genomics and metabolomics analyses have been developed. In this review, we provide a detailed discussion of RiPP-compatible software tools that integrate paired genomics and metabolomics data. We highlight current challenges in data integration and identify opportunities for further developments targeting new classes of bioactive RiPPs.

HypoRiPPAtlas as an Atlas of hypothetical natural products for mass spectrometry database search

Recent analyses of public microbial genomes have found over a million biosynthetic gene clusters, the natural products of the majority of which remain unknown. Additionally, GNPS harbors billions of mass spectra of natural products without known structures and biosynthetic genes. We bridge the gap between large-scale genome mining and mass spectral datasets for natural product discovery by developing HypoRiPPAtlas, an Atlas of hypothetical natural product structures, which is ready-to-use for in silico database search of tandem mass spectra. HypoRiPPAtlas is constructed by mining genomes using seq2ripp, a machine-learning tool for the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs). In HypoRiPPAtlas, we identify RiPPs in microbes and plants. HypoRiPPAtlas could be extended to other natural product classes in the future by implementing corresponding biosynthetic logic. This study paves the way for large-scale explorations of biosynthetic pathways and chemical structures of microbial and plant RiPP classes.

Toxicological effects of cyanobacterial metabolites on zebrafish larval development

Freshwater cyanobacteria are known worldwide for their potential to produce toxins. However, these organisms are also found in marine, terrestrial and extreme environments and produce unique compounds, other than toxins. Nevertheless, their effects on biological systems are still barely known. This work tested extracts of different cyanobacterial strains against zebrafish (Danio rerio) larvae and analyzed their metabolomic profiles using liquid chromatography combined with mass spectrometry. Strains Desertifilum tharense, Anagnostidinema amphibium, and Nostoc sp. promoted morphological abnormalities such as pericardial edema, edema in the digestive system region, curvature of the tail and spine in zebrafish larvae in vivo. In contrast, Microcystis aeruginosa and Chlorogloeopsis sp. did not promote such changes. Metabolomics revealed unique compounds belonging to the classes of terpenoids, peptides, and linear lipopeptides/microginins in the nontoxic strains. The toxic strains were shown to contain unique compounds belonging to the classes of cyclic peptides, amino acids and other peptides, anabaenopeptins, lipopeptides, terpenoids, and alkaloids and derivatives. Other unknown compounds were also detected, highlighting the rich structural diversity of secondary metabolites produced by cyanobacteria. The effects of cyanobacterial metabolites on living organisms, mainly those related to potential human and ecotoxicological risks, are still poorly known. This work highlights the diverse, complex, and unique metabolomic profiles of cyanobacteria and the biotechnological potential and associated risks of exposure to their metabolites.

Proteomic identification of beer brewing products in the ground layer of Danish Golden Age paintings

The application of mass spectrometry-based proteomics to artworks provides accurate and detailed characterization of protein-based materials used in their production. This is highly valuable to plan conservation strategies and reconstruct the artwork's history. In this work, the proteomic analysis of canvas paintings from the Danish Golden Age led to the confident identification of cereal and yeast proteins in the ground layer. This proteomic profile points to a (by-)product of beer brewing, in agreement with local artists' manuals. The use of this unconventional binder can be connected to the workshops within the Royal Danish Academy of Fine Arts. The mass spectrometric dataset generated from proteomics was also processed with a metabolomics workflow. The spectral matches observed supported the proteomic conclusions, and, in at least one sample, suggested the use of drying oils. These results highlight the value of untargeted proteomics in heritage science, correlating unconventional artistic materials with local culture and practices.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Modern Trends in Natural Antibiotic Discovery

Natural scaffolds remain an important basis for drug development. Therefore, approaches to natural bioactive compound discovery attract significant attention. In this account, we summarize modern and emerging trends in the screening and identification of natural antibiotics. The methods are divided into three large groups: approaches based on microbiology, chemistry, and molecular biology. The scientific potential of the methods is illustrated with the most prominent and recent results.

Peptidomics

Peptides are biopolymers, typically consisting of 2-50 amino acids. They are biologically produced by the cellular ribosomal machinery or by non-ribosomal enzymes and, sometimes, other dedicated ligases. Peptides are arranged as linear chains or cycles, and include post-translational modifications, unusual amino acids and stabilizing motifs. Their structure and molecular size render them a unique chemical space, between small molecules and larger proteins. Peptides have important physiological functions as intrinsic signalling molecules, such as neuropeptides and peptide hormones, for cellular or interspecies communication, as toxins to catch prey or as defence molecules to fend off enemies and microorganisms. Clinically, they are gaining popularity as biomarkers or innovative therapeutics; to date there are more than 60 peptide drugs approved and more than 150 in clinical development. The emerging field of peptidomics comprises the comprehensive qualitative and quantitative analysis of the suite of peptides in a biological sample (endogenously produced, or exogenously administered as drugs). Peptidomics employs techniques of genomics, modern proteomics, state-of-the-art analytical chemistry and innovative computational biology, with a specialized set of tools. The complex biological matrices and often low abundance of analytes typically examined in peptidomics experiments require optimized sample preparation and isolation, including in silico analysis. This Primer covers the combination of techniques and workflows needed for peptide discovery and characterization and provides an overview of various biological and clinical applications of peptidomics.

Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics

Since the discovery of penicillin, microbial metabolites have been extensively investigated for drug discovery purposes. In the last decades, microbial derived compounds have gained increasing attention in different fields from pharmacognosy to industry and agriculture. Microbial metabolites in microbiomes present specific functions and can be associated with the maintenance of the natural ecosystems. These metabolites may exhibit a broad range of biological activities of great interest to human purposes. Samples from either microbial isolated cultures or microbiomes consist of complex mixtures of metabolites and their analysis are not a simple process. Mass spectrometry-based metabolomics encompass a set of analytical methods that have brought several improvements to the microbial natural products field. This analytical tool allows the comprehensively detection of metabolites, and therefore, the access of the chemical profile from those biological samples. These analyses generate thousands of mass spectra which is challenging to analyse. In this context, bioinformatic metabolomics tools have been successfully employed to accelerate and facilitate the investigation of specialized microbial metabolites. Herein, we describe metabolomics tools used to provide chemical information for the metabolites, and furthermore, we discuss how they can improve investigation of microbial cultures and interactions.

Actinobacteria from Arctic and Atlantic deep-sea sediments-Biodiversity and bioactive potential

The deep-sea covers over 70% of the Earth's surface and harbors predominantly uncharacterized bacterial communities. Actinobacteria are the major prokaryotic source of bioactive natural products that find their way into drug discovery programs, and the deep-sea is a promising source of biotechnologically relevant actinobacteria. Previous studies on actinobacteria in deep-sea sediments were either regionally restricted or did not combine a community characterization with the analysis of their bioactive potential. Here we characterized the actinobacterial communities of upper layers of deep-sea sediments from the Arctic and the Atlantic (Azores and Madeira) ocean basins, employing 16S rRNA metabarcoding, and studied the biosynthetic potential of cultivable actinobacteria retrieved from those samples. Metabarcoding analysis showed that the actinobacterial composition varied between the sampled regions, with higher abundance in the Arctic samples but higher diversity in the Atlantic ones. Twenty actinobacterial genera were detected using metabarcoding, as a culture-independent method, while culture-dependent methods only allowed the identification of nine genera. Isolation of actinobacteria resulted on the retrieval of 44 isolates, mainly associated with Brachybacterium, Microbacterium, and Brevibacterium genera. Some of these isolates were only identified on a specific sampled region. Chemical extracts of the actinobacterial isolates were subsequently screened for their antimicrobial, anticancer and anti-inflammatory activities. Extracts from two Streptomyces strains demonstrated activity against Candida albicans. Additionally, eight extracts (obtained from Brachybacterium, Brevibacterium, Microbacterium, Rhodococcus, and Streptomyces isolates) showed significant activity against at least one of the tested cancer cell lines (HepG2 and T-47D). Furthermore, 15 actinobacterial extracts showed anti-inflammatory potential in the RAW 264.4 cell model assay, with no concomitant cytotoxic response. Dereplication and molecular networking analysis of the bioactive actinobacterial extracts showed the presence of some metabolites associated with known natural products, but one of the analyzed clusters did not show any match with the natural products described as responsible for these bioactivities. Overall, we were able to recover taxonomically diverse actinobacteria with different bioactivities from the studied deep-sea samples. The conjugation of culture-dependent and -independent methods allows a better understanding of the actinobacterial diversity of deep-sea environments, which is important for the optimization of approaches to obtain novel chemically-rich isolates.

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.

NPvis: An Interactive Visualizer of Peptidic Natural Product–MS/MS Matches

Peptidic natural products (PNPs) represent a medically important class of secondary metabolites that includes antibiotics, anti-inflammatory and antitumor agents. Advances in tandem mass spectra (MS/MS) acquisition and in silico database search methods have enabled high-throughput PNP discovery. However, the resulting spectra annotations are often error-prone and their validation remains a bottleneck. Here, we present NPvis, a visualizer suitable for the evaluation of PNP–MS/MS matches. The tool interactively maps annotated spectrum peaks to the corresponding PNP fragments and allows researchers to assess the match correctness. NPvis accounts for the wide chemical diversity of PNPs that prevents the use of the existing proteomics visualizers. Moreover, NPvis works even if the exact chemical structure of the matching PNP is unknown. The tool is available online and as a standalone application. We hope that it will benefit the community by streamlining PNP data analysis and validation.

Structure and Biosynthesis of Desmamides A-C, Lipoglycopeptides from the Endophytic Cyanobacterium Desmonostoc muscorum LEGE 12446

Certain cyanobacteria of the secondary metabolite-rich order Nostocales can establish permanent symbioses with a large number of cycads, by accumulating in their coralloid roots and shifting their metabolism to dinitrogen fixation. Here, we report the discovery of two new lipoglycopeptides, desmamides A (1) and B (2), together with their aglycone desmamide C (3), from the nostocalean cyanobacterium Desmonostoc muscorum LEGE 12446 isolated from a cycad (Cycas revoluta) coralloid root. The chemical structures of the compounds were elucidated using a combination of 1D and 2D NMR spectroscopy and mass spectrometry. The desmamides are decapeptides featuring O-glycosylation of tyrosine (in 1 and 2) and an unusual 3,5-dihydroxy-2-methyldecanoic acid residue. The biosynthesis of the desmamides was studied by substrate incubation experiments and bioinformatics. We describe herein the dsm biosynthetic gene cluster and propose it to be associated with desmamide production. The discovery of this class of very abundant (>1.5% d.w.) bacterial lipoglycopeptides paves the way for exploration of their potential role in root endosymbiosis.

Insights into the Antimicrobial Activities and Metabolomes of Aquimarina (Flavobacteriaceae, Bacteroidetes) Species from the Rare Marine Biosphere

Two novel natural products, the polyketide cuniculene and the peptide antibiotic aquimarin, were recently discovered from the marine bacterial genus Aquimarina. However, the diversity of the secondary metabolite biosynthetic gene clusters (SM-BGCs) in Aquimarina genomes indicates a far greater biosynthetic potential. In this study, nine representative Aquimarina strains were tested for antimicrobial activity against diverse human-pathogenic and marine microorganisms and subjected to metabolomic and genomic profiling. We found an inhibitory activity of most Aquimarina strains against Candida glabrata and marine Vibrio and Alphaproteobacteria species. Aquimarina sp. Aq135 and Aquimarina muelleri crude extracts showed particularly promising antimicrobial activities, amongst others against methicillin-resistant Staphylococcus aureus. The metabolomic and functional genomic profiles of Aquimarina spp. followed similar patterns and were shaped by phylogeny. SM-BGC and metabolomics networks suggest the presence of novel polyketides and peptides, including cyclic depsipeptide-related compounds. Moreover, exploration of the ‘Sponge Microbiome Project’ dataset revealed that Aquimarina spp. possess low-abundance distributions worldwide across multiple marine biotopes. Our study emphasizes the relevance of this member of the microbial rare biosphere as a promising source of novel natural products. We predict that future metabologenomics studies of Aquimarina species will expand the spectrum of known secondary metabolites and bioactivities from marine ecosystems.

Genomic and metabolomic profiling of endolithic Rhodococcus fascians strain S11 isolated from an arid serpentine environment

Genomic and metabolomic studies of endolithic bacteria are essential for understanding their adaptations to extreme conditions of the rock environment and their contributions to mineralization and weathering processes. The endoliths of arid serpentine rocks are exposed to different environmental stresses, including desiccation and re-hydration, temperature fluctuations, oligotrophy, and high concentrations of heavy metals. Bacteria of the genus Rhodococcus commonly inhabit endolithic environments. Here, we describe genomic and metabolomic analyses of the non-pathogenic wild-type Rhodococcus fascians strain S11, isolated from weathered serpentine rock at the arid Khalilovsky massif, Russia. We found that strain S11 lacks the virulence plasmid that functions in the phytopathogenecity of some R. fascians strains. Phenotypic profiling revealed a high pH tolerance, phytase activity and siderophore production. A widely untargeted metabolome analysis performed using an Orbitrap LC-MS/MS method demonstrated the presence of chrysobactin-type siderophores in the culture medium of strain S11. The natural variation of secondary metabolites produced by strain S11 might provide a practical basis for revealing antibacterial, fungicide or insecticidal activities. Finally, plant infection and plant growth stimulation studies showed no observable effect of exposure strain S11 bacteria on the aerial and root parts of Arabidopsis thaliana plants. Based on our findings, R. fascians strain S11 might be promising tool for investigations of organo-mineral interactions, heavy metal bioremediation, and mechanisms of bacterial mediated weathering of plant-free serpentine rock to soil.

Development of RIKEN Plant Metabolome MetaDatabase

The advancement of metabolomics in terms of techniques for measuring small molecules has enabled the rapid detection and quantification of numerous cellular metabolites. Metabolomic data provide new opportunities to gain a deeper understanding of plant metabolism that can improve the health of both plants and humans that consume them. Although major public repositories for general metabolomic data have been established, the community still has shortcomings related to data sharing, especially in terms of data reanalysis, reusability and reproducibility. To address these issues, we developed the RIKEN Plant Metabolome MetaDatabase (RIKEN PMM, http://metabobank.riken.jp/pmm/db/plantMetabolomics), which stores mass spectrometry-based (e.g. gas chromatography-MS-based) metabolite profiling data of plants together with their detailed, structured experimental metadata, including sampling and experimental procedures. Our metadata are described as Linked Open Data based on the Resource Description Framework using standardized and controlled vocabularies, such as the Metabolomics Standards Initiative Ontology, which are to be integrated with various life and biomedical science data using the World Wide Web. RIKEN PMM implements intuitive and interactive operations for plant metabolome data, including raw data (netCDF format), mass spectra (NIST MSP format) and metabolite annotations. The feature is suitable not only for biologists who are interested in metabolomic phenotypes, but also for researchers who would like to investigate life science in general through plant metabolomic approaches.

Mass spectrometry-based metabolomics in microbiome investigations

Microbiotas are a malleable part of ecosystems, including the human ecosystem. Microorganisms affect not only the chemistry of their specific niche, such as the human gut, but also the chemistry of distant environments, such as other parts of the body. Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota, and to understand the functional role of these microbial metabolites. This Review provides a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Data analysis remains an obstacle; therefore, the emphasis is placed on data analysis approaches and integrative analysis, including the integration of microbiome sequencing data.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Catch-enrich-release approach for amine-containing natural products

Chemoselective approach to extract amine-containing natural products from complex matrices. The enzymatic release from the probe affords the underivatised compounds as products.

Discovery of Novel Cyclic Ethers with Synergistic Antiplasmodial Activity in Combination with Valinomycin

With drug resistance threatening our first line antimalarial treatments, novel chemotherapeutics need to be developed. Ionophores have garnered interest as novel antimalarials due to their theorized ability to target unique systems found in the Plasmodium-infected erythrocyte. In this study, during the bioassay-guided fractionation of the crude extract of Streptomyces strain PR3, a group of cyclodepsipeptides, including valinomycin, and a novel class of cyclic ethers were identified and elucidated. Further study revealed that the ethers were cyclic polypropylene glycol (cPPG) oligomers that had leached into the bacterial culture from an extraction resin. Molecular dynamics analysis suggests that these ethers are able to bind cations such as K⁺, NH₄⁺ and Na⁺. Combination studies using the fixed ratio isobologram method revealed that the cPPGs synergistically improved the antiplasmodial activity of valinomycin and reduced its cytotoxicity in vitro. The IC₅₀ of valinomycin against P. falciparum NF54 improved by 4-5-fold when valinomycin was combined with the cPPGs. Precisely, it was improved from 3.75 ± 0.77 ng/mL to 0.90 ± 0.2 ng/mL and 0.75 ± 0.08 ng/mL when dosed in the fixed ratios of 3:2 and 2:3 of valinomycin to cPPGs, respectively. Each fixed ratio combination displayed cytotoxicity (IC₅₀) against the Chinese Hamster Ovary cell line of 57-65 µg/mL, which was lower than that of valinomycin (12.4 µg/mL). These results indicate that combinations with these novel ethers may be useful in repurposing valinomycin into a suitable and effective antimalarial.

Nerpa: A Tool for Discovering Biosynthetic Gene Clusters of Bacterial Nonribosomal Peptides

Microbial natural products are a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class of natural products that include antibiotics, immunosuppressants, and anticancer agents. Recent breakthroughs in natural product discovery have revealed the chemical structure of several thousand NRPs. However, biosynthetic gene clusters (BGCs) encoding them are known only for a few hundred compounds. Here, we developed Nerpa, a computational method for the high-throughput discovery of novel BGCs responsible for producing known NRPs. After searching 13,399 representative bacterial genomes from the RefSeq repository against 8368 known NRPs, Nerpa linked 117 BGCs to their products. We further experimentally validated the predicted BGC of ngercheumicin from Photobacterium galatheae via mass spectrometry. Nerpa supports searching new genomes against thousands of known NRP structures, and novel molecular structures against tens of thousands of bacterial genomes. The availability of these tools can enhance our understanding of NRP synthesis and the function of their biosynthetic enzymes.

Applications and Restrictions of Integrated Genomic and Metabolomic Screening: An Accelerator for Drug Discovery from Actinomycetes?

Since the golden age of antibiotics in the 1950s and 1960s actinomycetes have been the most prolific source for bioactive natural products. However, the number of discoveries of new bioactive compounds decreases since decades. New procedures (e.g., activating strategies or innovative fermentation techniques) were developed to enhance the productivity of actinomycetes. Nevertheless, compound identification remains challenging among others due to high rediscovery rates. Rapid and cheap genome sequencing as well as the advent of bioinformatical analysis tools for biosynthetic gene cluster identification in combination with mass spectrometry-based molecular networking facilitated the tedious process of dereplication. In recent years several studies have been dedicated to accessing the biosynthetic potential of Actinomyces species, especially streptomycetes, by using integrated genomic and metabolomic screening in order to boost the discovery rate of new antibiotics. This review aims to present the various possible applications of this approach as well as the newly discovered molecules, covering studies between 2014 and 2021. Finally, the effectiveness of this approach with regard to find new bioactive agents from actinomycetes will be evaluated.

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.

Chemical Gradients of Plant Substrates in an Atta texana Fungus Garden

Many ant species grow fungus gardens that predigest food as an essential step of the ants' nutrient uptake. These symbiotic fungus gardens have long been studied and feature a gradient of increasing substrate degradation from top to bottom. To further facilitate the study of fungus gardens and enable the understanding of the predigestion process in more detail than currently known, we applied recent mass spectrometry-based approaches and generated a three-dimensional (3D) molecular map of an Atta texana fungus garden to reveal chemical modifications as plant substrates pass through it. The metabolomics approach presented in this study can be applied to study similar processes in natural environments to compare with lab-maintained ecosystems. IMPORTANCE The study of complex ecosystems requires an understanding of the chemical processes involving molecules from several sources. Some of the molecules present in fungus-growing ants' symbiotic system originate from plants. To facilitate the study of fungus gardens from a chemical perspective, we provide a molecular map of an Atta texana fungus garden to reveal chemical modifications as plant substrates pass through it. The metabolomics approach presented in this study can be applied to study similar processes in natural environments.

Integrated omics approaches for deciphering antifungal metabolites produced by a novel Bacillus species, B. cabrialesii TE3T, against the spot blotch disease of wheat (Triticum turgidum L. subsp. durum)

Bipolaris sorokiniana is an important biotic constraint for global wheat production, causing spot blotch disease. In this work, we present a comprehensive characterization of the cell-free culture filtrate (CF) and precipitated fraction (PF) of Bacillus cabrialesii TE3^T showing an effective inhibition of spot blotch. Our results indicated that CF produced by B. cabrialesii TE3^T inhibits the growth of B. sorokiniana through stable metabolites (after autoclaving and proteinase K treatment). Antifungal metabolites in CF and PF were explored by an integrated genomic-metabolomic approach. Genome-mining revealed that strain TE3^T contains the biosynthetic potential to produce wide spectrum antifungal (surfactin, fengycin, and rhizocticin A) and antibacterial metabolites (bacillaene, bacilysin, bacillibactin, and subtilosin A), and through bioactivity-guided LC-ESI-MS/MS approach we determined that a lipopeptide complex of surfactin and fengycin homologs was responsible for antifungal activity exhibited by B. cabrialesii TE3^T against the studied phytopathogen. In addition, our results demonstrate that i) a lipopeptide complex inhibits B. sorokiniana by disrupting its cytoplasmatic membrane and ii) reduced spot blotch disease by 93 %. These findings show the potential application of metabolites produced by strain TE3^T against B. sorokiniana and provide the first insight into antifungal metabolites produced by the novel Bacillus species, Bacillus cabrialesii.

MassSpecBlocks: a web-based tool to create building blocks and sequences of nonribosomal peptides and polyketides for tandem mass spectra analysis

Nonribosomal peptides and polyketides are natural products commonly synthesized by microorganisms. They are widely used in medicine, agriculture, environmental protection, and other fields. The structures of natural products are often analyzed by high-resolution tandem mass spectrometry, which becomes more popular with its increasing availability. However, the characterization of nonribosomal peptides and polyketides from tandem mass spectra is a nontrivial task because they are composed of many uncommon building blocks in addition to proteinogenic amino acids. Moreover, many of them have cyclic and branch-cyclic structures. Here, we introduce MassSpecBlocks – an open-source and web-based tool that converts the input chemical structures in SMILES format into sequences of building blocks. The structures can be searched in public databases PubChem, ChemSpider, ChEBI, NP Atlas, COCONUT, and Norine and edited in a user-friendly graphical interface. Although MassSpecBlocks can serve as a stand-alone database, our primary goal was to enable easy construction of custom sequence and building block databases, which can be used to annotate mass spectra in CycloBranch software. CycloBranch is an open-source, cross-platform, and stand-alone tool that we recently released for annotating spectra of linear, cyclic, branched, and branch-cyclic nonribosomal peptides and polyketide siderophores. The sequences and building blocks created in MassSpecBlocks can be easily exported into a plain text format used by CycloBranch. MassSpecBlocks is available online or can be installed entirely offline. It offers a REST API to cooperate with other tools.

MolDiscovery: learning mass spectrometry fragmentation of small molecules

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.

Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery

Non-Ribosomal Peptides (NRPs) represent a biomedically important class of natural products that include a multitude of antibiotics and other clinically used drugs. NRPs are not directly encoded in the genome but are instead produced by metabolic pathways encoded by biosynthetic gene clusters (BGCs). Since the existing genome mining tools predict many putative NRPs synthesized by a given BGC, it remains unclear which of these putative NRPs are correct and how to identify post-assembly modifications of amino acids in these NRPs in a blind mode, without knowing which modifications exist in the sample. To address this challenge, here we report NRPminer, a modification-tolerant tool for NRP discovery from large (meta)genomic and mass spectrometry datasets. We show that NRPminer is able to identify many NRPs from different environments, including four previously unreported NRP families from soil-associated microbes and NRPs from human microbiota. Furthermore, in this work we demonstrate the anti-parasitic activities and the structure of two of these NRP families using direct bioactivity screening and nuclear magnetic resonance spectrometry, illustrating the power of NRPminer for discovering bioactive NRPs.

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.

Identification of isocyclosporins by collision-induced dissociation of doubly protonated species

Nonribosomal cyclopeptide cyclosporin A (CsA), produced by fungus Tolypocladium inflatum, is an extremely important immunosuppressive drug used in organ transplantations and for therapy of autoimmune diseases. Here we report for the first time production of CsA, along with related cyclosporins B and C, by Tolypocladium inflatum strains of marine origin (White Sea). Cyclosporins A-C contain an unusual amino acid, (4R)-4-((E)-2-butenyl)-4,N-dimethyl-l-threonine (MeBmt), and are prone to isomerization to non-active isocyclosporin by N→O acyl shift of valine connected to MeBmt in acidic conditions. CsA and isoCsA are not distinguishable in MS analysis of [M+H]⁺ ions due to rapid [CsA + H]⁺→[isoCsA + H]⁺ conversion. We found that the N→O acyl shift is completely suppressed in cyclosporine [M+2H]²⁺ ions, and their collision-induced dissociation (CID) can be used for rapid and unambiguous analysis of cyclosporins and isocylosporins. Fragmentation patterns of [CsA+2H]²⁺ and [isoCsA+2H]²⁺ ions were analyzed and explained. The developed approach could be useful for MS analysis of other peptides containing β-hydroxy-α-amino acids.

StreptomeDB 3.0: an updated compendium of streptomycetes natural products

Antimicrobial resistance is an emerging global health threat necessitating the rapid development of novel antimicrobials. Remarkably, the vast majority of currently available antibiotics are natural products (NPs) isolated from streptomycetes, soil-dwelling bacteria of the genus Streptomyces. However, there is still a huge reservoir of streptomycetes NPs which remains pharmaceutically untapped and a compendium thereof could serve as a source of inspiration for the rational design of novel antibiotics. Initially released in 2012, StreptomeDB (http://www.pharmbioinf.uni-freiburg.de/streptomedb) is the first and only public online database that enables the interactive phylogenetic exploration of streptomycetes and their isolated or mutasynthesized NPs. In this third release, there are substantial improvements over its forerunners, especially in terms of data content. For instance, about 2500 unique NPs were newly annotated through manual curation of about 1300 PubMed-indexed articles, published in the last five years since the second release. To increase interoperability, StreptomeDB entries were hyperlinked to several spectral, (bio)chemical and chemical vendor databases, and also to a genome-based NP prediction server. Moreover, predicted pharmacokinetic and toxicity profiles were added. Lastly, some recent real-world use cases of StreptomeDB are highlighted, to illustrate its applicability in life sciences.

Omics-based strategies to discover novel classes of RiPP natural products

Ribosomally synthesized and post-translationally modified peptides (RiPPs) form a highly diverse class of natural products, with various biotechnologically and clinically relevant activities. A recent increase in discoveries of novel RiPP classes suggests that currently known RiPPs constitute just the tip of the iceberg. Genome mining has been a driving force behind these discoveries, but remains challenging due to a lack of universal genetic markers for RiPP detection. In this review, we discuss how various genome mining methodologies contribute towards the discovery of novel RiPP classes. Some methods prioritize novel biosynthetic gene clusters (BGCs) based on shared modifications between RiPP classes. Other methods identify RiPP precursors using machine-learning classifiers. The integration of such methods as well as integration with other types of omics data in more comprehensive pipelines could help these tools reach their potential, and keep pushing the boundaries of the chemical diversity of this important class of molecules.

Feature-based molecular networking in the GNPS analysis environment

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present Feature-Based Molecular Networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. The FBMN method brings quantitative analyses, isomeric resolution, including from ion-mobility spectrometry, into molecular networks.

Genome mining strategies for ribosomally synthesised and post-translationally modified peptides

Genome mining is a computational method for the automatic detection and annotation of biosynthetic gene clusters (BGCs) from genomic data. This approach has been increasingly utilised in natural product (NP) discovery due to the large amount of sequencing data that is now available. Ribosomally synthesised and post-translationally modified peptides (RiPPs) are a class of structurally complex NP with diverse bioactivities. RiPPs have recently been shown to occupy a much larger expanse of genomic and chemical space than previously appreciated, indicating that annotation of RiPP BGCs in genomes may have been overlooked in the past. This review provides an overview of the genome mining tools that have been specifically developed to aid in the discovery of RiPP BGCs, which have been built from an increasing knowledgebase of RiPP structures and biosynthesis. Given these recent advances, the application of targeted genome mining has great potential to accelerate the discovery of important molecules such as antimicrobial and anticancer agents whilst increasing our understanding about how these compounds are biosynthesised in nature.

NPS: scoring and evaluating the statistical significance of peptidic natural product-spectrum matches

MOTIVATION

Peptidic natural products (PNPs) are considered a promising compound class that has many applications in medicine. Recently developed mass spectrometry-based pipelines are transforming PNP discovery into a high-throughput technology. However, the current computational methods for PNP identification via database search of mass spectra are still in their infancy and could be substantially improved.

RESULTS

Here we present NPS, a statistical learning-based approach for scoring PNP-spectrum matches. We incorporated NPS into two leading PNP discovery tools and benchmarked them on millions of natural product mass spectra. The results demonstrate more than 45% increase in the number of identified spectra and 20% more found PNPs at a false discovery rate of 1%.

AVAILABILITY AND IMPLEMENTATION

NPS is available as a command line tool and as a web application at http://cab.spbu.ru/software/NPS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.

Case studies on computer-based identification of natural products as lead molecules

The development and application of computer-aided drug design/discovery (CADD) techniques (such as structured-base virtual screening, ligand-based virtual screening and neural networks approaches) are on the point of disintermediation in the pharmaceutical drug discovery processes. The application of these CADD methods are standing out positively as compared to other experimental approaches in the identification of hits. In order to venture into new chemical spaces, research groups are exploring natural products (NPs) for the search and identification of new hits and more efficient leads as well as the repurposing of approved NPs. The chemical space of NPs is continuously increasing as a result of millions of years of evolution of species and these data are mainly stored in the form of databases providing access to scientists around the world to conduct studies using them. Investigation of these NP databases with the help of CADD methodologies in combination with experimental validation techniques is essential to identify and propose new drug molecules. In this chapter, we highlight the importance of the chemical diversity of NPs as a source for potential drugs as well as some of the success stories of NP-derived candidates against important therapeutic targets. The focus is on studies that applied a healthy dose of the emerging CADD methodologies (structure-based, ligand-based and machine learning).

Linking genomics and metabolomics to chart specialized metabolic diversity

Microbial and plant specialized metabolites constitute an immense chemical diversity, and play key roles in mediating ecological interactions between organisms. Also referred to as natural products, they have been widely applied in medicine, agriculture, cosmetic and food industries. Traditionally, the main discovery strategies have centered around the use of activity-guided fractionation of metabolite extracts. Increasingly, omics data is being used to complement this, as it has the potential to reduce rediscovery rates, guide experimental work towards the most promising metabolites, and identify enzymatic pathways that enable their biosynthetic production. In recent years, genomic and metabolomic analyses of specialized metabolic diversity have been scaled up to study thousands of samples simultaneously. Here, we survey data analysis technologies that facilitate the effective exploration of large genomic and metabolomic datasets, and discuss various emerging strategies to integrate these two types of omics data in order to further accelerate discovery.

Molecular networking as a dereplication strategy for monitoring metabolites of natural product treated cancer cells

RATIONALE

Natural products have been great sources for drug discovery. However, the structures of natural products are diverse and difficult to elucidate. Cordyceps militaris is a parasitic fungus which usually grows on host insects. The metabolites of C. militaris have been reported to act as chemotherapeutic agents. In this study, we aimed for the structural elucidation of specialized metabolites derived from C. militaris, and the metabolic impact in leukemia cells.

METHODS

We describe a liquid chromatography data-dependent mass spectrometric platform combining tandem mass analysis and molecular networking. Leukemia cells treated with C. militaris extract and control groups were visualized in terms of their metabolic profiles using Global Natural Product Social (GNPS) molecular networking. By this method, we were able to elucidate the structures of metabolites from medicinal fungus extracts and cancer cells and then to recognize their changes in a semi-quantitative manner.

RESULTS

Using C. militaris and leukemia cells as examples, we found that approximately 100 new ion species were present in the treated leukemia cells, suggesting a highly altered metabolic profile. Specifically, based on the tandem mass spectral similarity, we proposed that cordycepin, a key fungus-derived therapeutic agent known for its antitumor activity, was transformed into its methylthio form in leukemia cells.

CONCLUSIONS

The platform described provides an ability to investigate complex molecular interactions of natural products in mammalian cells. By incorporating tandem mass spectrometry and molecular networking, we were able to reveal the chemical modification of crude bioactive compounds, for example potential bioactive compounds which might be modified from cordycepin. We envision that such a mass spectrometry (MS)-based workflow, combined with other metabolomics platforms, would enable much wider applicability to cell biology and be of great potential to pharmacological study as well as drug discovery.

Local Phenomena Shape Backyard Soil Metabolite Composition

Soil covers most of Earth's continental surface and is fundamental to life-sustaining processes such as agriculture. Given its rich biodiversity, soil is also a major source for natural product drug discovery from soil microorganisms. However, the study of the soil small molecule profile has been challenging due to the complexity and heterogeneity of this matrix. In this study, we implemented high-resolution liquid chromatography-tandem mass spectrometry and large-scale data analysis tools such as molecular networking to characterize the relative contributions of city, state and regional processes on backyard soil metabolite composition, in 188 soil samples collected from 14 USA States, representing five USA climate regions. We observed that region, state and city of collection all influence the overall soil metabolite profile. However, many metabolites were only detected in unique sites, indicating that uniquely local phenomena also influence the backyard soil environment, with both human-derived and naturally-produced (plant-derived, microbially-derived) metabolites identified. Overall, these findings are helping to define the processes that shape the backyard soil metabolite composition, while also highlighting the need for expanded metabolomic studies of this complex environment.

Microviridin 1777: A Toxic Chymotrypsin Inhibitor Discovered by a Metabologenomic Approach

The toxicity of the cyanobacterium Microcystis aeruginosa EAWAG 127a was evaluated against the sensitive grazer Thamnocephalus platyurus, and the extract possessed strong activity. To investigate the compounds responsible for cytotoxicity, a series of peptides from this cyanobacterium were studied using a combined genomic and molecular networking approach. The results led to the isolation, structure elucidation, and biological evaluation of microviridin 1777, which represents the most potent chymotrypsin inhibitor characterized from this family of peptides to date. Furthermore, the biosynthetic gene clusters of microviridin, anabaenopeptin, aeruginosin, and piricyclamide were located in the producing organism, and six additional natural products were identified by tandem mass spectrometry analyses. These results highlight the potential of modern techniques for the identification of natural products, demonstrate the ecological role of protease inhibitors produced by cyanobacteria, and raise ramifications concerning the presence of novel, yet uncharacterized, toxin families in cyanobacteria beyond microcystin.

A UHPLC-HRMS based metabolomics and chemoinformatics approach to chemically distinguish 'super foods' from a variety of plant-based foods

The aim of this study was to investigate if the declared benefits associated with superfoods are related to a specific molecular composition. For this purpose, untargeted metabolomics and molecular networking were used to obtain an overview of all features, focusing on compounds with anti-inflammatory, antioxidant or antimicrobial properties. 565 plant-based food samples were analyzed using UHPLC-HRMS and advanced data analysis tools. The molecular networking of the whole dataset allowed identification of a greater diversity of molecules, in particular, prenol lipids, isoflavonoids and isoquinolines in superfoods, when compared with non-superfood species belonging to the same botanical family. Furthermore, in silico tools were used to expand our chemical knowledge of compounds observed in superfood samples.

De Novo Peptide Sequencing Reveals Many Cyclopeptides in the Human Gut and Other Environments

Cyclic and branch cyclic peptides (cyclopeptides) represent a class of bioactive natural products that include many antibiotics and anti-tumor compounds. Despite the recent advances in metabolomics analysis, still little is known about the cyclopeptides in the human gut and their possible interactions due to a lack of computational analysis pipelines that are applicable to such compounds. Here, we introduce CycloNovo, an algorithm for automated de novo cyclopeptide analysis and sequencing that employs de Bruijn graphs, the workhorse of DNA sequencing algorithms, to identify cyclopeptides in spectral datasets. CycloNovo reconstructed 32 previously unreported cyclopeptides (to the best of our knowledge) in the human gut and reported over a hundred cyclopeptides in other environments represented by various spectra on Global Natural Products Social Molecular Network (GNPS). https://github.com/bbehsaz/cyclonovo.