Cycloquest: identification of cyclopeptides via database search of their mass spectra against genome databases

Targeted Large-Scale Genome Mining and Candidate Prioritization for Natural Product Discovery

Large-scale genome-mining analyses have identified an enormous number of cryptic biosynthetic gene clusters (BGCs) as a great source of novel bioactive natural products. Given the sheer number of natural product (NP) candidates, effective strategies and computational methods are keys to choosing appropriate BGCs for further NP characterization and production. This review discusses genomics-based approaches for prioritizing candidate BGCs extracted from large-scale genomic data, by highlighting studies that have successfully produced compounds with high chemical novelty, novel biosynthesis pathway, and potent bioactivities. We group these studies based on their BGC-prioritization logics: detecting presence of resistance genes, use of phylogenomics analysis as a guide, and targeting for specific chemical structures. We also briefly comment on the different bioinformatics tools used in the field and examine practical considerations when employing a large-scale genome mining study.

Natural Cyclopeptides as Anticancer Agents in the Last 20 Years

Cyclopeptides or cyclic peptides are polypeptides formed by ring closing of terminal amino acids. A large number of natural cyclopeptides have been reported to be highly effective against different cancer cells, some of which are renowned for their clinical uses. Compared to linear peptides, cyclopeptides have absolute advantages of structural rigidity, biochemical stability, binding affinity as well as membrane permeability, which contribute greatly to their anticancer potency. Therefore, the discovery and development of natural cyclopeptides as anticancer agents remains attractive to academic researchers and pharmaceutical companies. Herein, we provide an overview of anticancer cyclopeptides that were discovered in the past 20 years. The present review mainly focuses on the anticancer efficacies, mechanisms of action and chemical structures of cyclopeptides with natural origins. Additionally, studies of the structure-activity relationship, total synthetic strategies as well as bioactivities of natural cyclopeptides are also included in this article. In conclusion, due to their characteristic structural features, natural cyclopeptides have great potential to be developed as anticancer agents. Indeed, they can also serve as excellent scaffolds for the synthesis of novel derivatives for combating cancerous pathologies.

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.

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

Peptidic natural products (PNPs) include many antibiotics and other bioactive compounds. While the recent launch of the Global Natural Products Social (GNPS) molecular networking infrastructure is transforming PNP discovery into a high-throughput technology, PNP identification algorithms are needed to realize the potential of the GNPS project. GNPS relies on the assumption that each connected component of a molecular network (representing related metabolites) illuminates the 'dark matter of metabolomics' as long as it contains a known metabolite present in a database. We reveal a surprising diversity of PNPs produced by related bacteria and show that, contrary to the 'comparative metabolomics' assumption, two related bacteria are unlikely to produce identical PNPs (even though they are likely to produce similar PNPs). Since this observation undermines the utility of GNPS, we developed a PNP identification tool, VarQuest, that illuminates the connected components in a molecular network even if they do not contain known PNPs and only contain their variants. VarQuest reveals an order of magnitude more PNP variants than all previous PNP discovery efforts and demonstrates that GNPS already contains spectra from 41% of the currently known PNP families. The enormous diversity of PNPs suggests that biosynthetic gene clusters in various microorganisms constantly evolve to generate a unique spectrum of PNP variants that differ from PNPs in other species.

Cyclisation mechanisms in the biosynthesis of ribosomally synthesised and post-translationally modified peptides

Ribosomally synthesised and post-translationally modified peptides (RiPPs) are a large class of natural products that are remarkably chemically diverse given an intrinsic requirement to be assembled from proteinogenic amino acids. The vast chemical space occupied by RiPPs means that they possess a wide variety of biological activities, and the class includes antibiotics, co-factors, signalling molecules, anticancer and anti-HIV compounds, and toxins. A considerable amount of RiPP chemical diversity is generated from cyclisation reactions, and the current mechanistic understanding of these reactions will be discussed here. These cyclisations involve a diverse array of chemical reactions, including 1,4-nucleophilic additions, [4 + 2] cycloadditions, ATP-dependent heterocyclisation to form thiazolines or oxazolines, and radical-mediated reactions between unactivated carbons. Future prospects for RiPP pathway discovery and characterisation will also be highlighted.

Informatic search strategies to discover analogues and variants of natural product archetypes

Natural products are a crucial source of antimicrobial agents, but reliance on low-resolution bioactivity-guided approaches has led to diminishing interest in discovery programmes. Here, we demonstrate that two in-house automated informatic platforms can be used to target classes of biologically active natural products, specifically, peptaibols. We demonstrate that mass spectrometry-based informatic approaches can be used to detect natural products with high sensitivity, identifying desired agents present in complex microbial extracts. Using our specialised software packages, we could elaborate specific branches of chemical space, uncovering new variants of trichopolyn and demonstrating a way forward in mining natural products as a valuable source of potential pharmaceutical agents.

The secondary metabolite bioinformatics portal: Computational tools to facilitate synthetic biology of secondary metabolite production

Natural products are among the most important sources of lead molecules for drug discovery. With the development of affordable whole-genome sequencing technologies and other ‘omics tools, the field of natural products research is currently undergoing a shift in paradigms. While, for decades, mainly analytical and chemical methods gave access to this group of compounds, nowadays genomics-based methods offer complementary approaches to find, identify and characterize such molecules. This paradigm shift also resulted in a high demand for computational tools to assist researchers in their daily work. In this context, this review gives a summary of tools and databases that currently are available to mine, identify and characterize natural product biosynthesis pathways and their producers based on ‘omics data. A web portal called Secondary Metabolite Bioinformatics Portal (SMBP at http://www.secondarymetabolites.org) is introduced to provide a one-stop catalog and links to these bioinformatics resources. In addition, an outlook is presented how the existing tools and those to be developed will influence synthetic biology approaches in the natural products field.

Dereplication, sequencing and identification of peptidic natural products: from genome mining to peptidogenomics to spectral networks

Covering: 2000 to 2015. While recent breakthroughs in the discovery of peptide antibiotics and other Peptidic Natural Products (PNPs) raise a challenge for developing new algorithms for their analyses, the computational technologies for high-throughput PNP discovery are still lacking. We discuss the computational bottlenecks in analyzing PNPs and review recent advances in genome mining, peptidogenomics, and spectral networks that are now enabling the discovery of new PNPs via mass spectrometry. We further describe the connections between these advances and the new generation of software tools for PNP dereplication, de novo sequencing, and identification.

Identification and Structural Characterization of Naturally-Occurring Broad-Spectrum Cyclic Antibiotics Isolated from Paenibacillus

The rise of antimicrobial resistance necessitates the discovery and/or production of novel antibiotics. Isolated strains of Paenibacillus alvei were previously shown to exhibit antimicrobial activity against a number of pathogens, such as E. coli, Salmonella, and methicillin-resistant Staphylococcus aureus (MRSA). The responsible antimicrobial compounds were isolated from these Paenibacillus strains and a combination of low and high resolution mass spectrometry with multiple-stage tandem mass spectrometry was used for identification. A group of closely related cyclic lipopeptides was identified, differing primarily by fatty acid chain length and one of two possible amino acid substitutions. Variation in the fatty acid length resulted in mass differences of 14 Da and yielded groups of related MS(n) spectra. Despite the inherent complexity of MS/MS spectra of cyclic compounds, straightforward analysis of these spectra was accomplished by determining differences in complementary product ion series between compounds that differ in molecular weight by 14 Da. The primary peptide sequence assignment was confirmed through genome mining; the combination of these analytical tools represents a workflow that can be used for the identification of complex antibiotics. The compounds also share amino acid sequence similarity to a previously identified broad-spectrum antibiotic isolated from Paenibacillus. The presence of such a wide distribution of related compounds produced by the same organism represents a novel class of broad-spectrum antibiotic compounds.

Exploration of Nonribosomal Peptide Families with an Automated Informatic Search Algorithm

Microbial natural products are some of the most important pharmaceutical agents and possess unparalleled chemical diversity. Here we present an untargeted metabolomics algorithm that builds on our validated iSNAP platform to rapidly identify families of peptide natural products. By utilizing known or in silico-dereplicated seed structures, this algorithm screens tandem mass spectrometry data to elaborate extensive molecular families within crude microbial culture extracts with high confidence and statistical significance. Analysis of peptide natural product producers revealed an abundance of unreported congeners, revealing one of the largest families of natural products described to date, as well as a novel variant with greater potency. These findings demonstrate the effectiveness of the iSNAP platform as an accurate tool for rapidly profiling large families of nonribosomal peptides.

Computational approaches to natural product discovery

Starting with the earliest Streptomyces genome sequences, the promise of natural product genome mining has been captivating: genomics and bioinformatics would transform compound discovery from an ad hoc pursuit to a high-throughput endeavor. Until recently, however, genome mining has advanced natural product discovery only modestly. Here, we argue that the development of algorithms to mine the continuously increasing amounts of (meta)genomic data will enable the promise of genome mining to be realized. We review computational strategies that have been developed to identify biosynthetic gene clusters in genome sequences and predict the chemical structures of their products. We then discuss networking strategies that can systematize large volumes of genetic and chemical data and connect genomic information to metabolomic and phenotypic data. Finally, we provide a vision of what natural product discovery might look like in the future, specifically considering longstanding questions in microbial ecology regarding the roles of metabolites in interspecies interactions.

The chemistry and biology of theta defensins

Cyclic peptides are found in a diverse range of organisms and are characterized by their stability and role in defense. Why is only one class of cyclic peptides found in mammals? Possibly we have not looked hard enough for them, or the technologies needed to identify them are not fully developed. We also do not yet understand their intriguing biosynthesis from two separate gene products. Addressing these challenges will require the application of chemical tools and insights from other classes of cyclic peptides. Herein, we highlight recent developments in the characterization of theta defensins and describe the important role that chemistry has played in delineating their modes of action. Furthermore, we emphasize the potential of theta defensins as antimicrobial agents and scaffolds for peptide drug design.

NRPquest: Coupling Mass Spectrometry and Genome Mining for Nonribosomal Peptide Discovery

Nonribosomal peptides (NRPs) such as vancomycin and daptomycin are among the most effective antibiotics. While NRPs are biomedically important, the computational techniques for sequencing these peptides are still in their infancy. The recent emergence of mass spectrometry techniques for NRP analysis (capable of sequencing an NRP from small amounts of nonpurified material) revealed an enormous diversity of NRPs. However, as many NRPs have nonlinear structure (e.g., cyclic or branched-cyclic peptides), the standard de novo sequencing tools (developed for linear peptides) are not applicable to NRP analysis. Here, we introduce the first NRP identification algorithm, NRPquest, that performs mutation-tolerant and modification-tolerant searches of spectral data sets against a database of putative NRPs. In contrast to previous studies aimed at NRP discovery (that usually report very few NRPs), NRPquest revealed nearly a hundred NRPs (including unknown variants of previously known peptides) in a single study. This result indicates that NRPquest can potentially make MS-based NRP identification as robust as the identification of linear peptides in traditional proteomics.

Automated genome mining of ribosomal peptide natural products

Ribosomally synthesized and posttranslationally modified peptides (RiPPs), especially from microbial sources, are a large group of bioactive natural products that are a promising source of new (bio)chemistry and bioactivity.1 In light of exponentially increasing microbial genome databases and improved mass spectrometry (MS)-based metabolomic platforms, there is a need for computational tools that connect natural product genotypes predicted from microbial genome sequences with their corresponding chemotypes from metabolomic data sets. Here, we introduce RiPPquest, a tandem mass spectrometry database search tool for identification of microbial RiPPs, and apply it to lanthipeptide discovery. RiPPquest uses genomics to limit search space to the vicinity of RiPP biosynthetic genes and proteomics to analyze extensive peptide modifications and compute p-values of peptide-spectrum matches (PSMs). We highlight RiPPquest by connecting multiple RiPPs from extracts of Streptomyces to their gene clusters and by the discovery of a new class III lanthipeptide, informatipeptin, from Streptomyces viridochromogenes DSM 40736 to reflect that it is a natural product that was discovered by mass spectrometry based genome mining using algorithmic tools rather than manual inspection of mass spectrometry data and genetic information. The presented tool is available at cyclo.ucsd.edu.

Mass spectrometry of natural products: current, emerging and future technologies

Although mass spectrometry is a century old technology, we are entering into an exciting time for the analysis of molecular information directly from complex biological systems. In this Highlight, we feature emerging mass spectrometric methods and tools used by the natural product community and give a perspective of future directions where the mass spectrometry field is migrating towards over the next decade.

CYCLONE--a utility for de novo sequencing of microbial cyclic peptides

We have developed a de novo sequencing software tool (CYCLONE) and applied it for determination of cyclic peptides. The program uses a non-redundant database of 312 nonribosomal building blocks identified to date in bacteria and fungi (more than 230 additional residues in the database list were isobaric). The software was used to fully characterize the tandem mass spectrum of several cyclic peptides and provide sequence tags. The general strategy of the script was based on fragment ion pre-characterization to accomplish unambiguous b-ion series assignments. Showcase examples were a cyclic tetradepsipeptide beauverolide, a cyclic hexadepsipeptide roseotoxin A, a lasso-like hexapeptide pseudacyclin A, and a cyclic undecapeptide cyclosporin A. The extent of ion scrambling in smaller peptides was as low as 5 % of total ion current; this demonstrated the feasibility of CYCLONE de novo sequencing. The robustness of the script was also tested against database sets of various sizes and isotope-containing data. It can be downloaded from the http://ms.biomed.cas.cz/MSTools/ website. ᅟ

A new approach to evaluating statistical significance of spectral identifications

While nonlinear peptide natural products such as Vancomycin and Daptomycin are among the most effective antibiotics, the computational techniques for sequencing such peptides are still in their infancy. Previous methods for sequencing peptide natural products are based on Nuclear Magnetic Resonance spectroscopy and require large amounts (milligrams) of purified materials. Recently, development of mass spectrometry-based methods has enabled accurate sequencing of nonlinear peptide natural products using picograms of material, but the question of evaluating statistical significance of Peptide Spectrum Matches (PSM) for these peptides remains open. Moreover, it is unclear how to decide whether a given spectrum is produced by a linear, cyclic, or branch-cyclic peptide. Surprisingly, all previous mass spectrometry studies overlooked the fact that a very similar problem has been successfully addressed in particle physics in 1951. In this work, we develop a method for estimating statistical significance of PSMs defined by any peptide (including linear and nonlinear). This method enables us to identify whether a peptide is linear, cyclic, or branch-cyclic, an important step toward identification of peptide natural products.

Circular proteins from plants and fungi

Circular proteins, defined as head-to-tail cyclized polypeptides originating from ribosomal synthesis, represent a novel class of natural products attracting increasing interest. From a scientific point of view, these compounds raise questions of where and why they occur in nature and how they are formed. From a rational point of view, these proteins and their structural concept may be exploited for crop protection and novel pharmaceuticals. Here, we review the current knowledge of three protein families: cyclotides and circular sunflower trypsin inhibitors from the kingdom of plants and the Amanita toxins from fungi. A particular emphasis is placed on their biological origin, structure, and activity. In addition, the opportunity for discovery of novel circular proteins and recent insights into their mechanism of action are discussed.

Lessons from the past and charting the future of marine natural products drug discovery and chemical biology

Marine life forms are an important source of structurally diverse and biologically active secondary metabolites, several of which have inspired the development of new classes of therapeutic agents. These success stories have had to overcome difficulties inherent to natural products-derived drugs, such as adequate sourcing of the agent and issues related to structural complexity. Nevertheless, several marine-derived agents are now approved, most as "first-in-class" drugs, with five of seven appearing in the past few years. Additionally, there is a rich pipeline of clinical and preclinical marine compounds to suggest their continued application in human medicine. Understanding of how these agents are biosynthetically assembled has accelerated in recent years, especially through interdisciplinary approaches, and innovative manipulations and re-engineering of some of these gene clusters are yielding novel agents of enhanced pharmaceutical properties compared with the natural product.

Informatic strategies for the discovery of polyketides and nonribosomal peptides

A modern challenge and opportunity exists for in the ability to link genomic and metabolomic data, using novel informatic methods to find new bioactive natural products.