RiPPMiner-Genome: A Web Resource for Automated Prediction of Crosslinked Chemical Structures of RiPPs by Genome Mining

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.

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

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

Proteases Involved in Leader Peptide Removal during RiPP Biosynthesis

Ribosomally synthesized and post-translationally modified peptides (RiPPs) have received much attention in recent years because of their promising bioactivities and the portability of their biosynthetic pathways. Heterologous expression studies of RiPP biosynthetic enzymes identified by genome mining often leave a leader peptide on the final product to prevent toxicity to the host and to allow the attachment of a genetically encoded affinity purification tag. Removal of the leader peptide to produce the mature natural product is then carried out in vitro with either a commercial protease or a protease that fulfills this task in the producing organism. This review covers the advances in characterizing these latter cognate proteases from bacterial RiPPs and their utility as sequence-dependent proteases. The strategies employed for leader peptide removal have been shown to be remarkably diverse. They include one-step removal by a single protease, two-step removal by two dedicated proteases, and endoproteinase activity followed by aminopeptidase activity by the same protease. Similarly, the localization of the proteolytic step varies from cytoplasmic cleavage to leader peptide removal during secretion to extracellular leader peptide removal. Finally, substrate recognition ranges from highly sequence specific with respect to the leader and/or modified core peptide to nonsequence specific mechanisms.

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.

Biocontrol potential of Chitinophaga flava HK235 producing antifungal-related peptide chitinocin

Botrytis cinerea is a necrotrophic fungal pathogen with an extremely broad host range, causing significant economic losses in agricultural production. In this study, we discovered a culture filtrate of bacterial strain HK235, which was identified as Chitinophaga flava, exhibiting high levels of antifungal activity against B. cinerea. From the HK235 culture filtrate, we isolated a new antimicrobial peptide molecule designated as chitinocin based on activity-guided fractionation followed by characterization of the amino acid composition and spectroscopic analyses. The HK235 culture filtrate and chitinocin completely inhibited both conidial germination and mycelial growth of B. cinerea at a concentration of 20% and 200 μg/mL, respectively. In addition to antibiosis against B. cinerea, the active compound chitinocin had a broad antifungal and antibacterial activity in vitro. When tomato plants were treated with the culture filtrate and chitinocin, the treatment strongly reduced the development of gray mold disease in a concentration-dependent manner compared to the untreated control. Here, considering the potent antifungal property in vitro and in vivo, we present the biocontrol potential of C. flava HK235 for the first time.

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.

Demystifying Bacteriocins of Human Microbiota by Genome Guided Prospects: An Impetus to Rekindle the Antimicrobial Research

The human microbiome is a reservoir of potential bacteriocins that can counteract multidrug resistant bacterial pathogens. Unlike antibiotics, bacteriocins selectively inhibit a spectrum of competent bacteria and are said to safeguard gut commensals, reducing the chance of dysbiosis. Bacteriocinogenic probiotics or bacteriocins of human origin will be more pertinent in human physiological conditions for therapeutic applications to act against invading pathogens. Recent advancement in the omics approach enables the mining of diverse and novel bacteriocins by identifying biosynthetic gene clusters from the human microbial genome, pangenome or shotgun metagenome, which is a breakthrough in the discovery line of novel bacteriocins. This review summarizes the most recent trends and therapeutic potential of bacteriocins of human microbial origin, the advancement in the in silico algorithms and databases in the discovery of novel bacteriocin, and how to bridge the gap between the discovery of bacteriocin genes from big datasets and their in vitro production. Besides, the later part of the review discussed the various impediments in their clinical applications and possible solution to bring them into the frontline therapeutics to control infections, thereby meeting the challenges of global antimicrobial resistance.

Genome sequence of freshwater nontoxigenic Limnoraphis associated with microcystin-producing blooms

A sample from a 2019 cyanobacterial bloom in a freshwater reservoir in eastern Oregon, USA, was used to produce a metagenome from which the complete, circular 7.3 Mbp genome of Limnoraphis sp. WC205 was assembled. The Limnoraphis sp. WC205 genome contains gas vesicle genes, genes for N₂-fixation and genes for both phycocyanin- and phycoerythrin-containing phycobilisomes. Limnoraphis was present in Willow Creek Reservoir throughout the summer and fall, coexisting with various other cyanobacteria in blooms that were associated with microcystin. The absence of cyanotoxin genes from the Limnoraphis sp. WC205 genome showed this cyanobacterium to be non-toxigenic, although it is predicted to produce cyanobactins closely related to Microcystis aeruginosa microcyclamides. DNA sequence corresponding to the Microcystis mcyG gene identified Microcystis as the microcystin producer in this lake.

Exploring fungal RiPPs from the perspective of chemical ecology

Since the initial detection, in 2007, of fungal ribosomally synthesised and post-translationally modified peptides (RiPPs), this group of natural products has undergone rapid expansion, with four separate classes now recognised: amatoxins/phallotoxins, borosins, dikaritins, and epichloëcyclins. Largely due to their historically anthropocentric employment in medicine and agriculture, novel fungal proteins and peptides are seldom investigated in relation to the fungus itself. Therefore, although the benefits these compounds confer to humans are often realised, their evolutionary advantage to the fungus, the reason for their continued production, is often obscure or ignored. This review sets out to summarise current knowledge on how these small peptide-derived products influence their producing species and surrounding biotic environment.

Bioprospecting of Ribosomally Synthesized and Post-translationally Modified Peptides Through Genome Characterization of a Novel Probiotic Lactiplantibacillus plantarum UTNGt21A Strain: A Promising Natural Antimicrobials Factory

The present work describes the genome sequencing and characterization of a novel Lactiplantibacillus plantarum strain assigned UTNGt21A isolated from wild Solanum quitoense (L.) fruits. In silico analysis has led to identifying a wide range of biosynthetic gene clusters (BGCs) and metabolic compounds. The genome had a total of 3,558,611 bp with GC of 43.96%, harboring 3,449 protein-coding genes, among which 3,209 were assigned by the EggNOG database, and 240 hypothetical proteins have no match in the BLASTN database. It also contains 68 tRNAs, 1 23S rRNA, 1 16S rRNA, 6 5S rRNA, and 1 tmRNA. In addition, no acquired resistance genes nor virulence and pathogenic factors were predicted, indicating that UTNGt21A is a safe strain. Three areas of interest (AOI) consisting of multiple genes encoding for bacteriocins and ABC transporters were predicted with BAGEL4, while eight secondary metabolite regions were predicted with the antiSMASH web tool. GutSMASH analysis predicted one metabolic gene cluster (MGC) type pyruvate to acetate-formate, a primary metabolite region essential for anaerobe growth. Several lanthipeptides and non-ribosomal peptide synthetase (NRPS) clusters were detected in the UTNGt21A but not the reference genomes, suggesting that their genome diversity might be linked to its niche-specific lineage and adaptation to a specific environment. Moreover, the application of a targeted genome mining tool (RiPPMiner) uncovered a diverse arsenal of important antimicrobial molecules such as lanthipeptides. Furthermore, in vitro analysis indicated that the crude extract (CE) of UTNGt21A exerted a wide spectrum of inhibition against several pathogens. The results indicated that the possible peptide-protein extract (PC) from UTNGt21A induces morphological and ultrastructural changes of Salmonella enterica subsp. enterica ATCC51741, compatible with its inhibitory potential. Genome characterization is the basis for further in vitro and in vivo studies to explore their use as antimicrobial producers or probiotic strains.

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.

DiZyme: Open-Access Expandable Resource for Quantitative Prediction of Nanozyme Catalytic Activity

Enzymes suffer from high cost, complex purification, and low stability. Development of low-cost artificial enzymes of comparative or higher effectiveness is desired. Given its complexity, it is desired to presume their activities prior to experiments. While computational approaches demonstrate success in modeling nanozyme activities, they require assumptions about the system to be made. Machine learning (ML) is an alternative approach towards data-driven material property prediction achieving high performance even on multicomponent complex systems. Despite the growing demand for customized nanozymes, there is no open access nanozyme database. Here, a user-friendly expandable database of >300 existing inorganic nanozymes is developed by data collection from >100 articles. Data analysis is performed to reveal the features responsible for catalytic activities of nanozymes, and new descriptors are proposed for its ML-assisted prediction. A random forest regression (RFR) model for evaluation of nanozyme peroxidase activity is developed and optimized by correlation-based feature selection and hyperparameter tuning, achieving performance up to R²  = 0.796 for K_cat and R²  = 0.627 for K_m . Experiment-confirmed unknown nanozyme activity prediction is also demonstrated. Moreover, the DiZyme expandable, open-access resource containing the database, predictive algorithm, and visualization tool is developed to boost novel nanozyme discovery worldwide (https://dizyme.net).

Artificial intelligence-guided discovery of anticancer lead compounds from plants and associated microorganisms

Plants and associated microorganisms are essential sources of natural products against human cancer diseases, partly exemplified by plant-derived anticancer drugs such as Taxol (paclitaxel). Natural products provide diverse mechanisms of action and can be used directly or as prodrugs for further anticancer optimization. Despite the success, major bottlenecks can delay anticancer lead discovery and implementation. Recent advances in sequencing and omics-related technology have provided a mine of information for developing new therapeutics from natural products. Artificial intelligence (AI), including machine learning (ML), has offered powerful techniques for extensive data analysis and prediction-making in anticancer leads discovery. This review presents an overview of current AI-guided solutions to discover anticancer lead compounds, focusing on natural products from plants and associated microorganisms.