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

A review on the diversity of antimicrobial peptides and genome mining strategies for their prediction

Antibiotic resistance has become one of the most serious threats to human health in recent years. In response to the increasing microbial resistance to the antibiotics currently available, it is imperative to develop new antibiotics or explore new approaches to combat antibiotic resistance. Antimicrobial peptides (AMPs) have shown considerable promise in this regard, as the microbes develop low or no resistance against them. The discovery and development of AMPs still confront numerous obstacles such as finding a target, developing assays, and identifying hits and leads, which are time-consuming processes, making it difficult to reach the market. However, with the advent of genome mining, new antibiotics could be discovered efficiently using tools such as BAGEL, antiSMASH, RODEO, etc., providing hope for better treatment of diseases in the future. Computational methods used in genome mining automatically detect and annotate biosynthetic gene clusters in genomic data, making it a useful tool in natural product discovery. This review aims to shed light on the history, diversity, and mechanisms of action of AMPs and the data on new AMPs identified by traditional as well as genome mining strategies. It further substantiates the various phases of clinical trials for some AMPs, as well as an overview of genome mining databases and tools built expressly for AMP discovery. In light of the recent advancements, it is evident that targeted genome mining stands as a beacon of hope, offering immense potential to expedite the discovery of novel antimicrobials.

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.

Harnessing the potential: advances in cyanobacterial natural product research and biotechnology

Covering: 2000 to 2023Cyanobacteria produce a variety of bioactive natural products that can pose a threat to humans and animals as environmental toxins, but also have potential for or inspire pharmaceutical use. As oxygenic phototrophs, cyanobacteria furthermore hold great promise for sustainable biotechnology. Yet, the necessary tools for exploiting their biotechnological potential have so far been established only for a few model strains of cyanobacteria, while large untapped biosynthetic resources are hidden in slow-growing cyanobacterial genera that are difficult to access by genetic techniques. In recent years, several approaches have been developed to circumvent the bottlenecks in cyanobacterial natural product research. Here, we summarize current progress that has been made in unlocking or characterizing cryptic metabolic pathways using integrated omics techniques, orphan gene cluster activation, use of genetic approaches in original producers, heterologous expression and chemo-enzymatic techniques. We are mainly highlighting genomic mining concepts and strategies towards high-titer production of cyanobacterial natural products from the last 10 years and discuss the need for further research developments in this field.

Learning sequence, structure, and function representations of proteins with language models

The sequence-structure-function relationships that ultimately generate the diversity of extant observed proteins is complex, as proteins bridge the gap between multiple informational and physical scales involved in nearly all cellular processes. One limitation of existing protein annotation databases such as UniProt is that less than 1% of proteins have experimentally verified functions, and computational methods are needed to fill in the missing information. Here, we demonstrate that a multi-aspect framework based on protein language models can learn sequence-structure-function representations of amino acid sequences, and can provide the foundation for sensitive sequence-structure-function aware protein sequence search and annotation. Based on this model, we introduce a multi-aspect information retrieval system for proteins, Protein-Vec, covering sequence, structure, and function aspects, that enables computational protein annotation and function prediction at tree-of-life scales.

Correlative metabologenomics of 110 fungi reveals metabolite-gene cluster pairs

Natural products research increasingly applies -omics technologies to guide molecular discovery. While the combined analysis of genomic and metabolomic datasets has proved valuable for identifying natural products and their biosynthetic gene clusters (BGCs) in bacteria, this integrated approach lacks application to fungi. Because fungi are hyper-diverse and underexplored for new chemistry and bioactivities, we created a linked genomics-metabolomics dataset for 110 Ascomycetes, and optimized both gene cluster family (GCF) networking parameters and correlation-based scoring for pairing fungal natural products with their BGCs. Using a network of 3,007 GCFs (organized from 7,020 BGCs), we examined 25 known natural products originating from 16 known BGCs and observed statistically significant associations between 21 of these compounds and their validated BGCs. Furthermore, the scalable platform identified the BGC for the pestalamides, demystifying its biogenesis, and revealed more than 200 high-scoring natural product-GCF linkages to direct future discovery.

AdenPredictor: accurate prediction of the adenylation domain specificity of nonribosomal peptide biosynthetic gene clusters in microbial genomes

SummaryMicrobial natural products represent a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class that include antibiotics, immunosuppressants, anticancer agents, toxins, siderophores, pigments, and cytostatics. The discovery of novel NRPs remains a laborious process because many NRPs consist of nonstandard amino acids that are assembled by nonribosomal peptide synthetases (NRPSs). Adenylation domains (A-domains) in NRPSs are responsible for selection and activation of monomers appearing in NRPs. During the past decade, several support vector machine-based algorithms have been developed for predicting the specificity of the monomers present in NRPs. These algorithms utilize physiochemical features of the amino acids present in the A-domains of NRPSs. In this article, we benchmarked the performance of various machine learning algorithms and features for predicting specificities of NRPSs and we showed that the extra trees model paired with one-hot encoding features outperforms the existing approaches. Moreover, we show that unsupervised clustering of 453 560 A-domains reveals many clusters that correspond to potentially novel amino acids. While it is challenging to predict the chemical structure of these amino acids, we developed novel techniques to predict their various properties, including polarity, hydrophobicity, charge, and presence of aromatic rings, carboxyl, and hydroxyl groups.

Deep self-supervised learning for biosynthetic gene cluster detection and product classification

Natural products are chemical compounds that form the basis of many therapeutics used in the pharmaceutical industry. In microbes, natural products are synthesized by groups of colocalized genes called biosynthetic gene clusters (BGCs). With advances in high-throughput sequencing, there has been an increase of complete microbial isolate genomes and metagenomes, from which a vast number of BGCs are undiscovered. Here, we introduce a self-supervised learning approach designed to identify and characterize BGCs from such data. To do this, we represent BGCs as chains of functional protein domains and train a masked language model on these domains. We assess the ability of our approach to detect BGCs and characterize BGC properties in bacterial genomes. We also demonstrate that our model can learn meaningful representations of BGCs and their constituent domains, detect BGCs in microbial genomes, and predict BGC product classes. These results highlight self-supervised neural networks as a promising framework for improving BGC prediction and classification.

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.

Strategizing the human microbiome for small molecules: Approaches and perspectives

Studies of the human microbiome are providing a deeper understanding of its significance to human health, and increasing evidence links the microbiota with several diseases. Nevertheless, the exact mechanisms involved in human-microbe interactions are mostly undefined. The genomic potential of the human microbiome to biosynthesize distinct molecules outmatches its known chemical space, and small-molecule discovery in this context remains in its infancy. The profiling of microbiome-derived small molecules and their contextualization through cause-effect mechanistic studies may provide a better understanding of host-microbe interactions, guide new therapeutic interventions, and modulate microbiome-based therapies. This review describes the advances, approaches, and allied challenges in mining new microbial scaffolds from the human microbiome using genomic, microbe cultivation, and chemical analytic platforms. In the future, the complete biological characterization of a single microbe-derived molecule that has a specific therapeutic application could resolve the current limitations of microbiota-modulating therapies.

Process Technologies of Cyanobacteria

Although the handling and exploitation of cyanobacteria is associated with some challenges, these phototrophic bacteria offer great opportunities for innovative biotechnological processes. This chapter covers versatile aspects of working with cyanobacteria, starting with up-to-date in silico and in vitro screening methods for bioactive substances. Subsequently, common conservation techniques and vitality/viability estimation methods are compared and supplemented by own data regarding the non-invasive vitality evaluation via pulse amplitude modulated fluorometry. Moreover, novel findings about the influence the state of the pre-cultures have on main cultures are presented. The following sub-chapters deal with different photobioreactor-designs, with special regard to biofilm photobioreactors, as well as with heterotrophic and mixotrophic cultivation modes. The latter topic provides information from literature on successfully enhanced cyanobacterial production processes, augmented by own data.

Artificial intelligence in microbial natural product drug discovery: current and emerging role

Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.

NPOmix: A machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters

Microbial specialized metabolites are an important source of and inspiration for many pharmaceuticals, biotechnological products and play key roles in ecological processes. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Efficiently linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs by allowing metabolomics to make use of genetic predictions. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical class). Our new pattern-based genome mining pipeline links biosynthetic genes to metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired datasets that include validated genes-mass spectral links from the Paired Omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra (17 for which the biosynthesis gene clusters can be found at the MIBiG database plus palmyramide A) to their corresponding previously experimentally validated biosynthetic genes (e.g., via nuclear magnetic resonance or genetic engineering). We illustrated a computational example of how to use our Natural Products Mixed Omics (NPOmix) tool for siderophore mining that can be reproduced by the users. We conclude that NPOmix minimizes the need for culturing (it worked well on microbiomes) and facilitates specialized metabolite prioritization based on integrative omics mining.

Identification of the Biosynthetic Gene Cluster for Pyracrimycin A, an Antibiotic Produced by Streptomyces sp

Actinomycetes make a wealth of complex, structurally diverse natural products, and a key challenge is to link them to their biosynthetic gene clusters and delineate the reactions catalyzed by each of the enzymes. Here, we report the biosynthetic gene cluster for pyracrimycin A, a set of nine genes that includes a core nonribosomal peptide synthase (pymB) that utilizes serine and proline as precursors and a monooxygenase (pymC) that catalyzes Baeyer-Villiger oxidation. The cluster is similar to the one for brabantamide A; however, pyracrimycin A biosynthesis differs in that feeding experiments with isotope-labeled serine and proline suggest that a ring opening reaction takes place and a carbon is lost from serine downstream of the oxidation reaction. Based on these data, we propose a full biosynthesis pathway for pyracrimycin A.

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.

In vivo and in silico screening for antimicrobial compounds from cyanobacteria

Due to the emerging rise of multi‐drug resistant bacteria, the discovery of novel antibiotics is of high scientific interest. Through their high chemodiversity of bioactive secondary metabolites, cyanobacteria have proven to be promising microorganisms for the discovery of antibacterial compounds. These aspects make appropriate antibacterial screening approaches for cyanobacteria crucial. Up to date, screenings are mostly carried out using a phenotypic methodology, consisting of cyanobacterial cultivation, extraction, and inhibitory assays. However, the parameters of these methods highly vary within the literature. Therefore, the common choices of parameters and inhibitory assays are summarized in this review. Nevertheless, less frequently used method variants are highlighted, which lead to hits from antimicrobial compounds. In addition to the considerations of phenotypic methods, this study provides an overview of developments in the genome‐based screening area, be it in vivo using PCR technique or in silico using the recent genome‐mining method. Though, up to date, these techniques are not applied as much as phenotypic screening.

An isotopic labeling approach linking natural products with biosynthetic gene clusters

Major advances in genome sequencing and large-scale biosynthetic gene cluster (BGC) analysis have prompted an age of natural product discovery driven by genome mining. Still, connecting molecules to their cognate BGCs is a substantial bottleneck for this approach. We have developed a mass-spectrometry-based parallel stable isotope labeling platform, termed IsoAnalyst, which assists in associating metabolite stable isotope labeling patterns with BGC structure prediction to connect natural products to their corresponding BGCs. Here we show that IsoAnalyst can quickly associate both known metabolites and unknown analytes with BGCs to elucidate the complex chemical phenotypes of these biosynthetic systems. We validate this approach for a range of compound classes, using both the type strain Saccharopolyspora erythraea and an environmentally isolated Micromonospora sp. We further demonstrate the utility of this tool with the discovery of lobosamide D, a new and structurally unique member of the family of lobosamide macrolactams.

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.

Advances in Microbiome-Derived Solutions and Methodologies Are Founding a New Era in Skin Health and Care

The microbiome, as a community of microorganisms and their structural elements, genomes, metabolites/signal molecules, has been shown to play an important role in human health, with significant beneficial applications for gut health. Skin microbiome has emerged as a new field with high potential to develop disruptive solutions to manage skin health and disease. Despite an incomplete toolbox for skin microbiome analyses, much progress has been made towards functional dissection of microbiomes and host-microbiome interactions. A standardized and robust investigation of the skin microbiome is necessary to provide accurate microbial information and set the base for a successful translation of innovations in the dermo-cosmetic field. This review provides an overview of how the landscape of skin microbiome research has evolved from method development (multi-omics/data-based analytical approaches) to the discovery and development of novel microbiome-derived ingredients. Moreover, it provides a summary of the latest findings on interactions between the microbiomes (gut and skin) and skin health/disease. Solutions derived from these two paths are used to develop novel microbiome-based ingredients or solutions acting on skin homeostasis are proposed. The most promising skin and gut-derived microbiome interventional strategies are presented, along with regulatory, safety, industrial, and technical challenges related to a successful translation of these microbiome-based concepts/technologies in the dermo-cosmetic industry.

Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities

Covering: 2010 to 2021Organisms in nature have evolved into proficient synthetic chemists, utilizing specialized enzymatic machinery to biosynthesize an inspiring diversity of secondary metabolites. Often serving to boost competitive advantage for their producers, these secondary metabolites have widespread human impacts as antibiotics, anti-inflammatories, and antifungal drugs. The natural products discovery field has begun a shift away from traditional activity-guided approaches and is beginning to take advantage of increasingly available metabolomics and genomics datasets to explore undiscovered chemical space. Major strides have been made and now enable -omics-informed prioritization of chemical structures for discovery, including the prospect of confidently linking metabolites to their biosynthetic pathways. Over the last decade, more integrated strategies now provide researchers with pipelines for simultaneous identification of expressed secondary metabolites and their biosynthetic machinery. However, continuous collaboration by the natural products community will be required to optimize strategies for effective evaluation of natural product biosynthetic gene clusters to accelerate discovery efforts. Here, we provide an evaluative guide to scientific literature as it relates to studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. Particular emphasis is placed on the unique insights that can be gained from large-scale integrated strategies, and we provide source organism-specific considerations to evaluate the gaps in our current knowledge.

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.

Mining and unearthing hidden biosynthetic potential

Genetically encoded small molecules (secondary metabolites) play eminent roles in ecological interactions, as pathogenicity factors and as drug leads. Yet, these chemical mediators often evade detection, and the discovery of novel entities is hampered by low production and high rediscovery rates. These limitations may be addressed by genome mining for biosynthetic gene clusters, thereby unveiling cryptic metabolic potential. The development of sophisticated data mining methods and genetic and analytical tools has enabled the discovery of an impressive array of previously overlooked natural products. This review shows the newest developments in the field, highlighting compound discovery from unconventional sources and microbiomes.

Natural products are an important source of bioactive compounds and have versatile applications in different fields, but their discovery is challenging. Here, the authors review the recent developments in genome mining for discovery of natural products, focusing on compounds from unconventional microorganisms and microbiomes.

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.

Synthetic biology-inspired strategies and tools for engineering of microbial natural product biosynthetic pathways

Microbial-derived natural products (NPs) and their derivative products are of great importance and used widely in many fields, especially in pharmaceutical industries. However, there is an immediate need to establish innovative approaches, strategies, and techniques to discover new NPs with novel or enhanced biological properties, due to the less productivity and higher cost on traditional drug discovery pipelines from natural bioresources. Revealing of untapped microbial cryptic biosynthetic gene clusters (BGCs) using DNA sequencing technology and bioinformatics tools makes genome mining possible for NP discovery from microorganisms. Meanwhile, new approaches and strategies in the area of synthetic biology offer great potentials for generation of new NPs by engineering or creating synthetic systems with improved and desired functions. Development of approaches, strategies and tools in synthetic biology can facilitate not only exploration and enhancement in supply, and also in the structural diversification of NPs. Here, we discussed recent advances in synthetic biology-inspired strategies, including bioinformatics and genetic engineering tools and approaches for identification, cloning, editing/refactoring of candidate biosynthetic pathways, construction of heterologous expression hosts, fitness optimization between target pathways and hosts and detection of NP production.

Coupling Mass Spectral and Genomic Information to Improve Bacterial Natural Product Discovery Workflows

Bacterial natural products possess potent bioactivities and high structural diversity and are typically encoded in biosynthetic gene clusters. Traditional natural product discovery approaches rely on UV- and bioassay-guided fractionation and are limited in terms of dereplication. Recent advances in mass spectrometry, sequencing and bioinformatics have led to large-scale accumulation of genomic and mass spectral data that is increasingly used for signature-based or correlation-based mass spectrometry genome mining approaches that enable rapid linking of metabolomic and genomic information to accelerate and rationalize natural product discovery. In this mini-review, these approaches are presented, and discovery examples provided. Finally, future opportunities and challenges for paired omics-based natural products discovery workflows are discussed.

Molecular engineering of antimicrobial peptides: microbial targets, peptide motifs and translation opportunities

The global public health threat of antimicrobial resistance has led the scientific community to highly engage into research on alternative strategies to the traditional small molecule therapeutics. Here, we review one of the most popular alternatives amongst basic and applied research scientists, synthetic antimicrobial peptides. The ease of peptide chemical synthesis combined with emerging engineering principles and potent broad-spectrum activity, including against multidrug-resistant strains, has motivated intense scientific focus on these compounds for the past decade. This global effort has resulted in significant advances in our understanding of peptide antimicrobial activity at the molecular scale. Recent evidence of molecular targets other than the microbial lipid membrane, and efforts towards consensus antimicrobial peptide motifs, have supported the rise of molecular engineering approaches and design tools, including machine learning. Beyond molecular concepts, supramolecular chemistry has been lately added to the debate; and helped unravel the impact of peptide self-assembly on activity, including on biofilms and secondary targets, while providing new directions in pharmaceutical formulation through taking advantage of peptide self-assembled nanostructures. We argue that these basic research advances constitute a solid basis for promising industry translation of rationally designed synthetic peptide antimicrobials, not only as novel drugs against multidrug-resistant strains but also as components of emerging antimicrobial biomaterials. This perspective is supported by recent developments of innovative peptide-based and peptide-carrier nanobiomaterials that we also review.

Linking biosynthetic and chemical space to accelerate microbial secondary metabolite discovery

Secondary metabolites can be viewed as a chemical language, facilitating communication between microorganisms. From an ecological point of view, this metabolite exchange is in constant flux due to evolutionary and environmental pressures. From a biomedical perspective, the chemistry is unsurpassed for its antibiotic properties. Genome sequencing of microorganisms has revealed a large reservoir of Biosynthetic Gene Clusters (BGCs); however, linking these to the secondary metabolites they encode is currently a major bottleneck to chemical discovery. This linking of genes to metabolites with experimental validation will aid the elicitation of silent or cryptic (not expressed under normal laboratory conditions) BGCs. As a result, this will accelerate chemical dereplication, our understanding of gene transcription and provide a comprehensive resource for synthetic biology. This will ultimately provide an improved understanding of both the biosynthetic and chemical space. In recent years, integrating these complex metabolomic and genomic data sets has been achieved using a spectrum of manual and automated approaches. In this review, we cover examples of these approaches, while addressing current challenges and future directions in linking these data sets.

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.

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.

A computational framework to explore large-scale biosynthetic diversity

Genome mining has become a key technology to exploit natural product diversity. Although initially performed on a single-genome basis, the process is now being scaled up to mine entire genera, strain collections and microbiomes. However, no bioinformatic framework is currently available for effectively analyzing datasets of this size and complexity. In the present study, a streamlined computational workflow is provided, consisting of two new software tools: the 'biosynthetic gene similarity clustering and prospecting engine' (BiG-SCAPE), which facilitates fast and interactive sequence similarity network analysis of biosynthetic gene clusters and gene cluster families; and the 'core analysis of syntenic orthologues to prioritize natural product gene clusters' (CORASON), which elucidates phylogenetic relationships within and across these families. BiG-SCAPE is validated by correlating its output to metabolomic data across 363 actinobacterial strains and the discovery potential of CORASON is demonstrated by comprehensively mapping biosynthetic diversity across a range of detoxin/rimosamide-related gene cluster families, culminating in the characterization of seven detoxin analogues.

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

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

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.

MetaMiner: A Scalable Peptidogenomics Approach for Discovery of Ribosomal Peptide Natural Products with Blind Modifications from Microbial Communities

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are an important class of natural products that contain antibiotics and a variety of other bioactive compounds. The existing methods for discovery of RiPPs by combining genome mining and computational mass spectrometry are limited to discovering specific classes of RiPPs from small datasets, and these methods fail to handle unknown post-translational modifications. Here, we present MetaMiner, a software tool for addressing these challenges that is compatible with large-scale screening platforms for natural product discovery. After searching millions of spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure against just eight genomic and metagenomic datasets, MetaMiner discovered 31 known and seven unknown RiPPs from diverse microbial communities, including human microbiome and lichen microbiome, and microorganisms isolated from the International Space Station.

From genomics to metabolomics, moving toward an integrated strategy for the discovery of fungal secondary metabolites

Fungal secondary metabolites are defined by bioactive properties that ensure adaptation of the fungus to its environment. Although some of these natural products are promising sources of new lead compounds especially for the pharmaceutical industry, others pose risks to human and animal health. The identification of secondary metabolites is critical to assessing both the utility and risks of these compounds. Since fungi present biological specificities different from other microorganisms, this review covers the different strategies specifically used in fungal studies to perform this critical identification. Strategies focused on the direct detection of the secondary metabolites are firstly reported. Particularly, advances in high-throughput untargeted metabolomics have led to the generation of large datasets whose exploitation and interpretation generally require bioinformatics tools. Then, the genome-based methods used to study the entire fungal metabolic potential are reported. Transcriptomic and proteomic tools used in the discovery of fungal secondary metabolites are presented as links between genomic methods and metabolomic experiments. Finally, the influence of the culture environment on the synthesis of secondary metabolites by fungi is highlighted as a major factor to consider in research on fungal secondary metabolites. Through this review, we seek to emphasize that the discovery of natural products should integrate all of these valuable tools. Attention is also drawn to emerging technologies that will certainly revolutionize fungal research and to the use of computational tools that are necessary but whose results should be interpreted carefully.

BiosyntheticSPAdes: reconstructing biosynthetic gene clusters from assembly graphs

Predicting biosynthetic gene clusters (BGCs) is critically important for discovery of antibiotics and other natural products. While BGC prediction from complete genomes is a well-studied problem, predicting BGCs in fragmented genomic assemblies remains challenging. The existing BGC prediction tools often assume that each BGC is encoded within a single contig in the genome assembly, a condition that is violated for most sequenced microbial genomes where BGCs are often scattered through several contigs, making it difficult to reconstruct them. The situation is even more severe in shotgun metagenomics, where the contigs are often short, and the existing tools fail to predict a large fraction of long BGCs. While it is difficult to assemble BGCs in a single contig, the structure of the genome assembly graph often provides clues on how to combine multiple contigs into segments encoding long BGCs. We describe biosyntheticSPAdes, a tool for predicting BGCs in assembly graphs and demonstrate that it greatly improves the reconstruction of BGCs from genomic and metagenomics data sets.

Toward a global picture of bacterial secondary metabolism

Bacterial metabolism is comprised of primary metabolites, the intracellular molecules of life that enable growth and proliferation, and secondary metabolites, predominantly extracellular molecules that facilitate a microbe's interaction with its environment. While our knowledge of primary metabolism and its web of interconnected intermediates is quantitative and holistic, significant knowledge gaps remain in our understanding of the secondary metabolomes of bacteria. In this Perspective, I discuss the main challenges involved in obtaining a global, comprehensive picture of bacterial secondary metabolomes, specifically in biosynthetically "gifted" microbes. Recent methodological advances that can meet these challenges will be reviewed. Applications of these methods combined with ongoing innovations will enable a detailed picture of global secondary metabolomes, which will in turn shed light onto the biology, chemistry, and enzymology underlying natural products and simultaneously aid drug discovery.

Bacteria Hunt Bacteria through an Intriguing Cyclic Peptide

In the last few decades, peptides have been victorious over small molecules as therapeutics due to their broad range of applications, high biological activity, and high specificity. However, the main challenges to overcome if peptides are to become effective drugs is their low oral bioavailability and instability under physiological conditions. Cyclic peptides play a vital role in this context because they show higher stability under physiological conditions, higher membrane permeability, and greater oral bioavailability than that of their corresponding linear analogues. In this regard, cyclic antimicrobial peptides (AMPs) have gained considerable attention in the field of novel antibiotic development. Bacterial strains produce cyclic AMPs through two pathways: ribosomal and nonribosomal. This review provides an overview of the chemical classification of cyclic AMPs isolated from bacteria, and provides a description of their biological activity and mode of action.

Concepts and Methods to Access Novel Antibiotics from Actinomycetes

Actinomycetes have been proven to be an excellent source of secondary metabolites for more than half a century. Exhibiting various bioactivities, they provide valuable approved drugs in clinical use. Most microorganisms are still untapped in terms of their capacity to produce secondary metabolites, since only a small fraction can be cultured in the laboratory. Thus, improving cultivation techniques to extend the range of secondary metabolite producers accessible under laboratory conditions is an important first step in prospecting underexplored sources for the isolation of novel antibiotics. Currently uncultured actinobacteria can be made available by bioprospecting extreme or simply habitats other than soil. Furthermore, bioinformatic analysis of genomes reveals most producers to harbour many more biosynthetic gene clusters than compounds identified from any single strain, which translates into a silent biosynthetic potential of the microbial world for the production of yet unknown natural products. This review covers discovery strategies and innovative methods recently employed to access the untapped reservoir of natural products. The focus is the order of actinomycetes although most approaches are similarly applicable to other microbes. Advanced cultivation methods, genomics- and metagenomics-based approaches, as well as modern metabolomics-inspired methods are highlighted to emphasise the interplay of different disciplines to improve access to novel natural products.

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.

Nonribosomal Peptides from Marine Microbes and Their Antimicrobial and Anticancer Potential

Marine environments are largely unexplored and can be a source of new molecules for the treatment of many diseases such as malaria, cancer, tuberculosis, HIV etc. The Marine environment is one of the untapped bioresource of getting pharmacologically active nonribosomal peptides (NRPs). Bioprospecting of marine microbes have achieved many remarkable milestones in pharmaceutics. Till date, more than 50% of drugs which are in clinical use belong to the nonribosomal peptide or mixed polyketide-nonribosomal peptide families of natural products isolated from marine bacteria, cyanobacteria and fungi. In recent years large numbers of nonribosomal have been discovered from marine microbes using multi-disciplinary approaches. The present review covers the NRPs discovered from marine microbes and their pharmacological potential along with role of genomics, proteomics and bioinformatics in discovery and development of nonribosomal peptides drugs.

SANDPUMA: ensemble predictions of nonribosomal peptide chemistry reveal biosynthetic diversity across Actinobacteria

SUMMARY

Nonribosomally synthesized peptides (NRPs) are natural products with widespread applications in medicine and biotechnology. Many algorithms have been developed to predict the substrate specificities of nonribosomal peptide synthetase adenylation (A) domains from DNA sequences, which enables prioritization and dereplication, and integration with other data types in discovery efforts. However, insufficient training data and a lack of clarity regarding prediction quality have impeded optimal use. Here, we introduce prediCAT, a new phylogenetics-inspired algorithm, which quantitatively estimates the degree of predictability of each A-domain. We then systematically benchmarked all algorithms on a newly gathered, independent test set of 434 A-domain sequences, showing that active-site-motif-based algorithms outperform whole-domain-based methods. Subsequently, we developed SANDPUMA, a powerful ensemble algorithm, based on newly trained versions of all high-performing algorithms, which significantly outperforms individual methods. Finally, we deployed SANDPUMA in a systematic investigation of 7635 Actinobacteria genomes, suggesting that NRP chemical diversity is much higher than previously estimated. SANDPUMA has been integrated into the widely used antiSMASH biosynthetic gene cluster analysis pipeline and is also available as an open-source, standalone tool.

AVAILABILITY AND IMPLEMENTATION

SANDPUMA is freely available at https://bitbucket.org/chevrm/sandpuma and as a docker image at https://hub.docker.com/r/chevrm/sandpuma/ under the GNU Public License 3 (GPL3).

CONTACT

chevrette@wisc.edu or marnix.medema@wur.nl.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A strategy for the identification of patterns in the biosynthesis of nonribosomal peptides by Betaproteobacteria species

Nonribosomal peptides have an important pharmacological role due to their extensive biological properties. The singularities in the biosynthesis of these natural products allowed the development of genome-mining strategies which associate them to their original biosynthetic gene clusters. Generally, these compounds present complex architectures that make their identification difficult. Based on these evidences, genomes from species of the class Betaproteobacteria were studied with the purpose of finding biosynthetic similarities among them. These organisms were applied as templates due to their large number of biosynthetic gene clusters and the natural products isolated from them. The strategy for Rapid Identification of Nonribosomal Peptides Portions (RINPEP) proposed in this work was built by reorganizing the data obtained from antiSMASH and NCBI with a product-centered way. The verification steps of RINPEP comprehended the fragments of existent compounds and predictions obtained in silico with the purpose of finding common subunits expressed by different genomic sequences. The results of this strategy revealed patterns in a global overview of the biosynthesis of nonribosomal peptides by Betaproteobacteria.

Development of fungal cell factories for the production of secondary metabolites: Linking genomics and metabolism

The genomic era has revolutionized research on secondary metabolites and bioinformatics methods have in recent years revived the antibiotic discovery process after decades with only few new active molecules being identified. New computational tools are driven by genomics and metabolomics analysis, and enables rapid identification of novel secondary metabolites. To translate this increased discovery rate into industrial exploitation, it is necessary to integrate secondary metabolite pathways in the metabolic engineering process. In this review, we will describe the novel advances in discovery of secondary metabolites produced by filamentous fungi, highlight the utilization of genome-scale metabolic models (GEMs) in the design of fungal cell factories for the production of secondary metabolites and review strategies for optimizing secondary metabolite production through the construction of high yielding platform cell factories.

Revisiting biodiscovery from microbial sources in the light of molecular advances

Since the discovery of penicillin microorganisms have been an unexhausted source of novel bioactive compounds that served as scaffolds for potential drug candidates as well for the development of new antibiotics via fermentative processes. However, after 30 glorious years of biodiscovery begun in the 1940s, discovery of new antibiotic or therapeutic compounds with medicinal value entered a decline phase from the late 1970s onwards. At the same time, significant increases in the numbers of antibiotic or multi-drug resistant bacteria resulting in serious infections were reported. Although natural product discovery research was encouraged to continue due to the need to treat these infections only a few discoveries of potent antibiotics were made in the years of decline such as the discovery of Nikkomycin and Spinosyn. However, at the dawn of the 21st century advances in molecular biology such as genome mining and metabolic engineering changed the scene providing new avenues to the field of drug discovery. This article will highlight some of these advances.

Batch-processing of imaging or liquid-chromatography mass spectrometry datasets and De Novo sequencing of polyketide siderophores

The open-source and cross-platform software CycloBranch was utilized for dereplication of organic compounds from mass spectrometry imaging imzML datasets and its functions were illustrated on microbial siderophores. The pixel-to-pixel batch-processing was analogous to liquid chromatography mass spectrometry data. Each data point represented here by accurate m/z values and the corresponding ion intensities was matched against integrated compound libraries. The fine isotopic structure matching was also embedded into CycloBranch dereplication process. The siderophores' characterization from single-pixel mass spectra was further supported by their de novo sequencing. New ketide building block library was utilized by CycloBranch to characterize the siderophores in images and mixtures and nomenclature of fragment ion series of linear and cyclic polyketide siderophores was proposed. The software is freely available at http://ms.biomed.cas.cz/cyclobranch. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Deciphering bioactive peptides and their action mechanisms through proteomics

INTRODUCTION

Bioactive peptides such as antimicrobial peptides (AMPs), ribosomally synthesized and post translationally modified peptides (RiPPs) and the non-ribosomal peptides (NRPs) have emerged with promising applications in medicine, agriculture and industry. However, their development has been limited by several difficulties making it necessary to search for novel discovery methods. In this context, proteomics has been considered a reliable tool. Areas covered: This review highlights recent developments in proteomic tools that facilitate the discovery of AMPs, RiPPs and NRPs as well as the elucidation of action mechanisms of AMPs and resistance mechanisms of pathogens to them. Expert commentary: Proteomic approaches have emerged as useful tools for the study of bioactive peptides, especially mass spectrometry-based peptidomics profiling, a promising strategy for AMP discovery. Furthermore, the rapidly expanding fields of genome mining and genome sequencing techniques, as well as mass spectrometry, have revolutionized the discovery of novel RiPPs and NRPs from complex biological samples.