Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery

Discovery of Acyl-Surugamide A2 from Marine Streptomyces albidoflavus RKJM-0023-A New Cyclic Nonribosomal Peptide Containing an N-ε-acetyl-L-lysine Residue

We report the discovery of a novel cyclic nonribosomal peptide (NRP), acyl-surugamide A2, from a marine-derived Streptomyces albidoflavus RKJM-0023 (CP133227). The structure of acyl-surugamide A2 was elucidated using a combination of NMR spectroscopy, MS2 fragmentation analysis, and comparative analysis of the sur biosynthetic gene cluster. Acyl-surugamide A2 contains all eight core amino acids of surugamide A, with a modified N-ε-acetyl-L-lysine residue. Our study highlights the potential of marine Streptomyces strains to produce novel natural products with potential therapeutic applications. The structure of cyclic peptides can be solved using MS2 spectra and analysis of their biosynthetic gene clusters.

Evolution-inspired engineering of nonribosomal peptide synthetases

Many clinically used drugs are derived from or inspired by bacterial natural products that often are produced through nonribosomal peptide synthetases (NRPSs), megasynthetases that activate and join individual amino acids in an assembly line fashion. In this work, we describe a detailed phylogenetic analysis of several bacterial NRPSs that led to the identification of yet undescribed recombination sites within the thiolation (T) domain that can be used for NRPS engineering. We then developed an evolution-inspired "eXchange Unit between T domains" (XUT) approach, which allows the assembly of NRPS fragments over a broad range of GC contents, protein similarities, and extender unit specificities, as demonstrated for the specific production of a proteasome inhibitor designed and assembled from five different NRPS fragments.

Precision enzyme discovery through targeted mining of metagenomic data

Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.

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.

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.

Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health

There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.

Modern Trends in Natural Antibiotic Discovery

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

Characterization of Peptaibols Produced by a Marine Strain of the Fungus Trichoderma endophyticum via Mass Spectrometry, Genome Mining and Phylogeny-Based Prediction

Trichoderma is recognized as a prolific producer of nonribosomal peptides (NRPs) known as peptaibols, which have remarkable biological properties, such as antimicrobial and anticancer activities, as well as the ability to promote systemic resistance in plants against pathogens. In this study, the sequencing of 11-, 14- and 15-res peptaibols produced by a marine strain of Trichoderma isolated from the ascidian Botrylloides giganteus was performed via liquid chromatography coupled to high-resolution tandem mass spectrometry (LC-MS/MS). Identification, based on multilocus phylogeny, revealed that our isolate belongs to the species T. endophyticum, which has never been reported in marine environments. Through genome sequencing and genome mining, 53 biosynthetic gene clusters (BGCs) were identified as being related to bioactive natural products, including two NRP-synthetases: one responsible for the biosynthesis of 11- and 14-res peptaibols, and another for the biosynthesis of 15-res. Substrate prediction, based on phylogeny of the adenylation domains in combination with molecular networking, permitted extensive annotation of the mass spectra related to two new series of 15-res peptaibols, which are referred to herein as "endophytins". The analyses of synteny revealed that the origin of the 15-module peptaibol synthetase is related to 18, 19 and 20-module peptaibol synthetases, and suggests that the loss of modules may be a mechanism used by Trichoderma species for peptaibol diversification. This study demonstrates the importance of combining genome mining techniques, mass spectrometry analysis and molecular networks for the discovery of new natural products.

Microbial derived antimicrobial peptides as potential therapeutics in atopic dermatitis

Atopic dermatitis (AD) is a common chronic inflammatory skin disease that significantly affects the patient's quality of life. A disrupted skin barrier, type 2 cytokine-dominated inflammation, and microbial dysbiosis with increased Staphylococcus aureus colonization are critical components of AD pathogenesis. Patients with AD exhibit decreased expression of antimicrobial peptides (AMPs) which is linked to increased colonization by Staphylococcus aureus. The skin microbiome itself is a source of several AMPs. These host- and microbiome-derived AMPs define the microbial landscape of the skin based on their differential antimicrobial activity against a range of skin microbes or their quorum sensing inhibitory properties. These are particularly important in preventing and limiting dysbiotic colonization with Staphylococcus aureus. In addition, AMPs are critical for immune homeostasis. In this article, we share our perspectives about the implications of microbial derived AMPs in AD patients and their potential effects on overlapping factors involved in AD. We argue and discuss the potential of bacterial AMPs as therapeutics in AD.

Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics

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

Antibiotic discovery in the artificial intelligence era

As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models-namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high-quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.

UPLC-MS based integrated plasma proteomic and metabolomic profiling of TSC-RAML and its relationship with everolimus treatment

Aim: To profile the plasma proteomics and metabolomics of patients with renal cysts, sporadic angiomyolipoma (S-AML) and tuberous sclerosis complex related angiomyolipoma (TSC-RAML) before and after everolimus treatment, and to find potential diagnostic and prognostic biomarkers as well as reveal the underlying mechanism of TSC tumorigenesis. Materials and Methods: We retrospectively measured the plasma proteins and metabolites from November 2016 to November 2017 in a cohort of pre-treatment and post-treatment TSC-RAML patients and compared them with renal cyst and S-AML patients by ultra-performance liquid chromatography-mass spectrometer (UPLC-MS). The tumor reduction rates of TSC-RAML were assessed and correlated with the plasma protein and metabolite levels. In addition, functional analysis based on differentially expressed molecules was performed to reveal the underlying mechanisms. Results: Eighty-five patients with one hundred and ten plasma samples were enrolled in our study. Multiple proteins and metabolites, such as pre-melanosome protein (PMEL) and S-adenosylmethionine (SAM), demonstrated both diagnostic and prognostic effects. Functional analysis revealed many dysregulated pathways, including angiogenesis synthesis, smooth muscle proliferation and migration, amino acid metabolism and glycerophospholipid metabolism. Conclusion: The plasma proteomics and metabolomics pattern of TSC-RAML was clearly different from that of other renal tumors, and the differentially expressed plasma molecules could be used as prognostic and diagnostic biomarkers. The dysregulated pathways, such as angiogenesis and amino acid metabolism, may shed new light on the treatment of TSC-RAML.

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.

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.

Functions of Gut Microbiota Metabolites, Current Status and Future Perspectives

Gut microbiota, a collection of microorganisms that live within gastrointestinal tract, provides crucial signaling metabolites for the physiological of hosts. In healthy state, gut microbiota metabolites are helpful for maintaining the basic functions of hosts, whereas disturbed production of these metabolites can lead to numerous diseases such as metabolic diseases, cardiovascular diseases, gastrointestinal diseases, neurodegenerative diseases, and cancer. Although there are many reviews about the specific mechanisms of gut microbiota metabolites on specific diseases, there is no comprehensive summarization of the functions of these metabolites. In this Opinion, we discuss the knowledge of gut microbiota metabolites including the types of gut microbiota metabolites and their ways acting on targets. In addition, we summarize their physiological and pathologic functions in health and diseases, such as shaping the composition of gut microbiota and acting as nutrition. This paper can be helpful for understanding the roles of gut microbiota metabolites and thus provide guidance for developing suitable therapeutic strategies to combat microbial-driven diseases and improve health.

Genomic and Chemical Decryption of the Bacteroidetes Phylum for Its Potential to Biosynthesize Natural Products

With progress in genome sequencing and data sharing, 1,000s of bacterial genomes are publicly available. Genome mining-using bioinformatics tools in terms of biosynthetic gene cluster (BGC) identification, analysis, and rating-has become a key technology to explore the capabilities for natural product (NP) biosynthesis. Comprehensively, analyzing the genetic potential of the phylum Bacteroidetes revealed Chitinophaga as the most talented genus in terms of BGC abundance and diversity. Guided by the computational predictions, we conducted a metabolomics and bioactivity driven NP discovery program on 25 Chitinophaga strains. High numbers of strain-specific metabolite buckets confirmed the upfront predicted biosynthetic potential and revealed a tremendous uncharted chemical space. Mining this data set, we isolated the new iron chelating nonribosomally synthesized cyclic tetradeca- and pentadecalipodepsipeptide antibiotics chitinopeptins with activity against Candida, produced by C. eiseniae DSM 22224 and C. flava KCTC 62435, respectively. IMPORTANCE The development of pipelines for anti-infectives to be applied in plant, animal, and human health management are dried up. However, the resistance development against compounds in use calls for new lead structures. To fill this gap and to enhance the probability of success for the discovery of new bioactive natural products, microbial taxa currently underinvestigated must be mined. This study investigates the potential within the bacterial phylum Bacteroidetes. A combination of omics-technologies revealed taxonomical hot spots for specialized metabolites. Genome- and metabolome-based analyses showed that the phylum covers a new chemical space compared with classic natural product producers. Members of the Bacteroidetes may thus present a promising bioresource for future screening and isolation campaigns.

Exploration of Chemical Biology Approaches to Facilitate the Discovery and Development of Novel Antibiotics

Approximately 2.8 million people worldwide are infected with bacteria that are deemed resistant to clinically relevant antibiotics. This accounts for 700,000 deaths every year and represents a major public health threat that has been on the rise for the past two decades. In contrast, the pace of antibiotic discovery to treat these resistant pathogens has significantly decreased. Most antibiotics are complex natural products that were isolated from soil microorganisms during the golden era of antibiotic discovery (1940s to 1960s) employing the “Waksman platform”. After the collapse of this discovery platform, other strategies and approaches emerged, including phenotype- or target-based screenings of large synthetic compound libraries. However, these methods have not resulted in the discovery and/or development of new drugs for clinical use in over 30 years. A better understanding of the structure and function of the molecular components that constitute the bacterial system is of paramount importance to design new strategies to tackle drug-resistant pathogens. Herein, we review the traditional approaches as well as novel strategies to facilitate antibiotic discovery that are chemical biology-focused. These include the design and application of chemical probes that can undergo bioorthogonal reactions, such as copper (I)-catalyzed azide-alkyne cycloadditions (CuAAC). By specifically interacting with bacterial proteins or being incorporated in the microorganism’s metabolism, chemical probes are powerful tools in drug discovery that can help uncover new drug targets and investigate the mechanisms of action and resistance of new antibacterial leads.

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.

Natural product drug discovery in the artificial intelligence era

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

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.

Top-down synthetic biology approach for titer improvement of clinically important antibiotic daptomycin in Streptomyces roseosporus

Secondary metabolites are produced at low titers by native producers due to tight regulations of their productions in response to environmental conditions. Synthetic biology provides a rational engineering principle for transcriptional optimization of secondary metabolite BGCs (biosynthetic gene clusters). Here, we demonstrate the use of synthetic biology principles for the development of a high-titer strain of the clinically important antibiotic daptomycin. Due to the presence of large NRPS (non-ribosomal peptide synthetase) genes with multiple direct repeats, we employed a top-down approach that allows transcriptional optimization of genes in daptomycin BGC with the minimum inputs of synthetic DNAs. The repeat-free daptomycin BGC was created through partial codon-reprogramming of a NRPS gene and cloned into a shuttle BAC vector, allowing BGC refactoring in a host with a powerful recombination system. Then, transcriptions of functionally divided operons were sequentially optimized through three rounds of DBTL (design-build-test-learn) cycles that resulted in up to ~2300% improvement in total lipopeptide titers compared to the wild-type strain. Upon decanoic acid feeding, daptomycin accounted for ∼ 40% of total lipopeptide production. To the best of our knowledge, this is the highest improvement of daptomycin titer ever achieved through genetic engineering of S. roseosporus. The top-down engineering approach we describe here could be used as a general strategy for the development of high-titer industrial strains of secondary metabolites produced by BGCs containing genes of large multi-modular NRPS and PKS enzymes.

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.

Discovery and mining of enzymes from the human gut microbiome

Advances in technological and bioinformatics approaches have led to the generation of a plethora of human gut metagenomic datasets. Metabolomics has also provided substantial data regarding the small metabolites produced and modified by the microbiota. Comparatively, the microbial enzymes mediating the transformation of metabolites have not been intensively investigated. Here, we discuss the recent efforts and technologies used for discovering and mining enzymes from the human gut microbiota. The wealth of knowledge on metabolites, reactions, genome sequences, and structures of proteins, may drive the development of strategies for enzyme mining. Ongoing efforts to annotate gut microbiota enzymes will explain catalytic mechanisms that may guide the clinical applications of the gut microbiome for diagnostic and therapeutic purposes.