RiPPMiner: a bioinformatics resource for deciphering chemical structures of RiPPs based on prediction of cleavage and cross-links

Synthetic Biology in Natural Product Biosynthesis

Synthetic biology has played an important role in the renaissance of natural products research during the post-genomics era. The development and integration of new tools have transformed the workflow of natural product discovery and engineering, generating multidisciplinary interest in the field. In this review, we summarize recent developments in natural product biosynthesis from three different aspects. First, advances in bioinformatics, experimental, and analytical tools to identify natural products associated with predicted biosynthetic gene clusters (BGCs) will be covered. This will be followed by an extensive review on the heterologous expression of natural products in bacterial, fungal and plant organisms. The native host-independent paradigm to natural product identification, pathway characterization, and enzyme discovery is where synthetic biology has played the most prominent role. Lastly, strategies to engineer biosynthetic pathways for structural diversification and complexity generation will be discussed, including recent advances in assembly-line megasynthase engineering, precursor-directed structural modification, and combinatorial biosynthesis.

Genome mining reveals the biosynthetic potential of a novel Lysinibacillus zambalensis sp. nov., isolated from a hyperalkaline spring

A novel bacterium, designated as strain M3T, was isolated from a hyperalkaline spring in the Philippines and identified as a new species within the genus Lysinibacillus through 16 S rRNA gene sequence and genomic analyses. Although strain M3T shared a high 16 S rRNA gene sequence similarity (> 98.7%) with many Lysinibacillus species, the digital DNA-DNA hybridization and orthologous average nucleotide identity values between strain M3T and its closet relative, Lysinibacillus xylanilyticus DSM 23,493T, were 41.2% and 90.6%, respectively-both below the established threshold for prokaryotic species delineation. Genome mining of the 5.3 Mbp-draft genome of strain M3T revealed eight biosynthetic gene clusters, which shared little sequence similarity with characterized clusters, suggesting the potential for encoding novel specialized metabolites. The cells of strain M3T were Gram-stain-positive, aerobic, rod-shaped, non-motile, and capable of endospore formation. Optimum growth was observed at 30 °C, pH 8.0, and 0.5% (w/v) NaCl. The major respiratory quinone was menaquinone-7, and the predominant polar lipids were diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, and two unknown phospholipids. Its fatty acid profile showed an elevated level of iso-C15:0, and the peptidoglycan type was determined to be A4α (L-Lys-D-Asp). This study contributes to the growing database and understanding of the genus and aims to help drive future research on the bioactive potential of the genus. Lysinibacillus zambalensis sp. nov. is proposed with strain M3T as the type strain (= TISTR 10640T = BIOTECH 10973T).

The Biarylitides: Understanding the Structure and Biosynthesis of a Fascinating Class of Cytochrome P450 Modified RiPP Natural Products

The biarylitides are a recently discovered class of RiPP natural products that are fascinating both from the small size of the core peptides as well as the diversity of peptide crosslinking exhibited by the cytochrome P450 enzymes found in these systems. In this review, we address the discovery and biosynthetic diversity of these systems and discuss the methods and challenges of analysing the structures of these constrained cyclic peptides. We also discuss the structures of the P450 enzymes involved in these pathways and address the potential for alternate catalytic outcomes and activities as seen most recently with the inclusion of biarylitide related enzymes within rufomycin biosynthesis.

BGC heteroexpression strategy for production of novel microbial secondary metabolites

Biosynthetic gene clusters (BGCs) in microbial genomes play a crucial role in the biosynthesis of diverse secondary metabolites (SMs) with pharmaceutical potential. However, most BGCs remain silent under conventional conditions, resulting in the frequently repeated discovery of known SMs. Fortunately, in the past two decades, the heterologous expression of BGCs in genetically tractable hosts has emerged as a powerful strategy to awaken microbial metabolic pathways for making novel microbial SMs. In this review, we comprehensively delineated the development and application of this strategy, highlighting various BGC cloning and assembly techniques and their technical characteristics. We also summarized 519 novel SMs from BGC hetero-expression-derived strains and described their occurrence, bioactivity, mode of action, and biosynthetic logic. Lastly, current challenges and future perspectives for developing more efficient BGC hetero-expression strategies were discussed in this review.

Molecular insights fast-tracked: AI in biosynthetic pathway research

Covering: 2000 to 2025This review explores the potential of artificial intelligence (AI) in addressing challenges and accelerating molecular insights in biosynthetic pathway research, which is crucial for developing bioactive natural products with applications in pharmacology, agriculture, and biotechnology. It provides an overview of various AI techniques relevant to this research field, including machine learning (ML), deep learning (DL), natural language processing, network analysis, and data mining. AI-powered applications across three main areas, namely, pathway discovery and mining, pathway design, and pathway optimization, are discussed, and the benefits and challenges of integrating omics data and AI for enhanced pathway research are also elucidated. This review also addresses the current limitations, future directions, and the importance of synergy between AI and experimental approaches in unlocking rapid advancements in biosynthetic pathway research. The review concludes with an evaluation of AI's current capabilities and future outlook, emphasizing the transformative impact of AI on biosynthetic pathway research and the potential for new opportunities in the discovery and optimization of bioactive natural products.

Computational advances in biosynthetic gene cluster discovery and prediction

Biosynthetic gene clusters (BGCs) are groups of clustered genes found in bacteria, fungi, and some plants and animals that are crucial for synthesizing secondary metabolites. In recent years, genome mining of BGCs has emerged as a prominent research focus, particularly in natural product discovery and drug development. Compared to traditional experimental methods, applying computational techniques has significantly enhanced the efficiency of BGC identification and annotation, thereby facilitating the discovery of novel metabolites. The advent of artificial intelligence, particularly machine learning models and more advanced deep learning algorithms, has significantly enhanced both the speed and precision of BGC mining. This review offers a comprehensive introduction to currently developed BGC databases and prediction tools, highlighting the potential of machine learning technologies in BGC mining. Additionally, it summarizes the challenges computational methods face in this area and discusses future research directions.

Integrating Metabolomics and Genomics to Uncover Antimicrobial Compounds in Lactiplantibacillus plantarum UTNGt2, a Cacao-Originating Probiotic from Ecuador

Background/Objectives: Lactic acid bacteria (LAB) produce several diverse metabolites during fermentation that play key roles in enhancing health and food quality. These metabolites include peptides, organic acids, exopolysaccharides, and antimicrobial compounds, which contribute to gut health, immune system modulation, and pathogen inhibition. This study analyzed the intracellular (Met-Int) and extracellular metabolites (Met-Ext-CFS; cell-free supernatant) of Lactiplantibacillus plantarum UTNGt2, a probiotic strain isolated from Theobroma grandiflorum. Methods: The assessment was performed using capillary LC-MS/MS metabolomics with a SWATH-based data-independent acquisition approach to identify molecules associated with antimicrobial activity. Results: The integration of metabolomic data with whole-genome annotation enabled the identification of several key metabolites, including amino acids, nucleotides, organic acids, oligopeptides, terpenes, and flavonoids, many of which were associated with the antimicrobial activity of UTNGt2. Pathway analysis reveals critical processes such as secondary metabolite biosynthesis, nucleotide and galactose metabolism, and cofactor biosynthesis. By integrating RiPP (ribosomally synthesized and post-translationally modified peptide) cluster gene predictions with LC-MS data, this study validates the production of specific RiPPs and uncovers novel bioactive compounds encoded within the UTNGt2 genome. The oligopeptide val-leu-pro-val-pro-gln found in both Met-Int (ESI+) and Met-Ext-CFS (ESI+) may contribute to the strain's antimicrobial strength. It could also enhance probiotic and fermentation-related functions. Conclusions: While genome-based predictions highlight the strain's biosynthetic potential, the actual metabolite profile is influenced by factors like transcriptional regulation, post-transcriptional and post-translational modifications, and environmental conditions. These findings emphasize the value of multi-omics approaches in providing a holistic understanding of metabolite production and its role in antimicrobial activity.

Approach advancements for engineering novel peptide analogs of existing lantibiotics: where are we today?

INTRODUCTION

The emergence of antibiotic resistance among the clinically important bacterial pathogens has increased healthcare costs and reduced patient safety and quality of life. Lantibiotics is a large class of ribosomally synthesized, and posttranslationally modified peptides have been the primary focus of numerous research aimed at discovering compounds for treating bacterial infections.

AREAS COVERED

The article explains the most up to date hierarchy of methods followed in the field for high throughput screening of lantibiotics/analogs with improved therapeutic properties. Herein, we explain how the structure and the biosynthesis of lantibiotics can be manipulated for the expansion of the horizon of lantibiotic potency. Furthermore, we discuss the lantibiotic analogs that have demonstrated the efficacy against bacterial pathogens of interest in animal models.

EXPERT OPINION

In this current age of rapidly advancing antimicrobial resistance, there is a dire need for the development of therapeutic agents that possess distinct mechanisms of action to existing modes of treatment. Recent advances in the understanding of many of the lantibiotic biosynthesis systems and the discovery of new analogs with superior properties to the native compound may have paved the way for the development of a much-needed novel potent class of antibiotic.

Bacterial Cytochrome P450 Catalyzed Macrocyclization of Ribosomal Peptides

Macrocyclization is a vital process in the biosynthesis of ribosomally synthesized and post-translationally modified peptides (RiPPs), significantly enhancing their structural diversity and biological activity. Universally found in living organisms, cytochrome P450 enzymes (P450s) are versatile catalysts that facilitate a wide array of chemical transformations and have recently been discovered to contribute to the expansion and complexity of the chemical spectrum of RiPPs. Particularly, P450-catalyzed biaryl-bridged RiPPs, characterized by highly modified structures, represent an intriguing but underexplored class of natural products, as demonstrated by the recent discovery of tryptorubin A, biarylitide and cittilin. These P450 enzymes demonstrate their versatility by facilitating peptide macrocyclization through the formation of carbon-carbon (C-C), carbon-nitrogen (C-N) and ether bonds between the side chains of tyrosine (Tyr), tryptophan (Trp) and histidine (His). This Review briefly highlights the latest progress in P450-catalyzed macrocyclization within RiPP biosynthesis, resulting in the generation of structurally complex RiPPs. These findings have expedited the discovery and detailed analysis of new P450s engaged in RiPP biosynthetic pathways.

Embleporicin: A Novel Class I Lanthipeptide from the Actinobacteria Embleya sp. NF3

Genome mining has emerged as a revolutionary tool for discovering new ribosomally synthesized and post-translationally modified peptides (RiPPs) in various genomes. Recently, these approaches have been used to detect and explore unique environments as sources of RiPP-producing microorganisms, particularly focusing on endophytic microorganisms found in medicinal plants. Some endophytic actinobacteria, especially strains of Streptomyces, are notable examples of peptide producers, as specific biosynthetic clusters encode them. To uncover the genetic potential of these organisms, we analyzed the genome of the endophytic actinobacterium Embleya sp. NF3 using genome mining and bioinformatics tools. Our analysis led to the identification of a putative class I lanthipeptide. We cloned the core biosynthetic genes of this putative lanthipeptide, named embleporicin, and expressed them in vitro using a cell-free protein system (CFPS). The resulting product demonstrated antimicrobial activity against Micrococcus luteus ATCC 9341. This represents the first RiPP reported in the genus Embleya and the first actinobacterial lanthipeptide produced through cell-free technology.

Heterologous biosynthesis of myxobacterial lanthipeptides melittapeptins

The myxobacteria are an attractive bioresource for bioactive compounds since the large size genome contains many biosynthetic gene clusters of secondary metabolites. The genome of the myxobacterium Melittangium boletus contains three biosynthetic gene clusters for lanthipeptide production. One of the gene clusters includes genes coding lanthipeptide precursor (melA), class II lanthipeptide synthetase (melM), and transporter (melT). The amino acid sequence of melA indicated similarity with that of known lanthipeptides mersacidin and lichenicidin A1 by the alignment. To perform heterologous production of new lanthipeptides, the expression vector containing the essential genes (melA and melM) was constructed by utilizing codon-optimized synthetic genes. The co-expression of two genes in the host bacterial cells of Escherichia coli BL21 (DE3) afforded new lanthipeptides named melittapeptins A-C. The structures of melittapeptins A-C including lanthionine/methyllanthionine bridge pattern were proposed based on protease digestion and MS/MS experiments. The native strain of M. boletus did not produce melittapeptins A-C, so heterologous production using the biosynthetic gene cluster was effective in obtaining the lanthipeptides. Melittapeptins A-C showed specific and potent antibacterial activity to the Gram-positive bacterium Micrococcus luteus. To the best of our knowledge, this is the first report of antibacterial lanthipeptides derived from myxobacterial origin. KEY POINTS: • New lanthipeptides melittapeptins were heterologously produced in Escherichia coli. • Melittapeptins showed specific antibacterial activity against Micrococcus luteus. • Melittapeptins were the first antibacterial lanthipeptides of myxobacterial origin.

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.

Chromosome architecture as a determinant for biosynthetic diversity in Micromonospora

Natural products - small molecules generated by organisms to facilitate ecological interactions - are of great importance to society and are used as antibacterial, antiviral, antifungal and anticancer drugs. However, the role and evolution of these molecules and the fitness benefits they provide to their hosts in their natural habitat remain an outstanding question. In bacteria, the genes that encode the biosynthetic proteins that generate these molecules are organised into discrete loci termed biosynthetic gene clusters (BGCs). In this work, we asked the following question: How are biosynthetic gene clusters organised at the chromosomal level? We sought to answer this using publicly available high-quality assemblies of Micromonospora, an actinomycete genus with members responsible for biosynthesizing notable natural products, such as gentamicin and calicheamicin. By orienting the Micromonospora chromosome around the origin of replication, we demonstrated that Micromonospora has a conserved origin-proximal region, which becomes progressively more disordered towards the antipodes of the origin. We then demonstrated through genome mining of these organisms that the conserved origin-proximal region and the origin-distal region of Micromonospora have distinct populations of BGCs and, in this regard, parallel the organization of Streptomyces, which possesses linear chromosomes. Specifically, the origin-proximal region contains highly syntenous, conserved BGCs predicted to biosynthesize terpenes and a type III polyketide synthase. In contrast, the ori-distal region contains a highly diverse population of BGCs, with many BGCs belonging to unique gene cluster families. These data highlight that genomic plasticity in Micromonospora is locus-specific, and highlight the importance of using high-quality genome assemblies for natural product discovery and guide future natural product discovery by highlighting that biosynthetic novelty may be enriched in specific chromosomal neighbourhoods.

GLYCOCINS: The sugar peppered antimicrobials

Glycosylated bacteriocins, known as glycocins, were first discovered in 2011. These bioactive peptides are produced by bacteria to gain survival advantages. They exhibit diverse types of glycans and demonstrate varied antimicrobial activity. Currently, there are 13 experimentally known glycocins, with over 250 identified in silico across different bacterial phyla. Notably, glycocins are recognized for their glycan-mediated antimicrobial activity, proving effective against drug-resistant and foodborne pathogens. Many glycocins contain rare S-linked glycans. Glycosyltransferases (GTs), responsible for transferring sugar to glycocins and involved in glycocin biosynthesis, often cluster together in the producer's genome. This clustering makes them valuable for custom glycoengineering with diverse substrate specificities. Heterologous expression of glycocins has paved the way for the establishment of microbial factories for glycopeptide and glycoconjugate production across various industries. In this review, we emphasize the primary roles of fully and partially characterized glycocins and their glycosylating enzymes. Additionally, we explore how specific glycan structures facilitate these functions in antibacterial activities. Furthermore, we discuss newer approaches and increasing efforts aimed at exploiting bacterial glycobiology for the development of food preservatives and as replacements or complements to traditional antibiotics, particularly in the face of antibiotic-resistant pathogenic bacteria.

Mining microbial and metabolic dark matter in extreme environments: a roadmap for harnessing the power of multi-omics data

Extreme environments such as hyperarid, hypersaline, hyperthermal environments, and the deep sea harbor diverse microbial communities, which are specially adapted to extreme conditions and are known as extremophiles. These extremophilic organisms have developed unique survival strategies, making them ideal models for studying microbial diversity, evolution, and adaptation to adversity. They also play critical roles in biogeochemical cycles. Additionally, extremophiles often produce novel bioactive compounds in response to corresponding challenging environments. Recent advances in technologies, including genomic sequencing and untargeted metabolomic analysis, have significantly enhanced our understanding of microbial diversity, ecology, evolution, and the genetic and physiological characteristics in extremophiles. The integration of advanced multi-omics technologies into culture-dependent research has notably improved the efficiency, providing valuable insights into the physiological functions and biosynthetic capacities of extremophiles. The vast untapped microbial resources in extreme environments present substantial opportunities for discovering novel natural products and advancing our knowledge of microbial ecology and evolution. This review highlights the current research status on extremophilic microbiomes, focusing on microbial diversity, ecological roles, isolation and cultivation strategies, and the exploration of their biosynthetic potential. Moreover, we emphasize the importance and potential of discovering more strain resources and metabolites, which would be boosted greatly by harnessing the power of multi-omics data.

A systematic comparison of natural product potential, with an emphasis on RiPPs, by mining of bacteria of three large ecosystems

The implementation of several global microbiome studies has yielded extensive insights into the biosynthetic potential of natural microbial communities. However, studies on the distribution of several classes of ribosomally synthesized and post-translationally modified peptides (RiPPs), non-ribosomal peptides (NRPs) and polyketides (PKs) in different large microbial ecosystems have been very limited. Here, we collected a large set of metagenome-assembled bacterial genomes from marine, freshwater and terrestrial ecosystems to investigate the biosynthetic potential of these bacteria. We demonstrate the utility of public dataset collections for revealing the different secondary metabolite biosynthetic potentials among these different living environments. We show that there is a higher occurrence of RiPPs in terrestrial systems, while in marine systems, we found relatively more terpene-, NRP-, and PK encoding gene clusters. Among the many new biosynthetic gene clusters (BGCs) identified, we analyzed various Nif-11-like and nitrile hydratase leader peptide (NHLP) containing gene clusters that would merit further study, including promising products, such as mersacidin-, LAP- and proteusin analogs. This research highlights the significance of public datasets in elucidating the biosynthetic potential of microbes in different living environments and underscores the wide bioengineering opportunities within the RiPP family.

Decoding the photoprotection strategies and manipulating cyanobacterial photoprotective metabolites, mycosporine-like amino acids, for next-generation sunscreens

The most recent evaluation of the impacts of UV-B radiation and depletion of stratospheric ozone points out the need for effective photoprotection strategies for both biological and nonbiological components. To mitigate the disruptive consequences of artificial sunscreens, photoprotective compounds synthesized from gram-negative, oxygenic, and photoautotrophic prokaryote, cyanobacteria have been studied. In a quest to counteract the harmful UV radiation, cyanobacterial species biosynthesize photoprotective metabolites named as mycosporine-like amino acids (MAAs). The investigation of MAAs as potential substitutes for commercial sunscreen compounds is motivated by their inherent characteristics, such as antioxidative properties, water solubility, low molecular weight, and high molar extinction coefficients. These attributes contribute to the stability of MAAs and make them promising candidates for natural alternatives in sunscreen formulations. They are effective at reducing direct damage caused by UV radiation and do not lead to the production of reactive oxygen radicals. In order to better understand the role, ecology, and its application at a commercial scale, tools like genome mining, heterologous expression, and synthetic biology have been explored in this review to develop next-generation sunscreens. Utilizing tactical concepts of bio-nanoconjugate formation for the development of an efficient MAA-nanoparticle conjugate structure would not only give the sunscreen complex stability but would also serve as a promising tool for the production of analogues. This review would provide insight on efforts to produce MAAs by diversifying the biosynthetic pathways, modulating the precursors and stress conditions, and comprehending the gene cluster arrangement for MAA biosynthesis and its application in developing effective sunscreen.

Structural features and substrate engagement in peptide-modifying radical SAM enzymes

In recent years, the biological significance of ribosomally synthesized, post-translationally modified peptides (RiPPs) and the intriguing chemistry catalyzed by their tailoring enzymes has garnered significant attention. A subgroup of bacterial radical S-adenosylmethionine (rSAM) enzymes can activate C-H bonds in peptides, which leads to the production of a diverse range of RiPPs. The remarkable ability of these enzymes to facilitate various chemical processes, to generate and harbor high-energy radical species, and to accommodate large substrates with a high degree of flexibility is truly intriguing. The wide substrate scope and diversity of the chemistry performed by rSAM enzymes raise one question: how does the protein environment facilitate these distinct chemical conversions while sharing a similar structural fold? In this review, we discuss recent advances in the field of RiPP-rSAM enzymes, with a particular emphasis on domain architectures and substrate engagements identified by biophysical and structural characterizations. We provide readers with a comparative analysis of six examples of RiPP-rSAM enzymes with experimentally characterized structures. Linking the structural elements and the nature of rSAM-catalyzed RiPP production will provide insight into the functional engineering of enzyme activity to harness their catalytic power in broader applications.

Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining

BACKGROUND

Microbial secondary metabolites play a crucial role in the intricate interactions within the natural environment. Among these metabolites, ribosomally synthesized and post-translationally modified peptides (RiPPs) are becoming a promising source of therapeutic agents due to their structural diversity and functional versatility. However, their biosynthetic capacity and ecological functions remain largely underexplored.

RESULTS

Here, we aim to explore the biosynthetic profile of RiPPs and their potential roles in the interactions between microbes and viruses in the ocean, which encompasses a vast diversity of unique biomes that are rich in interactions and remains chemically underexplored. We first developed TrRiPP to identify RiPPs from ocean metagenomes, a deep learning method that detects RiPP precursors in a hallmark gene-independent manner to overcome the limitations of classic methods in processing highly fragmented metagenomic data. Applying this method to metagenomes from the global ocean microbiome, we uncover a diverse array of previously uncharacterized putative RiPP families with great novelty and diversity. Through correlation analysis based on metatranscriptomic data, we observed a high prevalence of antiphage defense-related and phage-related protein families that were co-expressed with RiPP families. Based on this putative association between RiPPs and phage infection, we constructed an Ocean Virus Database (OVD) and established a RiPP-involving host-phage interaction network through host prediction and co-expression analysis, revealing complex connectivities linking RiPP-encoding prokaryotes, RiPP families, viral protein families, and phages. These findings highlight the potential of RiPP families involved in prokaryote-phage interactions and coevolution, providing insights into their ecological functions in the ocean microbiome.

CONCLUSIONS

This study provides a systematic investigation of the biosynthetic potential of RiPPs from the ocean microbiome at a global scale, shedding light on the essential insights into the ecological functions of RiPPs in prokaryote-phage interactions through the integration of deep learning approaches, metatranscriptomic data, and host-phage connectivity. This study serves as a valuable example of exploring the ecological functions of bacterial secondary metabolites, particularly their associations with unexplored microbial interactions. Video Abstract.

Genome Mining for New Enzyme Chemistry

A revolution in the field of biocatalysis has enabled scalable access to compounds of high societal values using enzymes. The construction of biocatalytic routes relies on the reservoir of available enzymatic transformations. A review of uncharacterized proteins predicted from genomic sequencing projects shows that a treasure trove of enzyme chemistry awaits to be uncovered. This Review highlights enzymatic transformations discovered through various genome mining methods and showcases their potential future applications in biocatalysis.

Embracing the era of antimicrobial peptides with marine organisms

Covering: 2018 to Jun of 2023The efficiency of traditional antibiotics has been undermined by the proliferation of antibiotic-resistant pathogenic microorganisms, necessitating the pursuit of innovative therapeutic agents. Antimicrobial peptides (AMPs), which are part of host defence peptides found ubiquitously in nature, exhibiting a wide range of activity towards bacteria, fungi, and viruses, offer a highly promising candidate solution. The efficacy of AMPs can frequently be augmented via alterations to their amino acid sequences or structural adjustments. Given the vast reservoir of marine life forms and their distinctive ecosystems, marine AMPs stand as a burgeoning focal point in the quest for alternative peptide templates extracted from natural sources. Advances in identification and characterization techniques have accelerated the discoveries of marine AMPs, thereby stimulating AMP customization, optimization, and synthesis research endeavours. This review presents an overview of recent discoveries related to the intriguing qualities of marine AMPs. Emphasis will be placed upon post-translational modifications (PTMs) of marine AMPs and how they may impact functionality and potency. Additionally, this review considers ways in which marine PTM might support larger-scale, heterologous AMP manufacturing initiatives, providing insights into translational applications of these important biomolecules.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Pseudomonassin, a New Bioactive Ribosomally Synthesised and Post-Translationally Modified Peptide from Pseudomonas sp. SST3

Genome mining and metabolomics have become valuable tools in natural products research to evaluate and identify potential new chemistry from bacteria. In the search for new compounds from the deep-sea organism, Pseudomonas sp. SST3, from the South Shetland Trough, Antarctica, a co-cultivation with a second deep-sea Pseudomonas zhaodongensis SST2, was undertaken to isolate pseudomonassin, a ribosomally synthesised and post-translationally modified peptide (RiPP) that belongs to a class of RiPP called lasso peptides. Pseudomonassin was identified using a genome-mining approach and isolated by means of mass spectrometric guided isolation. Extensive metabolomics analysis of the co-cultivation of Pseudomonas sp. SST3 and P. zhaodongensis SST2, Pseudomonas sp. SST3 and Escherichia coli, and P. zhaodongensis SST2 and E. coli were performed using principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA), which revealed potential new metabolites in the outlier regions of the co-cultivation, with other metabolites identified previously from other species of Pseudomonas. The sequence of pseudomonassin was completely deduced using high collision dissociation tandem mass spectrometry (HCD-MS/MS). Preliminary studies on its activity against the pathogenic P. aeruginosa and its biofilm formation have been assessed and produced a minimum inhibitory concentration (MIC) of 63 μg/mL and 28 μg/mL, respectively.

Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products

Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.

Bioactivity Screening and Genomic Analysis Reveals Deep-Sea Fish Microbiome Isolates as Sources of Novel Antimicrobials

With the increase in antimicrobial resistance and the subsequent demand for novel therapeutics, the deep-sea fish microbiome can be a relatively untapped source of antimicrobials, including bacteriocins. Previously, bacterial isolates were recovered from the gut of deep-sea fish sampled from the Atlantic Ocean.In this study, we used in vitro methods to screen a subset of these isolates for antimicrobial activity, and subsequently mined genomic DNA from isolates of interest for bacteriocin and other antimicrobial metabolite genes. We observed antimicrobial activity against foodborne pathogens, including Staphylococcus aureus, Listeria monocytogenes, Enterococcus faecalis and Micrococcus luteus. In total, 147 candidate biosynthetic gene clusters were identified in the genomic sequences, including 35 bacteriocin/RiPP-like clusters. Other bioactive metabolite genes detected included non-ribosomal peptide synthases (NRPS), polyketide synthases (PKS; Types 1 and 3), beta-lactones and terpenes. Moreover, four unique bacteriocin gene clusters were annotated and shown to encode novel peptides: a class IIc bacteriocin, two class IId bacteriocins and a class I lanthipeptide (LanM subgroup). Our dual in vitro and in silico approach allowed for a more comprehensive understanding of the bacteriocinogenic potential of these deep-sea isolates and an insight into the antimicrobial molecules that they may produce.

CRISPR-based knockout and base editing confirm the role of MYRF in heart development and congenital heart disease

High-throughput DNA sequencing studies increasingly associate DNA variants with congenital heart disease (CHD). However, functional modeling is a crucial prerequisite for translating genomic data into clinical care. We used CRISPR-Cas9-mediated targeting of 12 candidate genes in the vertebrate model medaka (Oryzias latipes), five of which displayed a novel cardiovascular phenotype spectrum in F0 (crispants): mapre2, smg7, cdc42bpab, ankrd11 and myrf, encoding a transcription factor recently linked to cardiac-urogenital syndrome. Our myrf mutant line showed particularly prominent embryonic cardiac defects recapitulating phenotypes of pediatric patients, including hypoplastic ventricle. Mimicking human mutations, we edited three sites to generate specific myrf single-nucleotide variants via cytosine and adenine base editors. The Glu749Lys missense mutation in the conserved intramolecular chaperon autocleavage domain fully recapitulated the characteristic myrf mutant phenotype with high penetrance, underlining the crucial function of this protein domain. The efficiency and scalability of base editing to model specific point mutations accelerate gene validation studies and the generation of human-relevant disease models.

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

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

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.

Biotechnologically potential genes in a polysaccharide-degrading epibiont of the Indonesian brown algae Hydroclathrus sp

BACKGROUND

Marine bacteria have recently attracted increasing attention to be harnessed for the production of valuable enzymes, vitamins, and bioactive compounds. Bacteria associated with the surfaces of marine macroalgae, called epibionts, are particularly interesting from ecological and biotechnological points of view, as they often exhibit antimicrobial activities to compete with pathogenic bacteria for nutrients and spaces. In search for biotechnologically potential genes from marine bacteria, we sequenced and analysed the genome of the epibiont HI03-3b, a polysaccharide-degrading bacterium associated with the surface of the Indonesian brown algae Hydroclathrus sp.

RESULTS

The algal epibiont HI03-3b has a genome of approximately 4,860,704 bp in size with 42.02 mol% G + C content, consisting of 5655 open reading frames (ORFs), 4409 genes coding for proteins (CDSs), 94 genes for tRNAs, and 32 genes for rRNAs. The genome sequence of HI03-3b was most closely related to that of Cytobacillus firmus NCTC10335 with the average amino acid identity (AAI) of 95.0 %, average nucleotide identity (ANI) of 94.1 %, and a recommended DNA-DNA hybridization (DDH) of 57.60 %. These scores are lower than the most frequently used standard for species demarcation (95% ANI cutoff) and the new species threshold (DDH > 70.0% for the same bacterial species). Some differences in genome features and gene composition were observed between HI03-3b and NCTC10335, such as genes encoding carbohydrate active enzymes. These suggest that HI03-3b is unique and likely a novel species within Cytobacillus genus, and we therefore proposed its name as Cytobacillus wakatobiense HI03-3b. Genome sequence analyses indicated the presence of genes involved not only in polysaccharide and protein degradation but also in vitamin and secondary metabolite biosynthesis. Some of them encode enzymes and compounds with biotechnological interest, such as protease, chitinase, subtilisin, pullulanase, and bacillolysin, which are often associated with antimicrobial or antibiofilm activities. This antimicrobial potential is supported by our finding that the extracellular protein fraction of this epibiont inhibited the growth of the bacterial pathogen Staphylococcus aureus.

CONCLUSION

The epibiont Cytobacillus HI03-3b harbours genes for polysaccharide and protein degradation as well as for natural product biosynthesis, suggesting its potential ecological roles in outcompeting other bacteria during biofilm formation as well as in protecting its algal host from predation. Due to the presence of genes for vitamin biosynthesis, it might also provide the algal host with vitamins for growth and development. Some of these metabolic genes are biotechnologically important, as they could become a platform for bioengineering to generate various seaweed-derived substances sustainably, such as antibiofilm agents and vitamins, which are beneficial for human health.

Genomic Analysis of the Rare Slightly Halophilic Myxobacterium "Paraliomyxa miuraensis" SMH-27-4, the Producer of the Antibiotic Miuraenamide A

Halophilic/halotolerant myxobacteria are extremely rare bacteria but an important source of novel bioactive secondary metabolites as drug leads. A slightly halophilic myxobacterium, "Paraliomyxa miuraensis" SMH-27-4, the producer of the antifungal antibiotic miuraenamide A, was considered to represent a novel genus. This study aimed to use the whole-genome sequence of this difficult-to-culture bacterium to provide genomic evidence supporting its taxonomy and to explore its potential as a novel secondary metabolite producer and its predicted gene functions. The draft genome was sequenced and de novo assembled into 164 contigs (11.8 Mbp). The 16S rRNA gene sequence-based and genome sequence-based phylogenetic analyses supported that this strain represents a novel genus of the family Nannocystaceae. Seventeen biosynthetic gene clusters (BGCs) were identified, and only five of them show some degree of similarity with the previously annotated BGCs, suggesting the great potential of producing novel secondary metabolites. The comparative genomic analysis within the family Nannocystaceae revealed the distribution of its members' gene functions. This study unveiled the novel genomic features and potential of the secondary metabolite production of this myxobacterium.

Advances in mining and expressing microbial biosynthetic gene clusters

Natural products (NPs) especially the secondary metabolites originated from microbes exhibit great importance in biomedical, industrial and agricultural applications. However, mining biosynthetic gene clusters (BGCs) to produce novel NPs has been hindered owing that a large population of environmental microbes are unculturable. In the past decade, strategies to explore BGCs directly from (meta)genomes have been established along with the fast development of high-throughput sequencing technologies and the powerful bioinformatics data-processing tools, which greatly expedited the exploitations of novel BGCs from unculturable microbes including the extremophilic microbes. In this review, we firstly summarized the popular bioinformatics tools and databases available to mine novel BGCs from (meta)genomes based on either pure cultures or pristine environmental samples. Noticeably, approaches rooted from machine learning and deep learning with focuses on the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs) were dramatically increased in recent years. Moreover, synthetic biology techniques to express the novel BGCs in culturable native microbes or heterologous hosts were introduced. This working pipeline including the discovery and biosynthesis of novel NPs will greatly advance the exploitations of the abundant but unexplored microbial BGCs.

BADASS: BActeriocin-Diversity ASsessment Software

BACKGROUND

Bacteriocins are defined as thermolabile peptides produced by bacteria with biological activity against taxonomically related species. These antimicrobial peptides have a wide application including disease treatment, food conservation, and probiotics. However, even with a large industrial and biotechnological application potential, these peptides are still poorly studied and explored. BADASS is software with a user-friendly graphical interface applied to the search and analysis of bacteriocin diversity in whole-metagenome shotgun sequencing data.

RESULTS

The search for bacteriocin sequences is performed with tools such as BLAST or DIAMOND using the BAGEL4 database as a reference. The putative bacteriocin sequences identified are used to determine the abundance and richness of the three classes of bacteriocins. Abundance is calculated by comparing the reads identified as bacteriocins to the reads identified as 16S rRNA gene using SILVA database as a reference. BADASS has a complete pipeline that starts with the quality assessment of the raw data. At the end of the analysis, BADASS generates several plots of richness and abundance automatically as well as tabular files containing information about the main bacteriocins detected. The user is able to change the main parameters of the analysis in the graphical interface. To demonstrate how the software works, we used four datasets from WMS studies using default parameters. Lantibiotics were the most abundant bacteriocins in the four datasets. This class of bacteriocin is commonly produced by Streptomyces sp.

CONCLUSIONS

With a user-friendly graphical interface and a complete pipeline, BADASS proved to be a powerful tool for prospecting bacteriocin sequences in Whole-Metagenome Shotgun Sequencing (WMS) data. This tool is publicly available at https://sourceforge.net/projects/badass/ .

The structure of MadC from Clostridium maddingley reveals new insights into class I lanthipeptide cyclases

The rapid emergence of microbial multi-resistance against antibiotics has led to intense search for alternatives. One of these alternatives are ribosomally synthesized and post-translationally modified peptides (RiPPs), especially lantibiotics. They are active in a low nanomolar range and their high stability is due to the presence of characteristic (methyl-) lanthionine rings, which makes them promising candidates as bacteriocides. However, innate resistance against lantibiotics exists in nature, emphasizing the need for artificial or tailor-made lantibiotics. Obviously, such an approach requires an in-depth mechanistic understanding of the modification enzymes, which catalyze the formation of (methyl-)lanthionine rings. Here, we determined the structure of a class I cyclase (MadC), involved in the modification of maddinglicin (MadA) via X-ray crystallography at a resolution of 1.7 Å, revealing new insights about the structural composition of the catalytical site. These structural features and substrate binding were analyzed by mutational analyses of the leader peptide as well as of the cyclase, shedding light into the mode of action of MadC.

Mammalian Commensal Streptococci Utilize a Rare Family of Class VI Lanthipeptide Synthetases to Synthesize Miniature Lanthipeptide-type Ribosomal Peptide Natural Products

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are natural products with remarkable chemical and functional diversities. These peptides are often synthesized as signals or antibiotics and frequently associated with quorum sensing (QS) systems. With the increasing number of available genomes, many hitherto unseen RiPP biosynthetic pathways have been mined, providing new resources for novel bioactive compounds. Herein, we investigated the underexplored biosynthetic potential of Streptococci, prevalent bacteria in mammal-microbiomes that include pathogenic, mutualistic, and commensal members. Using the transcription factor-centric genome mining strategy, we discovered a new family of lanthipeptide biosynthetic loci under the control of potential QS. By in vitro studies, we investigated the reaction of one of these lanthipeptide synthetases and found that it installs only one lanthionine moiety onto its short precursor peptide by connecting a conserved TxxC region. Bioinformatics and in vitro studies revealed that these lanthipeptide synthetases (class VI) are novel lanthipeptide synthetases with a truncated lyase, a kinase, and a truncated cyclase domain. Our data provide important insights into the processing and evolution of lanthipeptide synthetase to tailor smaller substrates. The data are important for obtaining a mechanistic understanding of the post-translational biosynthesis machinery of the growing variety of lanthipeptides.

Cloacaenodin, an Antimicrobial Lasso Peptide with Activity against Enterobacter

Using genome mining and heterologous expression, we report the discovery and production of a new antimicrobial lasso peptide from species related to the Enterobacter cloacae complex. Using NMR and mass spectrometric analysis, we show that this lasso peptide, named cloacaenodin, employs a threaded lasso fold which imparts proteolytic resistance that its unthreaded counterpart lacks. Cloacaenodin has selective, low micromolar, antimicrobial activity against species related to the E. cloacae complex, including species implicated in nosocomial infections and against clinical isolates of carbapenem-resistant Enterobacterales. We further used site-directed mutagenesis to probe the importance of specific residues to the peptide's biosynthesis, stability, and bioactivity.

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.

How to Completely Squeeze a Fungus-Advanced Genome Mining Tools for Novel Bioactive Substances

Fungal species have the capability of producing an overwhelming diversity of bioactive substances that can have beneficial but also detrimental effects on human health. These so-called secondary metabolites naturally serve as antimicrobial "weapon systems", signaling molecules or developmental effectors for fungi and hence are produced only under very specific environmental conditions or stages in their life cycle. However, as these complex conditions are difficult or even impossible to mimic in laboratory settings, only a small fraction of the true chemical diversity of fungi is known so far. This also implies that a large space for potentially new pharmaceuticals remains unexplored. We here present an overview on current developments in advanced methods that can be used to explore this chemical space. We focus on genetic and genomic methods, how to detect genes that harbor the blueprints for the production of these compounds (i.e., biosynthetic gene clusters, BGCs), and ways to activate these silent chromosomal regions. We provide an in-depth view of the chromatin-level regulation of BGCs and of the potential to use the CRISPR/Cas technology as an activation tool.

Biological Dark Matter Exploration using Data Mining for the Discovery of Antimicrobial Natural Products

The discovery of novel antimicrobials has significantly slowed down over the last three decades. At the same time, humans rely increasingly on antimicrobials because of the progressive antimicrobial resistance in medical practices, human communities, and the environment. Data mining is currently considered a promising option in the discovery of new antibiotics. Some of the advantages of data mining are the ability to predict chemical structures from sequence data, anticipation of the presence of novel metabolites, the understanding of gene evolution, and the corroboration of data from multiple omics technologies. This review analyzes the state-of-the-art for data mining in the fields of bacteria, fungi, and plant genomic data, as well as metabologenomics. It also summarizes some of the most recent research accomplishments in the field, all pinpointing to innovation through uncovering and implementing the next generation of antimicrobials.

Genome Mining as an Alternative Way for Screening the Marine Organisms for Their Potential to Produce UV-Absorbing Mycosporine-like Amino Acid

Mycosporine-like amino acids (MAAs) are small molecules with robust ultraviolet (UV)-absorbing capacities and a huge potential to be used as an environmentally friendly natural sunscreen. MAAs, temperature, and light-stable compounds demonstrate powerful photoprotective capacities and the ability to capture light in the UV-A and UV-B ranges without the production of damaging free radicals. The biotechnological uses of these secondary metabolites have been often limited by the small quantities restored from natural resources, variation in MAA expression profiles, and limited success in heterologous expression systems. Overcoming these obstacles requires a better understanding of MAA biosynthesis and its regulatory processes. MAAs are produced to a certain extent via a four-enzyme pathway, including genes encoding enzymes dehydroquinate synthase, enzyme O-methyltransferase, adenosine triphosphate grasp, and a nonribosomal peptide synthetase. However, there are substantial genetic discrepancies in the MAA genetic pathway in different species, suggesting further complexity of this pathway that is yet to be fully explored. In recent years, the application of genome-mining approaches allowed the identification of biosynthetic gene clusters (BGCs) that resulted in the discovery of many new compounds from unconventional sources. This review explores the use of novel genomics tools for linking BGCs and secondary metabolites based on the available omics data, including MAAs, and evaluates the potential of using novel genome-mining tools to reveal a cryptic potential for new bioproduct screening approaches and unrevealing new MAA producers.

Strategies to access biosynthetic novelty in bacterial genomes for drug discovery

Bacteria provide a rich source of natural products with potential therapeutic applications, such as novel antibiotic classes or anticancer drugs. Bioactivity-guided screening of bacterial extracts and characterization of biosynthetic pathways for drug discovery is now complemented by the availability of large (meta)genomic collections, placing researchers into the postgenomic, big-data era. The progress in next-generation sequencing and the rise of powerful computational tools provide unprecedented insights into unexplored taxa, ecological niches and 'biosynthetic dark matter', revealing diverse and chemically distinct natural products in previously unstudied bacteria. In this Review, we discuss such sources of new chemical entities and the implications for drug discovery with a particular focus on the strategies that have emerged in recent years to identify and access novelty.

Complex peptide natural products: Biosynthetic principles, challenges and opportunities for pathway engineering

Complex peptide natural products exhibit diverse biological functions and a wide range of physico-chemical properties. As a result, many peptides have entered the clinics for various applications. Two main routes for the biosynthesis of complex peptides have evolved in nature: ribosomally synthesized and post-translationally modified peptide (RiPP) biosynthetic pathways and non-ribosomal peptide synthetases (NRPSs). Insights into both bioorthogonal peptide biosynthetic strategies led to the establishment of universal principles for each of the two routes. These universal rules can be leveraged for the targeted identification of novel peptide biosynthetic blueprints in genome sequences and used for the rational engineering of biosynthetic pathways to produce non-natural peptides. In this review, we contrast the key principles of both biosynthetic routes and compare the different biochemical strategies to install the most frequently encountered peptide modifications. In addition, the influence of the fundamentally different biosynthetic principles on past, current and future engineering approaches is illustrated. Despite the different biosynthetic principles of both peptide biosynthetic routes, the arsenal of characterized peptide modifications encountered in RiPP and NRPS systems is largely overlapping. The continuous expansion of the biocatalytic toolbox of peptide modifying enzymes for both routes paves the way towards the production of complex tailor-made peptides and opens up the possibility to produce NRPS-derived peptides using the ribosomal route and vice versa.

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.

Cyanobacterial Genome Sequencing, Annotation, and Bioinformatics

Cyanobacteria are collectively a globally important monophyletic phylum of bacteria. They have attracted a lot of attention, not only because they are rich sources of natural bioactive products, including toxic substances, but also because they play an important role in global nitrogen and carbon cycles, and are capable of maintaining versatile environmental niche adaptations. A vast number of cyanobacterial genomes have become available due to fast development of sequencing technologies, but effort is still needed to comprehensively understand the molecular basis of their diversity. Here, we introduce a basic pipeline for the cyanobacterial genome sequencing project that can be employed to complete the whole cyanobacterial genome. The pipeline includes DNA extraction from the cyanobacterial culture of interest, hybrid genome sequencing, and genome assembly and annotation. At the end of the chapter, we briefly introduce genome mining tools and one successful genome mining example from our laboratory. This chapter provides general guidance regarding the sequencing project and thus includes several references for alternative methods and tools so that the reader can easily modify the pipeline according to the needs of the laboratory.

Genome-Guided Discovery of the First Myxobacterial Biarylitide Myxarylin Reveals Distinct C-N Biaryl Crosslinking in RiPP Biosynthesis

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a structurally diverse group of natural products. They feature a wide range of intriguing post-translational modifications, as exemplified by the biarylitides. These are a family of cyclic tripeptides found in Planomonospora, carrying a biaryl linkage between two aromatic amino acids. Recent genomic analyses revealed that the minimal biosynthetic prerequisite of biarylitide biosynthesis consists of only one ribosomally synthesized pentapeptide precursor as the substrate and a modifying cytochrome-P450-dependent enzyme. In silico analyses revealed that minimal biarylitide RiPP clusters are widespread among natural product producers across phylogenetic borders, including myxobacteria. We report here the genome-guided discovery of the first myxobacterial biarylitide MeYLH, termed Myxarylin, from Pyxidicoccus fallax An d48. Myxarylin was found to be an N-methylated tripeptide that surprisingly exhibits a C–N biaryl crosslink. In contrast to Myxarylin, previously isolated biarylitides are N-acetylated tripeptides that feature a C–C biaryl crosslink. Furthermore, the formation of Myxarylin was confirmed by the heterologous expression of the identified biosynthetic genes in Myxococcus xanthus DK1622. These findings expand the structural and biosynthetic scope of biarylitide-type RiPPs and emphasize the distinct biochemistry found in the myxobacterial realm.

Metagenomic sequencing-driven multidisciplinary approaches to shed light on the untapped microbial natural products

The advantage of metagenomics over the culture-based natural product (NP) discovery pipeline is the ability to access the biosynthetic potential of uncultivable microbes. Advances in DNA sequencing are revolutionizing conventional metagenomics approaches for microbial NP discovery. The genomes of (in)cultivable bugs can be resolved straightforwardly from environmental samples, enabling in situ prediction of biosynthetic gene clusters (BGCs). The predicted chemical diversities could be realized not only by heterologous expression of gene clusters originating from DNA synthesis or direct cloning, but also potentially by bioinformatic-directed organic synthesis or chemoenzymatic total synthesis. In this review, we suggest that metagenomic sequencing in tandem with multidisciplinary approaches will form a versatile platform to shed light on a plethora of microbial 'dark matter'.

Substrate Sequence Controls Regioselectivity of Lanthionine Formation by ProcM

Lanthipeptides belong to the family of ribosomally synthesized and post-translationally modified peptides (RiPPs). The (methyl)lanthionine cross-links characteristic to lanthipeptides are essential for their stability and bioactivities. In most bacteria, lanthipeptides are maturated from single precursor peptides encoded in the corresponding biosynthetic gene clusters. However, cyanobacteria engage in combinatorial biosynthesis and encode as many as 80 substrate peptides with highly diverse sequences that are modified by a single lanthionine synthetase into lanthipeptides of different lengths and ring patterns. It is puzzling how a single enzyme could exert control over the cyclization processes of such a wide range of substrates. Here, we used a library of ProcA3.3 precursor peptide variants and show that it is not the enzyme ProcM but rather its substrate sequences that determine the regioselectivity of lanthionine formation. We also demonstrate the utility of trapped ion mobility spectrometry-tandem mass spectrometry (TIMS-MS/MS) as a fast and convenient method to efficiently separate lanthipeptide constitutional isomers, particularly in cases where the isomers cannot be resolved by conventional liquid chromatography. Our data allowed identification of factors that are important for the cyclization outcome, but also showed that there are no easily identifiable predictive rules for all sequences. Our findings provide a platform for future deep learning approaches to allow such prediction of ring patterns of products of combinatorial biosynthesis.

Complete Genome Sequence of Two Deep-Sea Streptomyces Isolates from Madeira Archipelago and Evaluation of Their Biosynthetic Potential

The deep-sea constitutes a true unexplored frontier and a potential source of innovative drug scaffolds. Here, we present the genome sequence of two novel marine actinobacterial strains, MA3_2.13 and S07_1.15, isolated from deep-sea samples (sediments and sponge) and collected at Madeira archipelago (NE Atlantic Ocean; Portugal). The de novo assembly of both genomes was achieved using a hybrid strategy that combines short-reads (Illumina) and long-reads (PacBio) sequencing data. Phylogenetic analyses showed that strain MA3_2.13 is a new species of the Streptomyces genus, whereas strain S07_1.15 is closely related to the type strain of Streptomyces xinghaiensis. In silico analysis revealed that the total length of predicted biosynthetic gene clusters (BGCs) accounted for a high percentage of the MA3_2.13 genome, with several potential new metabolites identified. Strain S07_1.15 had, with a few exceptions, a predicted metabolic profile similar to S. xinghaiensis. In this work, we implemented a straightforward approach for generating high-quality genomes of new bacterial isolates and analyse in silico their potential to produce novel NPs. The inclusion of these in silico dereplication steps allows to minimize the rediscovery rates of traditional natural products screening methodologies and expedite the drug discovery process.

Antimicrobial Peptides: An Update on Classifications and Databases

Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an indispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimicrobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.