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

An interlaboratory proficiency test using metagenomic sequencing as a diagnostic tool for the detection of RNA viruses in swine fecal material

Metagenomic shotgun sequencing (mNGS) can serve as a generic molecular diagnostic tool. An mNGS proficiency test (PT) was performed in six European veterinary and public health laboratories to detect porcine astroviruses in fecal material and the extracted RNA. While different mNGS workflows for the generation of mNGS data were used in the different laboratories, the bioinformatic analysis was standardized using a metagenomic read classifier as well as read mapping to selected astroviral reference genomes to assess the semiquantitative representation of astrovirus species mixtures. All participants successfully identified and classified most of the viral reads to the two dominant species. The normalized read counts obtained by aligning reads to astrovirus reference genomes by Bowtie2 were in line with Kraken read classification counts. Moreover, participants performed well in terms of repeatability when the fecal sample was tested in duplicate. However, the normalized read counts per detected astrovirus species differed substantially between participants, which was related to the different laboratory methods used for data generation. Further modeling of the mNGS data indicated the importance of selecting appropriate reference data for mNGS read classification. As virus- or sample-specific biases may apply, caution is needed when extrapolating this swine feces-based PT for the detection of other RNA viruses or using different sample types. The suitability of experimental design to a given pathogen/sample matrix combination, quality assurance, interpretation, and follow-up investigation remain critical factors for the diagnostic interpretation of mNGS results.

IMPORTANCE

Metagenomic shotgun sequencing (mNGS) is a generic molecular diagnostic method, involving laboratory preparation of samples, sequencing, bioinformatic analysis of millions of short sequences, and interpretation of the results. In this paper, we investigated the performance of mNGS on the detection of porcine astroviruses, a model for RNA viruses in a pig fecal material, among six European veterinary and public health laboratories. We showed that different methods for data generation affect mNGS performance among participants and that the selection of reference genomes is crucial for read classification. Follow-up investigation remains a critical factor for the diagnostic interpretation of mNGS results. The paper contributes to potential improvements of mNGS as a diagnostic tool in clinical settings.

Whole-genome sequencing for antimicrobial surveillance: species-specific quality thresholds and data evaluation from the network of the European Union Reference Laboratory for Antimicrobial Resistance genomic proficiency tests of 2021 and 2022

As antimicrobial resistance (AMR) surveillance shifts to genomics, ensuring the quality of whole-genome sequencing (WGS) data produced across laboratories is critical. Participation in genomic proficiency tests (GPTs) not only increases individual laboratories' WGS capacity but also provides a unique opportunity to improve species-specific thresholds for WGS quality control (QC) by repeated resequencing of distinct isolates. Here, we present the results of the EU Reference Laboratory for Antimicrobial Resistance (EURL-AR) network GPTs of 2021 and 2022, which included 25 EU national reference laboratories (NLRs). A total of 392 genomes from 12 AMR-bacteria were evaluated based on WGS QC metrics. Two percent (n = 9) of the data were excluded, due to contamination, and 11% (n = 41) of the remaining genomes were identified as outliers in at least one QC metric and excluded from computation of the adjusted QC thresholds (AQT). Two QC metric correlation groups were identified through linear regression. Eight percent (n = 28) of the submitted genomes, from 11 laboratories, failed one or more of the AQTs. However, only three laboratories (12%) were identified as underperformers, failing across AQTs for uncorrelated QC metrics in at least two genomes. Finally, new species-specific thresholds for "N50" and "number of contigs > 200 bp" are presented for guidance in routine laboratory QC. The continued participation of NRLs in GPTs will reveal WGS workflow flaws and improve AMR surveillance data. GPT data will continue to contribute to the development of reliable species-specific thresholds for routine WGS QC, standardizing sequencing data QC and ensure inter- and intranational laboratory comparability.IMPORTANCEIllumina next-generation sequencing is an integral part of antimicrobial resistance (AMR) surveillance and the most widely used whole-genome sequencing (WGS) platform. The high-throughput, relative low-cost, high discriminatory power, and rapid turnaround time of WGS compared to classical biochemical methods means the technology will likely remain a fundamental tool in AMR surveillance and public health. In this study, we present the current level of WGS capacity among national reference laboratories in the EU Reference Laboratory for AMR network, summarizing applied methodology and statistically evaluating the quality of the obtained sequence data. These findings provide the basis for setting new and revised thresholds for quality metrics used in routine WGS, which have previously been arbitrarily defined. In addition, underperforming participants are identified and encouraged to evaluate their workflows to produce reliable results.

Plant peptides - redefining an area of ribosomally synthesized and post-translationally modified peptides

Covering 1965 to February 2024Plants are prolific peptide chemists and are known to make thousands of different peptidic molecules. These peptides vary dramatically in their size, chemistry, and bioactivity. Despite their differences, all plant peptides to date are biosynthesized as ribosomally synthesized and post-translationally modified peptides (RiPPs). Decades of research in plant RiPP biosynthesis have extended the definition and scope of RiPPs from microbial sources, establishing paradigms and discovering new families of biosynthetic enzymes. The discovery and elucidation of plant peptide pathways is challenging due to repurposing and evolution of housekeeping genes as both precursor peptides and biosynthetic enzymes and due to the low rates of gene clustering in plants. In this review, we highlight the chemistry, biosynthesis, and function of the known RiPP classes from plants and recommend a nomenclature for the recent addition of BURP-domain-derived RiPPs termed burpitides. Burpitides are an emerging family of cyclic plant RiPPs characterized by macrocyclic crosslinks between tyrosine or tryptophan side chains and other amino acid side chains or their peptide backbone that are formed by copper-dependent BURP-domain-containing proteins termed burpitide cyclases. Finally, we review the discovery of plant RiPPs through bioactivity-guided, structure-guided, and gene-guided approaches.

Different sampling strategies for optimal detection of the overall genetic diversity of methicillin-resistant Staphylococcus aureus

Surveillance schemes for methicillin-resistant Staphylococcus aureus (MRSA) are widely established at the national and international levels. Due to the simple standardization of the protocol, mainly isolates from bloodstream infections are used. However, the limitations of this simple surveillance system are well described. We conducted a comprehensive analysis of MRSA isolates in a large Slovenian region over 5 years to identify the optimal sample group for assessing the overall MRSA diversity. At the same time, this study provides to date non-available molecular characterization of Slovenian MRSA isolates. A total of 306 MRSA isolates from various sources were sequenced and phenotypically tested for resistance. The isolates exhibited significant molecular diversity, encompassing 30 multi locus sequence type (MLST) sequence types (STs), 39 ST-SCCmec genetic lineages, 49 spa types, and 29 antibiotic resistance profiles. Furthermore, the isolate pool comprised 57 resistance genes, representing 22 resistance mechanisms, and 96 virulence genes. While bloodstream isolates, commonly used in surveillance, provided insights into frequently detected clones, they overlooked majority of clones and important virulence and resistance genes. Blood culture isolates detected 21.3% spa types, 24.1% resistance phenotypes, and 28.2% MLST-SCCmec profiles. In contrast, strains from soft tissues demonstrated superior genomic diversity capture, with 65.3% spa types, 58.6% resistance phenotypes, and 71.8% MLST-SCCmec profiles. These strains also encompassed 100.0% of virulence and 82.5% of resistance genes, making them better candidates for inclusion in surveillance programs. This study highlights the limitations of relying solely on bloodstream isolates in MRSA surveillance and suggests incorporating strains from soft tissues to obtain a more comprehensive understanding of the epidemiology of MRSA.IMPORTANCEIn this study, we investigated the diversity of methicillin-resistant Staphylococcus aureus (MRSA), a bacterium that can cause infections that are difficult to treat due to its resistance to antimicrobial agents. Currently, surveillance programs for MRSA mainly rely on isolates from bloodstream infections, employing a standardized protocol. However, this study highlights the limitations of this approach and introduces a more comprehensive method. The main goal was to determine which group of samples is best suited to understand the overall diversity of MRSA and to provide, for the first time, molecular characterization of Slovenian MRSA isolates. Our results suggest that including MRSA strains from soft tissue infections rather than just blood infections provides a more accurate and comprehensive view of bacterial diversity and characteristics. This insight is valuable for improving the effectiveness of surveillance programs and for developing strategies to better manage MRSA infections.

Compendium of Metabolomic and Genomic Datasets for Cyanobacteria: Mined the Gap

Cyanobacterial blooms, producing toxic secondary metabolites, are becoming increasingly common phenomena in the face of rising global temperatures. They are the world's most abundant photosynthetic organisms, largely owing their success to a range of highly diverse and complex natural products possessing a broad spectrum of different bioactivities. Over 2600 compounds have been isolated from cyanobacteria thus far, and their characterisation has revealed unusual and useful chemistries and motifs including alkynes, halogens, and non-canonical amino acids. Genome sequencing of cyanobacteria lags behind natural product isolation, with only 19% of cyanobacterial natural products associated with a sequenced organism. Recent advances in meta(genomics) provide promise to narrow this gap and has also facilitated the uprise of combined genomic and metabolomic approaches, heralding a new era of discovery of novel compounds. Analyses of the datasets described within this manuscript reveal the asynchrony of current genomic and metabolomic data, highlight the chemical diversity of cyanobacterial natural products. Linked to this manuscript, we make these manually curated datasets freely accessible for the public to facilitate further research in this important area.

A Tag-Free Platform for Synthesis and Screening of Cyclic Peptide Libraries

In the realm of high-throughput screening (HTS), macrocyclic peptide libraries traditionally necessitate decoding tags, essential for both library synthesis and identifying hit peptide sequences post-screening. Our innovation introduces a tag-free technology platform for synthesizing cyclic peptide libraries in solution and facilitates screening against biological targets to identify peptide binders through unconventional intramolecular CyClick and DeClick chemistries (CCDC) discovered through our research. This combination allows for the synthesis of diverse cyclic peptide libraries, the incorporation of various amino acids, and facile linearization and decoding of cyclic peptide binder sequences. Our sensitivity-enhancing derivatization method, utilized in tandem with nano LC-MS/MS, enables the sequencing of peptides even at exceedingly low picomolar concentrations. Employing our technology platform, we have successfully unearthed novel cyclic peptide binders against a monoclonal antibody and the first cyclic peptide binder of HIV capsid protein responsible for viral infections as validated by microscale thermal shift assays (TSA), biolayer interferometry (BLI) and functional assays.

Genome sequence of Alteromonas macleodii strain OCN004 isolated from the extracellular mucus of an apparently healthy rice coral (Montipora capitata)

Alteromonas macleodii strain OCN004, a marine gammaproteobacterium in the Alteromonadaceae family, has primarily been studied as a non-pathogenic negative control bacterium during laboratory infection trials to test the virulence of bacterial coral pathogens. The draft genome sequence of A. macleodii strain OCN004 is presented here.

De Novo Sequencing of Synthetic Bis-cysteine Peptide Macrocycles Enabled by "Chemical Linearization" of Compound Mixtures

A "chemical linearization" approach was applied to synthetic peptide macrocycles to enable their de novo sequencing from mixtures using nanoliquid chromatography-tandem mass spectrometry (nLC-MS/MS). This approach─previously applied to individual macrocycles but not to mixtures─involves cleavage of the peptide backbone at a defined position to give a product capable of generating sequence-determining fragment ions. Here, we first established the compatibility of "chemical linearization" by Edman degradation with a prominent macrocycle scaffold based on bis-Cys peptides cross-linked with the m-xylene linker, which are of major significance in therapeutics discovery. Then, using macrocycle libraries of known sequence composition, the ability to recover accurate de novo assignments to linearized products was critically tested using performance metrics unique to mixtures. Significantly, we show that linearized macrocycles can be sequenced with lower recall compared to linear peptides but with similar accuracy, which establishes the potential of using "chemical linearization" with synthetic libraries and selection procedures that yield compound mixtures. Sodiated precursor ions were identified as a significant source of high-scoring but inaccurate assignments, with potential implications for improving automated de novo sequencing more generally.

Rapid Unambiguous Structure Elucidation of Streptnatamide A, a New Cyclic Peptide Isolated from A Marine-derived Streptomyces sp

Cyclic peptides have been excellent source of drug leads. With the advances in discovery platforms, the pharmaceutical industry has a growing interest in cyclic peptides and has pushed several into clinical trials. However, structural complexity of cyclic peptides brings extreme challenges for structure elucidation efforts. Isotopic fine structure analysis, Nuclear magnetic resonance (NMR), and detailed tandem mass spectrometry rapidly provided peptide sequence for streptnatamide A, a cyclic peptide isolated from a marine-derived Streptomyces sp. Marfey's analysis determined the stereochemistry of all amino acids, enabling the unambiguous structure determination of this compound. A non-ribosomal peptide synthetase biosynthetic gene cluster (stp) was tentatively identified and annotated for streptnatamide A based on the in silico analysis of whole genome sequencing data. These analytical tools will be powerful tools to overcome the challenges for cyclic peptide structure elucidation and accelerate the development of bioactive cyclic peptides.

Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review

Peptide sequencing is of great significance to fundamental and applied research in the fields such as chemical, biological, medicinal and pharmaceutical sciences. With the rapid development of mass spectrometry and sequencing algorithms, de-novo peptide sequencing using tandem mass spectrometry (MS/MS) has become the main method for determining amino acid sequences of novel and unknown peptides. Advanced algorithms allow the amino acid sequence information to be accurately obtained from MS/MS spectra in short time. In this review, algorithms from exhaustive search to the state-of-art machine learning and neural network for high-throughput and automated de-novo sequencing are introduced and compared. Impacts of datasets on algorithm performance are highlighted. The current limitations and promising direction of de-novo peptide sequencing are also discussed in this review.

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.

Dietary Modulation of the Human Gut Microbiota and Metabolome with Flaxseed Preparations

Flaxseeds are typically consumed either as whole flaxseed, ground flaxseed, flaxseed oil, partially defatted flaxseed meal, or as a milk alternative. They are considered a rich source of vitamins, minerals, proteins and peptides, lipids, carbohydrates, lignans, and dietary fiber, which have shown hypolipidemic, antiatherogenic, anticholesterolemic, and anti-inflammatory property activity. Here, an in vitro batch culture model was used to investigate the influence of whole milled flaxseed and partially defatted milled flaxseed press cake on the gut microbiota and the liberation of flaxseed bioactives. Microbial communities were profiled using 16S rRNA gene-based high-throughput sequencing with targeted mass spectrometry measuring lignan, cyclolinopeptide, and bile acid content and HPLC for short-chain fatty acid profiles. Flaxseed supplementation decreased gut microbiota richness with Firmicutes, Proteobacteria, and Bacteroidetes becoming the predominant phyla. Secoisolariciresinol, enterodiol, and enterolactone were rapidly produced with acetic acid, butyric acid, and propionic acid being the predominant acids after 24 h of fermentation. The flaxseed press cake and whole flaxseed were equivalent in microbiota changes and functionality. However, press cake may be superior as a functional additive in a variety of foods in terms of consumer acceptance as it would be more resistant to oxidative changes.

The Potential Role of Cyclopeptides from Pseudostellaria heterophylla, Linum usitatissimum and Drymaria diandra, and Peptides Derived from Heterophyllin B as Dipeptidyl Peptidase IV Inhibitors for the Treatment of Type 2 Diabetes: An In Silico Study

Dipeptidyl peptidase 4 (DPP4) inhibitors can treat type 2 diabetes by slowing GLP-1 degradation to increase insulin secretion. Studies have reported that Pseudostellaria heterophylla, Linum usita-tissimum (flaxseed), and Drymaria diandra, plants rich in Caryophyllaceae-type cyclopeptides and commonly used as herbal or dietary supplements, are effective in controlling blood sugar. The active site of DPP4 is in a cavity large enough to accommodate their cyclopeptides. Molecular modeling by AutoDock Vina reveals that certain cyclopeptides in these plants have the potential for DPP4 inhibition. In particular, “Heterophyllin B” from P. heterophylla, “Cyclolinopeptide C” from flaxseed, and “Diandrine C” from D. diandra, with binding affinities of −10.4, −10.0, and −10.7 kcal/mol, are promising. Docking suggests that DPP4 inhibition may be one of the reasons why these three plants are beneficial for lowering blood sugar. Because many protein hydrolysates have shown the effect of DPP4 inhibition, a series of peptides derived from Heterophyllin B precursor “IFGGLPPP” were included in the study. It was observed that IFWPPP (−10.5 kcal/mol), IFGGWPPP (−11.4 kcal/mol), and IFGWPPP (−12.0 kcal/mol) showed good binding affinity and interaction for DPP4. Various IFGGLPPP derivatives have the potential to serve as scaffolds for the design of novel DPP4 inhibitors.

Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies

Constant improvements in mass spectrometry technologies and laboratory workflows have enabled the proteomics investigation of biological samples of growing complexity. Microbiomes represent such complex samples for which metaproteomics analyses are becoming increasingly popular. Metaproteomics experimental procedures create large amounts of data from which biologically relevant signal must be efficiently extracted to draw meaningful conclusions. Such a data processing requires appropriate bioinformatics tools specifically developed for, or capable of handling metaproteomics data. In this chapter, we outline current and novel tools that can perform the most commonly used steps in the analysis of cutting-edge metaproteomics data, such as peptide and protein identification and quantification, as well as data normalization, imputation, mining, and visualization. We also provide details about the experimental setups in which these tools should be used.

Discovery and biosynthesis of cyclic plant peptides via autocatalytic cyclases

Many bioactive plant cyclic peptides form side-chain-derived macrocycles. Lyciumins, cyclic plant peptides with tryptophan macrocyclizations, are ribosomal peptides (RiPPs) originating from repetitive core peptide motifs in precursor peptides with plant-specific BURP (BNM2, USP, RD22 and PG1beta) domains, but the biosynthetic mechanism for their formation has remained unknown. Here, we characterize precursor-peptide BURP domains as copper-dependent autocatalytic peptide cyclases and use a combination of tandem mass spectrometry-based metabolomics and plant genomics to systematically discover five BURP-domain-derived plant RiPP classes, with mono- and bicyclic structures formed via tryptophans and tyrosines, from botanical collections. As BURP-domain cyclases are scaffold-generating enzymes in plant specialized metabolism that are physically connected to their substrates in the same polypeptide, we introduce a bioinformatic method to mine plant genomes for precursor-peptide-encoding genes by detection of repetitive substrate domains and known core peptide features. Our study sets the stage for chemical, biosynthetic and biological exploration of plant RiPP natural products from BURP-domain cyclases.

Structural Characterization of the PawL-Derived Peptide Family, an Ancient Subfamily of Orbitides

Plants are an excellent source of bioactive peptides, often with disulfide bonds and/or a cyclic backbone. While focus has predominantly been directed at disulfide-rich peptides, a large family of small, cyclic plant peptides lacking disulfide bonds, known as orbitides, has been relatively ignored. A recently discovered subfamily of orbitides is the PawL-derived peptides (PLPs), produced during the maturation of precursors for seed storage albumins. Although their evolutionary origins have been dated, in-depth exploration of the family's structural characteristics and potential bioactivities remains to be conducted. Here we present an extensive and systematic characterization of the PLP family. Nine PLPs were chosen and prepared by solid phase peptide synthesis. Their structural features were studied using solution NMR spectroscopy, and seven were found to possess regions of backbone order. Ordered regions consist of β-turns, with some PLPs adopting two well-defined β-turns within sequences as short as seven residues, which are largely the result of side chain interactions. Our data highlight that the sequence diversity within this family results in equally diverse structures. None of these nine PLPs showed antibacterial or antifungal activity.

Discovery and characterisation of an amidine-containing ribosomally-synthesised peptide that is widely distributed in nature

Ribosomally synthesised and post-translationally modified peptides (RiPPs) are a structurally diverse class of natural product with a wide range of bioactivities. Genome mining for RiPP biosynthetic gene clusters (BGCs) is often hampered by poor annotation of the short precursor peptides that are ultimately modified into the final molecule. Here, we utilise a previously described genome mining tool, RiPPER, to identify novel RiPP precursor peptides near YcaO-domain proteins, enzymes that catalyse various RiPP post-translational modifications including heterocyclisation and thioamidation. Using this dataset, we identified a novel and diverse family of RiPP BGCs spanning over 230 species of Actinobacteria and Firmicutes. A representative BGC from Streptomyces albidoflavus J1074 (formerly known as Streptomyces albus) was characterised, leading to the discovery of streptamidine, a novel amidine-containing RiPP. This new BGC family highlights the breadth of unexplored natural products with structurally rare features, even in model organisms.

Meeting report of the fourth annual Tri-Service Microbiome Consortium symposium

The Tri-Service Microbiome Consortium (TSMC) was founded to enhance collaboration, coordination, and communication of microbiome research among U.S. Department of Defense (DoD) organizations. The annual TSMC symposium is designed to enable information sharing between DoD scientists and leaders in the field of microbiome science, thereby keeping DoD consortium members informed of the latest advances within the microbiome community and facilitating the development of new collaborative research opportunities. The 2020 annual symposium was held virtually on 24-25 September 2020. Presentations and discussions centered on microbiome-related topics within four broad thematic areas: (1) Enabling Technologies; (2) Microbiome for Health and Performance; (3) Environmental Microbiome; and (4) Microbiome Analysis and Discovery. This report summarizes the presentations and outcomes of the 4th annual TSMC symposium.

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.

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.

Approaches for peptide and protein cyclisation

The cyclisation of polypeptides can play a crucial role in exerting biological functions, maintaining stability under harsh conditions and conferring proteolytic resistance, as demonstrated both in nature and in the laboratory. To date, various approaches have been reported for polypeptide cyclisation. These approaches range from the direct linkage of N- and C- termini to the connection of amino acid side chains, which can be applied both in reaction vessels and in living systems. In this review, we categorise the cyclisation approaches into chemical methods (e.g. direct backbone cyclisation, native chemical ligation, aldehyde-based ligations, bioorthogonal reactions, disulphide formation), enzymatic methods (e.g. subtiligase variants, sortases, asparaginyl endopeptidases, transglutaminases, non-ribosomal peptide synthetases) and protein tags (e.g. inteins, engineered protein domains for isopeptide bond formation). The features of each approach and the considerations for selecting an appropriate method of cyclisation are discussed.

Database-independent de novo metaproteomics of complex microbial communities

Metaproteomics has emerged as one of the most promising approaches for determining the composition and metabolic functions of complete microbial communities. Conventional metaproteomics approaches rely on the construction of protein sequence databases and efficient peptide-spectrum-matching algorithms, an approach that is intrinsically biased towards the content of the constructed sequence database. Here, we introduce a highly efficient, database-independent de novo metaproteomics approach and systematically evaluate its quantitative performance using synthetic and natural microbial communities comprising dozens of taxonomic families. Our work demonstrates that the de novo sequencing approach can vastly expand many metaproteomics applications by enabling rapid quantitative profiling and by capturing unsequenced community members that otherwise remain inaccessible for further interpretation. Kleikamp et al., describe a novel de novo metaproteomics pipeline (NovoBridge) that enables rapid community profiling without the need for constructing protein sequence databases.

The year 2020 in natural product bioinformatics: an overview of the latest tools and databases

Covering: 2020 Bioinformatic approaches to document and analyse chemical structures, biosynthetic gene clusters and analytical data play an important role in the study of natural products. Every year, such a large number of new algorithms, tools and databases are released, that it is difficult to keep track of all the latest developments. The aim of this short article is to provide a concise overview of and reference to the major tools, methods and databases that have been released in the past year.

metaFlye: scalable long-read metagenome assembly using repeat graphs

Long-read sequencing technologies have substantially improved the assemblies of many isolate bacterial genomes as compared to fragmented short-read assemblies. However, assembling complex metagenomic datasets remains difficult even for state-of-the-art long-read assemblers. Here we present metaFlye, which addresses important long-read metagenomic assembly challenges, such as uneven bacterial composition and intra-species heterogeneity. First, we benchmarked metaFlye using simulated and mock bacterial communities and show that it consistently produces assemblies with better completeness and contiguity than state-of-the-art long-read assemblers. Second, we performed long-read sequencing of the sheep microbiome and applied metaFlye to reconstruct 63 complete or nearly complete bacterial genomes within single contigs. Finally, we show that long-read assembly of human microbiomes enables the discovery of full-length biosynthetic gene clusters that encode biomedically important natural products.

The genetic origin of evolidine, the first cyclopeptide discovered in plants, and related orbitides

Cyclic peptides are reported to have antibacterial, antifungal, and other bioactivities. Orbitides are a class of cyclic peptides that are small, head-to-tail cyclized, composed of proteinogenic amino acids and lack disulfide bonds; they are also known in several genera of the plant family Rutaceae. Melicope xanthoxyloides is the Australian rain forest tree of the Rutaceae family in which evolidine, the first plant cyclic peptide, was discovered. Evolidine (cyclo-SFLPVNL) has subsequently been all but forgotten in the academic literature, so to redress this we used tandem MS and de novo transcriptomics to rediscover evolidine and decipher its biosynthetic origin from a short precursor just 48 residues in length. We also identified another six M. xanthoxyloides orbitides using the same techniques. These peptides have atypically diverse C termini consisting of residues not recognized by either of the known proteases plants use to macrocyclize peptides, suggesting new cyclizing enzymes await discovery. We examined the structure of two of the novel orbitides by NMR, finding one had a definable structure, whereas the other did not. Mining RNA-seq and whole genome sequencing data from other species of the Rutaceae family revealed that a large and diverse family of peptides is encoded by similar sequences across the family and demonstrates how powerful de novo transcriptomics can be at accelerating the discovery of new peptide families.

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.

Software tools, databases and resources in metabolomics: updates from 2018 to 2019

Metabolomics has evolved as a discipline from a discovery and functional genomics tool, and is now a cornerstone in the era of big data-driven precision medicine. Sample preparation strategies and analytical technologies have seen enormous growth, and keeping pace with data analytics is challenging, to say the least. This review introduces and briefly presents around 100 metabolomics software resources, tools, databases, and other utilities that have surfaced or have improved in 2019. Table 1 provides the computational dependencies of the tools, categorizes the resources based on utility and ease of use, and provides hyperlinks to webpages where the tools can be downloaded or used. This review intends to keep the community of metabolomics researchers up to date with all the software tools, resources, and databases developed in 2019, in one place.