Expansion of RiPP biosynthetic space through integration of pan-genomics and machine learning uncovers a novel class of lanthipeptides

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.

Multidimensional insights into the biodiversity of Streptomyces in soils of China: a pilot study

Streptomyces, a diverse group of filamentous bacteria found predominantly in soil, play a crucial role in nutrient cycling and produce many valuable secondary metabolites for the pharmaceutical industry. In this pilot study, we collected 19 soil samples from 14 provinces in China to preliminarily investigate the biodiversity and genetic structure of Streptomyces in soils of China from different dimensions, using recently developed cost-efficient amplicon and whole-genome library preparation methods. Amplicon analysis showed that Actinobacteria were among the most abundant bacteria, with 0.3% of amplicon sequence variants (ASVs) belonging to Streptomyces. Meanwhile, we successfully isolated 136 Streptomyces natural strains and assembled their genomes, including 26 previously unreported species, underscoring the need for further exploration of soil Streptomyces in China. Population genetics analysis revealed that homologous recombination may primarily drive the extensive genetic diversity observed in Streptomyces, as well as a complex population structure. Complementing this, pan-genome analysis shed light on gene diversity within Streptomyces and led to the discovery of rare genes, further emphasizing the vast genetic diversity of this genus. Additionally, multiple metabolic gene clusters were found in these Streptomyces strains, as well as some potentially unique or uncommon ones were found. These findings not only highlight the biological and metabolic diversity of Streptomyces but also provide a technical framework for future studies on the global biodiversity and evolution of this genus.

IMPORTANCE

Streptomyces, a prominent group of Actinobacteria, holds significant importance in ecosystems and biotechnology due to their diverse array of metabolic products. However, research on the biodiversity of soil Streptomyces across extensive geographical scales in China has been limited, and their genetic diversity has rarely been evaluated using modern population genetics principles. This pilot study successfully addresses these gaps by conducting a preliminary exploration on the biodiversity of Streptomyces in Chinese soils from multiple perspectives, providing valuable insights for a deeper understanding of their biodiversity and a novel technical framework for future large-scale explorations of its diversity.

Advances in AI-based strategies and tools to facilitate natural product and drug development

Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly aided the discovery and development of natural products and drugs. AI facilitates to: connect genetic data to chemical structures or vice-versa, repurpose known natural products, predict metabolic pathways, and design and optimize metabolites biosynthesis. More recently, the emergence and improvement in neural networks such as deep learning and ensemble automated web based bioinformatics platforms have sped up the discovery process. Meanwhile, AI also improves the identification and structure elucidation of unknown compounds from raw data like mass spectrometry and nuclear magnetic resonance. This article reviews these AI-driven methods and tools, highlighting their practical applications and guide for efficient natural product discovery and drug development.

Large-scale biosynthetic analysis of human microbiomes reveals diverse protective ribosomal peptides

The human microbiome produces diverse metabolites that influence host health, yet the chemical landscape of ribosomally synthesized and post-translationally modified peptides (RiPPs)-a versatile class of bioactive compounds-remains underexplored. Here, we conduct a large-scale biosynthetic analysis of 306,481 microbial genomes from human-associated microbiomes, uncovering a broad array of yet-to-be-discovered RiPPs. These RiPPs are distributed across various body sites but show a specific enrichment in the gut and oral microbiome. Big data omics analysis reveals that numerous RiPP families are inversely related to various diseases, suggesting their potential protective effects on health. For a proof of principle study, we apply the synthetic-bioinformatic natural product (syn-BNP) approach to RiPPs and chemically synthesize nine autoinducing peptides (AIPs) for in vitro and ex vivo assay. Our findings reveal that five AIPs effectively inhibit the biofilm formation of disease-associated pathogens. Furthermore, when ex vivo testing gut microbiota from mice with inflammatory bowel disease, we observe that two AIPs can regulate the microbial community and reduce harmful species. These findings highlight the vast potential of human microbial RiPPs in regulating microbial communities and maintaining human health, emphasizing their potential for therapeutic development.

Bioactive Specialized Metabolites from Staphylococcus: Diversity, Biosynthesis, and Biotechnological Potential

Staphylococci are a heterogeneous group of bacteria capable of colonizing diverse ecological niches and adopting a wide variety of lifestyles. While several strains are known as notorious, multidrug-resistant human pathogens, others are harmless inhabitants of soil, water, and food products, or beneficial members of the skin microbiota. To survive and remain competitive under challenging environmental conditions, staphylococci have evolved the ability to assemble and secrete a diverse range of ribosomally synthesized and posttranslationally modified peptides, nonribosomal peptides, terpenes, siderophores, and other specialized metabolites with antibiotic, immunomodulating and metal chelating activities. In this review, an overview of the bioactive metabolite arsenal of staphylococci is provided with a focus on their biosynthetic pathway, mode of action, and industrial application potential. Also, unexplored natural product biosynthetic pathways in staphylococci, along with strategies to access this hidden potential, are highlighted.

Regulatory Genes as Beacons for Discovery and Prioritization of Biosynthetic Gene Clusters in Streptomyces

Actinobacteria are renowned for their ability to produce a wide range of bioactive molecules, including many anticancer compounds and antibiotics that are critical in the battle against antimicrobial resistance. Despite identification of a vast array of biosynthetic gene clusters (BGCs) through genome mining, much of this biosynthetic potential remains unexplored, partially due to the fact that many remain silent or cryptic under typical laboratory conditions. Regulatory networks can provide clues to the location of yet undiscovered gene cluster families or be leveraged to predict their expression. Here, we investigate the associations between regulatory genes and BGCs to uncover their predictive capabilities in discovering and prioritizing gene clusters for downstream wet-lab validation. By analyzing the protein domain architectures of 128,993 potential regulators derived from 440 complete Streptomyces genomes, we uncovered various associations between biosynthetic classes, biological activities of their products, and regulator families. Specifically, subsets of the Streptomyces Antibiotic Regulatory Protein (SARP) and LuxR families were strongly associated with biosynthetic pathways encoding the production of bioactive compounds. After closer genomic inspection of the small SARPs, we discovered 82 putative SARP-associated BGCs that escaped detection by state-of-the-art software. This shows that continued exploration of regulatory systems will not only deepen our understanding of Actinobacteria's biosynthetic capabilities but also facilitates discovery and prioritization of high-potential BGCs in future genome-mining applications.

Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives

Drug discovery, a multifaceted process from compound identification to regulatory approval, historically plagued by inefficiencies and time lags due to limited data utilization, now faces urgent demands for accelerated lead compound identification. Innovations in biological data and computational chemistry have spurred a shift from trial-and-error methods to holistic approaches to medicinal chemistry. Computational techniques, particularly artificial intelligence (AI), notably machine learning (ML) and deep learning (DL), have revolutionized drug development, enhancing data analysis and predictive modeling. Natural products (NPs) have long served as rich sources of biologically active compounds, with many successful drugs originating from them. Advances in information science expanded NP-related databases, enabling deeper exploration with AI. Integrating AI into NP drug discovery promises accelerated discoveries, leveraging AI's analytical prowess, including generative AI for data synthesis. This perspective illuminates AI's current landscape in NP drug discovery, addressing strengths, limitations, and future trajectories to advance this vital research domain.

De novo design of ribosomally synthesized and post-translationally modified peptides

In nature, peptides are enzymatically modified to constrain their structure and introduce functional moieties. De novo peptide structures could be built by combining enzymes from different pathways, but determining the rules of their use is difficult. We present a biophysical model to combine enzymes sourced from bacterial ribosomally synthesized and post-translationally modified peptide (RiPP) gene clusters. Using a pipeline to evaluate more than 1,000 peptides, the model was parameterized under uniform conditions in Escherichia coli for enzymes from different classes (graspetide, spliceotide, pantocin, cyanobactin, glycocin, lasso peptide and lanthipeptide). Synthetic leader peptides with recognition sequences for up to three enzymes were designed to modify core sequences sharing no identity to natural RiPPs. Empirically, RiPPs with the desired modifications constituted 7-67% of the total peptides produced, and 6 of our 8 peptide designs were successfully modified. This work is an example of the design of enzyme-modified peptides and libraries, using a framework that can be expanded to include new enzymes and chemical moieties.

New solutions for antibiotic discovery: Prioritizing microbial biosynthetic space using ecology and machine learning

With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. We discuss the challenges and state-of-the-art of antibiotic discovery based on ecological principles, genome mining and artificial intelligence.

The genus Nocardia as a source of new antimicrobials

The genus Nocardia comprises over 130 species of soil-dwelling actinomycetes, many of which are opportunistic pathogens. Beyond their pathogenicity, Nocardia exhibits significant biosynthetic potential, producing an array of diverse antimicrobial secondary metabolites. This review highlights notable examples of these compounds and explores modern approaches to unlocking their untapped biosynthetic potential. As a relatively underexplored genus, Nocardia represents a promising source for new antibiotics to combat the growing resistance crisis.

Kinetic Analysis of Cyclization by the Substrate-Tolerant Lanthipeptide Synthetase ProcM

Lanthipeptides are ribosomally synthesized and post-translationally modified peptides (RiPPs) characterized by the presence of thioether cross-links called lanthionine and methyllanthionine, formed by dehydration of Ser/Thr residues and Michael-type addition of Cys side chains onto the resulting dehydroamino acids. Class II lanthipeptide synthetases are bifunctional enzymes responsible for both steps, thus generating macrocyclic natural products. ProcM is part of a group of class II lanthipeptide synthetases that are known for their remarkable substrate tolerance, having large numbers of natural substrates with highly diverse peptide sequences. They install multiple (methyl)lanthionine rings with high accuracy, attributes that have been used to make large libraries of polycyclic peptides. Previous studies suggested that the final ring pattern of the lanthipeptide product may be determined by the substrate sequence rather than by ProcM. The current investigation on the ProcM-catalyzed modification of one of its 30 natural substrates (ProcA3.3) and its sequence variants utilizes kinetic assays to understand the factors that determine the ring pattern. The data show that changes in the substrate sequence result in changes to the reaction rates of ring formation that in some cases lead to a change in the order of the modifications and thereby bring about different ring patterns. These observations provide further support that the substrate sequence determines to a large degree the final ring pattern. The data also show that similar to a previous study on another substrate (ProcA2.8), the reaction rates of successive reactions slow down as the peptide is matured; rate constants observed for the reactions of these two substrates are similar, suggesting that they reflect the intrinsic activity of the enzyme with its 30 natural substrates. We also investigated whether rates of formation of single isolated rings can predict the final ring pattern of polycyclic products, an important question for the products of genome mining exercises, as well as library generation. Collectively, the findings in this study indicate that the rates of isolated modifications can be used for predicting the final ProcM-produced ring pattern, but they also revealed limitations. One unexpected observation was that even changing Ser to Thr and vice versa, a common means to convert lanthionine to methyllanthionine and vice versa, can result in a change in the ring pattern.

A Stable Dehydratase Complex Catalyzes the Formation of Dehydrated Amino Acids in a Class V Lanthipeptide

Lanthipeptides are ribosomally synthesized and post-translationally modified peptides that bear the characteristic lanthionine (Lan) or methyllanthionine (MeLan) thioether linkages. (Me)Lan moieties bestow lanthipeptides with robust stability and diverse antimicrobial, anticancer, and antiallodynic activities. Installation of (Me)Lan requires dehydration of serine and threonine residues to 2,3-dehydroalanine (Dha) and (Z)-2,3-dehydrobutyrine (Dhb), respectively. LxmK and LxmY enzymes comprise the biosynthetic machinery of a newly discovered class V lanthipeptide, lexapeptide, and are proposed to catalyze the dehydration of serine and threonine residues in the precursor peptide. We demonstrate that LxmK and LxmY form a stable dehydratase complex to dehydrate precursor peptides. In addition, we present crystal structures of the LxmKY heterodimer, revealing structural and mechanistic features that enable iterative phosphorylation and elimination by the LxmKY complex. These findings provide molecular insights into class V lanthionine synthetases and lay the foundation for their applications as enzymatic tools in the biosynthesis of exquisitely modified peptides.

Illuminating Substrate Preferences of Promiscuous F420H2-Dependent Dehydroamino Acid Reductases with 4-Track mRNA Display

Stereoselective reduction of dehydroamino acids is a common biosynthetic strategy to introduce d-amino acids into peptidic natural products. The reduction, often observed during the biosynthesis of lanthipeptides, is performed by dedicated dehydroamino acid reductases (dhAARs). Enzymes from the three known dhAAR families utilize nicotinamide, flavin, or F420H2 coenzymes as hydride donors, and little is known about the catalysis performed by the latter family proteins. Here, we perform a bioinformatics-guided identification and large-scale in vitro characterization of five F420H2-dependent dhAARs. We construct an mRNA display-based pipeline for ultrahigh throughput substrate specificity profiling of the enzymes. The pipeline relies on a 4-track selection strategy to deliver large quantities of clean data, which were leveraged to build accurate substrate fitness models. Our results identify a remarkably promiscuous enzyme, referred to as MaeJC, that is capable of installing d-Ala residues into arbitrary substrates with minimal recognition requirements. We integrate MaeJC into a thiopeptide biosynthetic pathway to produce d-amino acids-containing thiopeptides, demonstrating the utility of MaeJC for the programmable installation of d-amino acids in ribosomal peptides.

Discovery, Biosynthesis, and Characterization of Rodencin, a Two-Component Lanthipeptide, Harboring d-Amino Acids Introduced by the Unusual Dehydrogenase RodJA

Lanthipeptides, a group of ribosomally synthesized and post-translationally modified peptides (RiPPs), exhibit diverse structures and bioactivities. Their biosynthetic enzymes serve as valuable tools for peptide bioengineering. Here, we report a class II lanthipeptide biosynthetic gene cluster in a Bacillus strain, driving the biosynthesis of a two-component lanthipeptide, termed rodencin, featured by the presence of two different d-amino acids, i.e., d-Ala and d-Abu. Rodencin displays synergistic antimicrobial activity against food-borne pathogens such as Bacillus cereus, Staphylococcus aureus, and Listeria monocytogenes. The α-peptide of rodencin contains one d-Ala and the β-peptide features both d-Ala and d-Abu. These are installed by dehydratases RodM1 and RodM2 and dehydrogenase RodJA, the activities of which were successfully reconstituted using a dedicated E. coli expression system. To illustrate the unusual d-Abu incorporation potential of the enzymes, analogous to the d-amino acid-containing β peptide of lacticin 3147, was successfully produced with the rodencin heterologous expression system, by employing RodM2 and the dehydrogenase RodJA.

Microbial-derived peptides with anti-mycobacterial potential

Tuberculosis (TB), an airborne infectious disease caused by Mycobacterium tuberculosis, has become the leading cause of death. The subsequent emergence of multidrug-resistant, extensively drug-resistant and totally drug-resistant strains, brings an urgent need to discover novel anti-TB drugs. Among them, microbial-derived anti-mycobacterial peptides, including ribosomally synthesized and post-translationally modified peptides (RiPPs) and multimodular nonribosomal peptides (NRPs), now arise as promising candidates for TB treatment. This review presents 96 natural RiPP and NRP families from bacteria and fungi that have broad spectrum in vitro activities against non-resistant and drug-resistant mycobacteria. In addition, intracellular targets of 22 molecules are the subject of much attention. Meanwhile, chemical features of 38 families could be modified in order to improve properties. In final, structure-activity relationships suggest that the modifications of various groups, especially the peptide side chains, the amino acid moieties, the cyclic peptide skeletons, various special groups, stereochemistry and entire peptide chain length are important for increasing the potency.

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.

Substrate Specificity of a Methyltransferase Involved in the Biosynthesis of the Lantibiotic Cacaoidin

Modification of the N- and C-termini of peptides enhances their stability against degradation by exopeptidases. The biosynthetic pathways of many peptidic natural products feature enzymatic modification of their termini, and these enzymes may represent a valuable pool of biocatalysts. The lantibiotic cacaoidin carries an N,N-dimethylated N-terminal amine group. Its biosynthetic gene cluster encodes the putative methyltransferase Cao4. In this work, we present reconstitution of the activity of the enzyme, which we termed CaoSC following standardized lanthipeptide nomenclature, using a heterologously produced peptide as the model substrate. In vitro methylation of diverse lanthipeptides revealed the substrate requirements of CaoSC. The enzyme accepts peptides of varying lengths and C-terminal sequences but requires dehydroalanine or dehydrobutyrine at the second position. CaoSC-mediated dimethylation of natural lantibiotics resulted in modestly enhanced antimicrobial activity of the lantibiotic haloduracin compared to that of the native compound. Improved activity and/or metabolic stability as a result of methylation illustrates the potential future application of CaoSC in the bioengineering of therapeutic peptides.

Exploration, expansion and definition of the atropopeptide family of ribosomally synthesized and posttranslationally modified peptides

Ribosomally synthesized and posttranslationally modified peptides (RiPPs) constitute a diverse class of natural products. Atropopeptides are a recent addition to the class. Here we developed AtropoFinder, a genome mining algorithm to chart the biosynthetic landscape of the atropopeptides. AtropoFinder identified more than 650 atropopeptide biosynthetic gene clusters (BGCs). We pinpointed crucial motifs and residues in leader and core peptide sequences, prompting a refined definition of the atropopeptide RiPP family. Our study revealed that a substantial subset of atropopeptide BGCs harbors multiple tailoring genes, thus suggesting a broader structural diversity than previously anticipated. To verify AtropoFinder, we heterologously expressed four atropopeptide BGCs, which resulted in the identification of novel atropopeptides with varying peptide lengths, number and types of modifications. Atropopeptides serve as a proof-of-principle for the versatile genome mining approach developed in this study that can be repurposed for the identification of RiPP and other BGCs that currently evade detection.

Diversity and specificity of molecular functions in cyanobacterial symbionts

Cyanobacteria are globally occurring photosynthetic bacteria notable for their contribution to primary production and production of toxins which have detrimental ecosystem impacts. Furthermore, cyanobacteria can form mutualistic symbiotic relationships with a diverse set of eukaryotes, including land plants, aquatic plankton and fungi. Nevertheless, not all cyanobacteria are found in symbiotic associations suggesting symbiotic cyanobacteria have evolved specializations that facilitate host-interactions. Photosynthetic capabilities, nitrogen fixation, and the production of complex biochemicals are key functions provided by host-associated cyanobacterial symbionts. To explore if additional specializations are associated with such lifestyles in cyanobacteria, we have conducted comparative phylogenomics of molecular functions and of biosynthetic gene clusters (BGCs) in 984 cyanobacterial genomes. Cyanobacteria with host-associated and symbiotic lifestyles were concentrated in the family Nostocaceae, where eight monophyletic clades correspond to specific host taxa. In agreement with previous studies, symbionts are likely to provide fixed nitrogen to their eukaryotic partners, through multiple different nitrogen fixation pathways. Additionally, our analyses identified chitin metabolising pathways in cyanobacteria associated with specific host groups, while obligate symbionts had fewer BGCs. The conservation of molecular functions and BGCs between closely related symbiotic and free-living cyanobacteria suggests the potential for additional cyanobacteria to form symbiotic relationships than is currently known.

Non-modular fatty acid synthases yield distinct N-terminal acylation in ribosomal peptides

Recent efforts in genome mining of ribosomally synthesized and post-translationally modified peptides (RiPPs) have expanded the diversity of post-translational modification chemistries. However, RiPPs are rarely reported as hybrid molecules incorporating biosynthetic machinery from other natural product families. Here we report lipoavitides, a class of RiPP/fatty-acid hybrid lipopeptides that display a unique, putatively membrane-targeting 4-hydroxy-2,4-dimethylpentanoyl (HMP)-modified N terminus. The HMP is formed via condensation of isobutyryl-coenzyme A (isobutyryl-CoA) and methylmalonyl-CoA catalysed by a 3-ketoacyl-(acyl carrier protein) synthase III enzyme, followed by successive tailoring reactions in the fatty acid biosynthetic pathway. The HMP and RiPP substructures are then connected by an acyltransferase exhibiting promiscuous activity towards the fatty acyl and RiPP substrates. Overall, the discovery of lipoavitides contributes a prototype of RiPP/fatty-acid hybrids and provides possible enzymatic tools for lipopeptide bioengineering.

Characterization of a LanC-free pathway for the formation of an ll-MeLan residue and an alloAviMeCys residue in the newly identified class V lanthipeptide triantimycins

The thioether-connected bis-amino acid lanthionine (Lan) residues are class-defining residues of lanthipeptides. Typically, the cyclization step of lanthionine formation, which relies on the addition of a cysteine to an unsaturated dehydroamino acid, is directed either by a standalone cyclase LanC (class I) or by a cyclase domain (class II-IV). However, the pathways of characterized class V members often lack a known cyclase (domain), raising a question on the mechanism by which their multi-macrocycle systems are formed. Herein, we report a new RiPP gene cluster in Streptomyces TN 58, where it encodes the biosynthesis of 3 distinct class V lanthipeptides-termed triantimycins (TAMs). TAM A1∼A3 share an N-terminal ll-MeLan residue, and only TAM A1 contains an additional internal ll-Lan residue. TAM A1 also has a C-terminal (2S, 3R)-S-((Z)-2-aminovinyl)-3-methyl-d-cysteine (alloAviMeCys) residue, which is distinct from the previously reported (2S, 3S)-AviMeCys residue in other RiPPs. Gene deletion, heterologous coexpression, and structural elucidation demonstrated that the cyclization for an ll-MeLan formation occurs spontaneously and is independent of any known lanthionine cyclase. This study provides a new paradigm for lanthionine formation and facilitates genome mining and engineering efforts on RiPPs containing (Me)Lan and (allo)Avi(Me)Cys residues.

BGCFlow: systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets

Genome mining is revolutionizing natural products discovery efforts. The rapid increase in available genomes demands comprehensive computational platforms to effectively extract biosynthetic knowledge encoded across bacterial pangenomes. Here, we present BGCFlow, a novel systematic workflow integrating analytics for large-scale genome mining of bacterial pangenomes. BGCFlow incorporates several genome analytics and mining tools grouped into five common stages of analysis such as: (i) data selection, (ii) functional annotation, (iii) phylogenetic analysis, (iv) genome mining, and (v) comparative analysis. Furthermore, BGCFlow provides easy configuration of different projects, parallel distribution, scheduled job monitoring, an interactive database to visualize tables, exploratory Jupyter Notebooks, and customized reports. Here, we demonstrate the application of BGCFlow by investigating the phylogenetic distribution of various biosynthetic gene clusters detected across 42 genomes of the Saccharopolyspora genus, known to produce industrially important secondary/specialized metabolites. The BGCFlow-guided analysis predicted more accurate dereplication of BGCs and guided the targeted comparative analysis of selected RiPPs. The scalable, interoperable, adaptable, re-entrant, and reproducible nature of the BGCFlow will provide an effective novel way to extract the biosynthetic knowledge from the ever-growing genomic datasets of biotechnologically relevant bacterial species.

Rule-based omics mining reveals antimicrobial macrocyclic peptides against drug-resistant clinical isolates

Antimicrobial resistance remains a significant global threat, driving up mortality rates worldwide. Ribosomally synthesized and post-translationally modified peptides have emerged as a promising source of novel peptide antibiotics due to their diverse chemical structures. Here, we report the discovery of new aminovinyl-(methyl)cysteine (Avi(Me)Cys)-containing peptide antibiotics through a synergistic approach combining biosynthetic rule-based omics mining and heterologous expression. We first bioinformatically identify 1172 RiPP biosynthetic gene clusters (BGCs) responsible for Avi(Me)Cys-containing peptides formation from a vast pool of over 50,000 bacterial genomes. Subsequently, we successfully establish the connection between three identified BGCs and the biosynthesis of five peptide antibiotics via biosynthetic rule-guided metabolic analysis. Notably, we discover a class V lanthipeptide, massatide A, which displays excellent activity against gram-positive pathogens, including drug-resistant clinical isolates like linezolid-resistant S. aureus and methicillin-resistant S. aureus, with a minimum inhibitory concentration of 0.25 μg/mL. The remarkable performance of massatide A in an animal infection model, coupled with a relatively low risk of resistance and favorable safety profile, positions it as a promising candidate for antibiotic development. Our study highlights the potential of Avi(Me)Cys-containing peptides in expanding the arsenal of antibiotics against multi-drug-resistant bacteria, offering promising drug leads in the ongoing battle against infectious diseases.

Synteruptor: mining genomic islands for non-classical specialized metabolite gene clusters

Microbial specialized metabolite biosynthetic gene clusters (SMBGCs) are a formidable source of natural products of pharmaceutical interest. With the multiplication of genomic data available, very efficient bioinformatic tools for automatic SMBGC detection have been developed. Nevertheless, most of these tools identify SMBGCs based on sequence similarity with enzymes typically involved in specialised metabolism and thus may miss SMBGCs coding for undercharacterised enzymes. Here we present Synteruptor (https://bioi2.i2bc.paris-saclay.fr/synteruptor), a program that identifies genomic islands, known to be enriched in SMBGCs, in the genomes of closely related species. With this tool, we identified a SMBGC in the genome of Streptomyces ambofaciens ATCC23877, undetected by antiSMASH versions prior to antiSMASH 5, and experimentally demonstrated that it directs the biosynthesis of two metabolites, one of which was identified as sphydrofuran. Synteruptor is also a valuable resource for the delineation of individual SMBGCs within antiSMASH regions that may encompass multiple clusters, and for refining the boundaries of these SMBGCs.

Kinetic Analysis of Lanthipeptide Cyclization by Substrate-Tolerant ProcM

Lanthipeptides are ribosomally synthesized and post-translationally modified peptides characterized by the presence of thioether crosslinks. Class II lanthipeptide synthetases are bifunctional enzymes responsible for the multistep chemical modification of these natural products. ProcM is a class II lanthipeptide synthetase known for its remarkable substrate tolerance and ability to install diverse (methyl)lanthionine rings with high accuracy. Previous studies suggested that the final ring pattern of the lanthipeptide product may be determined by the substrate sequence rather than by ProcM, and that ProcM operates by a kinetically controlled mechanism, wherein the ring pattern is dictated by the relative rates of the individual cyclization reactions. This study utilizes kinetic assays to determine if rates of isolated modifications can predict the final ring pattern present in prochlorosins. Changes in the core substrate sequence resulted in changes to the reaction rates of ring formation as well as a change in the order of modifications. Additionally, individual chemical reaction rates were significantly impacted by the presence of other modifications on the peptide. These findings indicate that the rates of isolated modifications are capable of predicting the final ring pattern but are not necessarily a good predictor of the order of modification in WT ProcA3.3 and its variants.

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.

Antagonistic actions of Paucibacter aquatile B51 and its lasso peptide paucinodin toward cyanobacterial bloom-forming Microcystis aeruginosa PCC7806

Superior antagonistic activity against axenic Microcystis aeruginosa PCC7806 was observed with Paucibacter sp. B51 isolated from cyanobacterial bloom samples among 43 tested freshwater bacterial species. Complete genome sequencing, analyzing average nucleotide identity and digital DNA-DNA hybridization, designated the B51 strain as Paucibacter aquatile. Electron and fluorescence microscopic image analyses revealed the presence of the B51 strain in the vicinity of M. aeruginosa cells, which might provoke direct inhibition of the photosynthetic activity of the PCC7806 cells, leading to perturbation of cellular metabolisms and consequent cell death. Our speculation was supported by the findings that growth failure of the PCC7806 cells led to low pH conditions with fewer chlorophylls and down-regulation of photosystem genes (e.g., psbD and psaB) during their 48-h co-culture condition. Interestingly, the concentrated ethyl acetate extracts obtained from B51-grown supernatant exhibited a growth-inhibitory effect on PCC7806. The physical separation of both strains by a filter system led to no inhibitory activity of the B51 cells, suggesting that contact-mediated anti-cyanobacterial compounds might also be responsible for hampering the growth of the PCC7806 cells. Bioinformatic tools identified 12 gene clusters that possibly produce secondary metabolites, including a class II lasso peptide in the B51 genome. Further chemical analysis demonstrated anti-cyanobacterial activity from fractionated samples having a rubrivinodin-like lasso peptide, named paucinodin. Taken together, both contact-mediated inhibition of photosynthesis and the lasso peptide secretion of the B51 strain are responsible for the anti-cyanobacterial activity of P. aquatile B51.

Facile Method for Determining Lanthipeptide Stereochemistry

Lanthipeptides make up a large group of natural products that belong to the ribosomally synthesized and post-translationally modified peptides (RiPPs). Lanthipeptides contain lanthionine and methyllanthionine bis-amino acids that have varying stereochemistry. The stereochemistry of new lanthipeptides is often not determined because current methods require equipment that is not standard in most laboratories. In this study, we developed a facile, efficient, and user-friendly method for detecting lanthipeptide stereochemistry, utilizing advanced Marfey's analysis with detection by liquid chromatography coupled with mass spectrometry (LC-MS). Under optimized conditions, 0.05 mg of peptide is sufficient to characterize the stereochemistry of five (methyl)lanthionines of different stereochemistry using a simple liquid chromatography setup, which is a much lower detection limit than current methods. In addition, we describe methods to readily access standards of the three different methyllanthionine stereoisomers and two different lanthionine stereoisomers that have been reported in known lanthipeptides. The developed workflow uses a commonly used nonchiral column system and offers a scalable platform to assist antimicrobial discovery. We illustrate its utility with an example of a lanthipeptide discovered by genome mining.

Uncovering the diversity and distribution of biosynthetic gene clusters of prochlorosins and other putative RiPPs in marine Synechococcus strains

IMPORTANCE

Genome mining studies have revealed the remarkable combinatorial diversity of ribosomally synthesized and post-translationally modified peptides (RiPPs) in marine bacteria, including prochlorosins. However, mining strategies also prove valuable in investigating the genomic landscape of associated genes within biosynthetic gene cluster (BGC) specific to targeted RiPPs of interest. Our study contributes to the enrichment of knowledge regarding prochlorosin diversity. It offers insights into potential mechanisms involved in their biosynthesis and modification, such as hyper-modification, which may give rise to active lantibiotics. Additionally, our study uncovers putative novel promiscuous post-translational enzymes, thereby expanding the chemical space explored within the Synechococcus genus. Moreover, this research extends the applications of mining techniques beyond the discovery of new RiPP-like clusters, allowing for a deeper understanding of genomics and diversity. Furthermore, it holds the potential to reveal previously unknown functions within the intriguing RiPP families, particularly in the case of prochlorosins.

Evolutionary Spread of Distinct O-methyltransferases Guides the Discovery of Unique Isoaspartate-Containing Peptides, Pamtides

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a structurally diverse class of natural products with a distinct biosynthetic logic, the enzymatic modification of genetically encoded precursor peptides. Although their structural and biosynthetic diversity remains largely underexplored, the identification of novel subclasses with unique structural motifs and biosynthetic pathways is challenging. Here, it is reported that peptide/protein L-aspartyl O-methyltransferases (PAMTs) present in several RiPP subclasses are highly homologous. Importantly, it is discovered that the apparent evolutionary transmission of the PAMT gene to unrelated RiPP subclasses can serve as a basis to identify a novel RiPP subclass. Biochemical and structural analyses suggest that homologous PAMTs convert aspartate to isoaspartate via aspartyl-O-methyl ester and aspartimide intermediates, and often require cyclic or hairpin-like structures for modification. By conducting homology-based bioinformatic analysis of PAMTs, over 2,800 biosynthetic gene clusters (BGCs) are identified for known RiPP subclasses in which PAMTs install a secondary modification, and over 1,500 BGCs where PAMTs function as a primary modification enzyme, thereby defining a new RiPP subclass, named pamtides. The results suggest that the genome mining of proteins with secondary biosynthetic roles can be an effective strategy for discovering novel biosynthetic pathways of RiPPs through the principle of "guilt by association".

The untapped potential of actinobacterial lanthipeptides as therapeutic agents

The increase in bacterial resistance generated by the indiscriminate use of antibiotics in medical practice set new challenges for discovering bioactive natural products as alternatives for therapeutics. Lanthipeptides are an attractive natural product group that has been only partially explored and shows engaging biological activities. These molecules are small peptides with potential application as therapeutic agents. Some members show antibiotic activity against problematic drug-resistant pathogens and against a wide variety of viruses. Nevertheless, their biological activities are not restricted to antimicrobials, as their contribution to the treatment of cystic fibrosis, cancer, pain symptoms, control of inflammation, and blood pressure has been demonstrated. The study of biosynthetic gene clusters through genome mining has contributed to accelerating the discovery, enlargement, and diversification of this group of natural products. In this review, we provide insight into the recent advances in the development and research of actinobacterial lanthipeptides that hold great potential as therapeutics.

Artificial intelligence for natural product drug discovery

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Non-modular Fatty Acid Synthases Yield Unique Acylation in Ribosomal Peptides

Recent efforts in genome mining of ribosomally synthesized and post-translationally modified peptides (RiPPs) have expanded the diversity of post-translational modification chemistries 1, 2 . However, RiPPs are rarely reported as hybrid molecules incorporating biosynthetic machineries from other natural product families 3-8 . Here, we report lipoavitides, a class of RiPP/fatty acid hybrid lipopeptides that display a unique, membrane-targeting 4-hydroxy-2,4-dimethylpentanoyl (HMP)-modified N -terminus. The HMP is formed via condensation of isobutyryl-CoA and methylmalonyl-CoA catalyzed by a 3-ketoacyl-ACP synthase III enzyme, followed by successive tailoring reactions in the fatty acid biosynthetic pathway. The HMP and RiPP substructures are then connected by an acyltransferase exhibiting promiscuous activity towards the fatty acyl and RiPP substrates. Overall, the discovery of lipoavitides contributes a prototype of RiPP/fatty acid hybrids and provides possible enzymatic tools for lipopeptide bioengineering.

Structure and Function of a Class III Metal-Independent Lanthipeptide Synthetase

In bacteria, Ser/Thr protein kinase-like sequences are found as part of large multidomain polypeptides that biosynthesize lanthipeptides, a class of natural products distinguished by the presence of thioether cross-links. The kinase domain phosphorylates Ser or Thr residues in the peptide substrates. Subsequent β-elimination by a lyase domain yields electrophilic dehydroamino acids, which can undergo cyclase domain-catalyzed cyclization to yield conformationally restricted, bioactive compounds. Here, we reconstitute the biosynthetic pathway for a class III lanthipeptide from Bacillus thuringiensis NRRL B-23139, including characterization of a two-component protease for leader peptide excision. We also describe the first crystal structures of a class III lanthipeptide synthetase, consisting of the lyase, kinase, and cyclase domains, in various states including complexes with its leader peptide and nucleotide. The structure shows interactions between all three domains that result in an active conformation of the kinase domain. Biochemical analysis demonstrates that the three domains undergo movement upon binding of the leader peptide to establish interdomain allosteric interactions that stabilize this active form. These studies inform on the regulatory mechanism of substrate recognition and provide a framework for engineering of variants of biotechnological interest.

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.

Genome Mining and Discovery of Imiditides, a Family of RiPPs with a Class-Defining Aspartimide Modification

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a large and diverse class of natural products of ribosomal origin. In the past decade, various sophisticated machine-learning-based software packages have been established to discover novel RiPPs that do not resemble the known families. Here, we show that tailoring enzymes that cluster with various RiPP families can serve as effective bioinformatic seeds, providing a complementary approach for novel RiPP discovery. Leveraging the fact that O-methyltransferases homologous to protein isoaspartyl methyltransferases (PIMTs) are associated with lasso peptide, graspetide, and lanthipeptide biosynthetic gene clusters (BGCs), we utilized a C-terminal motif unique to RiPP-associated O-methyltransferases as the search query to discover a novel family of RiPPs, the imiditides. Our genome-mining algorithm reveals a total of 670 imiditide BGCs, distributed across Gram-positive bacterial genomes. In addition, we demonstrate the heterologous production of the founding member of the imiditide family, mNmaAM, encoded in the genome of Nonomuraea maritima. In contrast to other RiPP-associated PIMTs that recognize constrained peptides as substrates, the PIMT homologue in the mNmaAM BGC, NmaM, methylates a specific Asp residue on the linear precursor peptide, NmaA. The methyl ester is then turned into an aspartimide spontaneously. Substrate specificity is achieved by extensive charge-charge interactions between the precursor NmaA and the modifying enzyme NmaM suggested by both experiments and an AlphaFold model prediction. Our study shows that PIMT-mediated aspartimide formation is an emerging backbone modification strategy in the biosynthesis of multiple RiPP families.

Metabolome-guided genome mining of RiPP natural products

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a chemically diverse class of metabolites. Many RiPPs show potent biological activities that make them attractive starting points for drug development. A promising approach for the discovery of new classes of RiPPs is genome mining. However, the accuracy of genome mining is hampered by the lack of signature genes shared across different RiPP classes. One way to reduce false-positive predictions is by complementing genomic information with metabolomics data. In recent years, several new approaches addressing such integrative genomics and metabolomics analyses have been developed. In this review, we provide a detailed discussion of RiPP-compatible software tools that integrate paired genomics and metabolomics data. We highlight current challenges in data integration and identify opportunities for further developments targeting new classes of bioactive RiPPs.

The conformationally dynamic structural biology of lanthipeptide biosynthesis

Lanthipeptide synthetases are fascinating biosynthetic enzymes that install intramolecular thioether bridges into genetically encoded peptides, typically endowing the peptide with therapeutic properties. The factors that control the macrocyclic topology of lanthipeptides are numerous and remain difficult to predict and manipulate. The key challenge in this endeavor derives from the vast conformational space accessible to the disordered precursor lanthipeptide, which can be manipulated in subtle ways by interaction with the cognate synthetase. This review explores the unique strategies employed by each of the five phylogenetically divergent classes of lanthipeptide synthetase to manipulate and exploit the dynamic lanthipeptide conformational ensemble, collectively enabling these biosynthetic enzymes to guide peptide maturation along specific trajectories to products with distinct macrocyclic topology and biological activity.

Development of a Streptomyces-based system for facile thioholgamide library generation and analysis

Derivatizing natural products (NPs) is essential in structure-activity relationship (SAR) studies, compound optimization, and drug development. Ribosomally synthesized and post-translationally modified peptides (RiPPs) represent one of the major classes of natural products. Thioholgamide represents thioamitide - a recently emerged family of RiPPs with unique structures and great potential in anticancer drug development. Although the method for generating the RiPP library by codon substitutions in the precursor peptide gene is straightforward, the techniques to perform RiPP derivatization in Actinobacteria remain limited and time-consuming. Here, we report a facile system for producing a library of randomized thioholgamide derivatives utilizing an optimized Streptomyces host. This technique enabled us to access all possible amino acid substitutions of the thioholgamide molecule, one position at a time. Out of 152 potential derivatives, 85 were successfully detected, revealing the impact of amino acid substitutions on thioholgamide post-translational modifications (PTMs). Moreover, new PTMs were observed among thioholgamide derivatives: thiazoline heterocycles, which have not yet been reported for thioamitides, and S-methylmethionine, which is very rare in nature. The obtained library was subsequently used for thioholgamide SAR studies and stability assays.

Genome Mining and Discovery of Imiditides, a Novel Family of RiPPs with a Class-defining Aspartimide Modification

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a fascinating class of natural products of ribosomal origins. In the past decade, various sophisticated machine learning-based software packages have been established to discover novel RiPPs that do not resemble the known families. Instead, we argue that tailoring enzymes that cluster with various RiPP families can serve as effective bioinformatic seeds for novel RiPP discovery. Leveraging that O -methyltransferases homologous to protein isoaspartyl methyltransferases (PIMTs) are associated with lasso peptide, graspetide, and lanthipeptide biosynthetic gene clusters (BGCs), we utilized the C-terminal motif unique to RiPP-associated O -methyltransferases as the search query to discover a novel family of RiPPs, imiditides. Our genome-mining algorithm reveals a total of 670 imiditide BGCs, widely distributed in Gram-positive bacterial genomes. In addition, we demonstrate the heterologous production of the founding member of the imiditide family, mNmaA M , encoded in the genome of Nonomuraea maritima . In contrast to other RiPP associated PIMTs that recognize constrained peptides as substrates, the PIMT homolog in mNmaA M BGC, NmaM, methylates a specific Asp residue on the linear precursor peptide, NmaA. The methyl ester is then turned into an aspartimide spontaneously. The aspartimide moiety formed is unusually stable, leading to the accumulation of the aspartimidylated product in vivo . The substrate specificity is achieved by extensive charge-charge interactions between the precursor NmaA and the modifying enzyme NmaM suggested by both experimental validations as well as an AlphaFold model prediction. Our study suggests that PIMT-mediated aspartimide formation is an underappreciated backbone modification strategy in RiPP biosynthesis, compared to the well-studied backbone rigidification chemistries, such as thiazol(in)e and oxazol(in)e formations. Additionally, our findings suggest that aspartimide formation in Gram-positive bacterial proteomes are not limited to spontaneous protein aging and degradation.

TOC Figure

Genome mining unveils a class of ribosomal peptides with two amino termini

The era of inexpensive genome sequencing and improved bioinformatics tools has reenergized the study of natural products, including the ribosomally synthesized and post-translationally modified peptides (RiPPs). In recent years, RiPP discovery has challenged preconceptions about the scope of post-translational modification chemistry, but genome mining of new RiPP classes remains an unsolved challenge. Here, we report a RiPP class defined by an unusual (S)-N2,N2-dimethyl-1,2-propanediamine (Dmp)-modified C-terminus, which we term the daptides. Nearly 500 daptide biosynthetic gene clusters (BGCs) were identified by analyzing the RiPP Recognition Element (RRE), a common substrate-binding domain found in half of prokaryotic RiPP classes. A representative daptide BGC from Microbacterium paraoxydans DSM 15019 was selected for experimental characterization. Derived from a C-terminal threonine residue, the class-defining Dmp is installed over three steps by an oxidative decarboxylase, aminotransferase, and methyltransferase. Daptides uniquely harbor two positively charged termini, and thus we suspect this modification could aid in membrane targeting, as corroborated by hemolysis assays. Our studies further show that the oxidative decarboxylation step requires a functionally unannotated accessory protein. Fused to the C-terminus of the accessory protein is an RRE domain, which delivers the unmodified substrate peptide to the oxidative decarboxylase. This discovery of a class-defining post-translational modification in RiPPs may serve as a prototype for unveiling additional RiPP classes through genome mining.

Improved production of class I lanthipeptides in Escherichia coli

Lanthipeptides are ribosomally synthesised and post-translationally modified peptides containing lanthionine (Lan) and methyllanthionine (MeLan) residues that are formed by dehydration of Ser/Thr residues followed by conjugate addition of Cys to the resulting dehydroamino acids. Class I lanthipeptide dehydratases utilize glutamyl-tRNAGlu as a co-substrate to glutamylate Ser/Thr followed by glutamate elimination. Here we report a new system to heterologously express class I lanthipeptides in Escherichia coli through co-expression of the producing organism's glutamyl-tRNA synthetase (GluRS) and tRNAGlu pair in the vector pEVOL. In contrast to the results in the absence of the pEVOL system, we observed the production of fully-dehydrated peptides, including epilancin 15X, and peptides from the Bacteroidota Chryseobacterium and Runella. A second common obstacle to production of lanthipeptides in E. coli is the formation of glutathione adducts. LanC-like (LanCL) enzymes were previously reported to add glutathione to dehydroamino-acid-containing proteins in Eukarya. Herein, we demonstrate that the LanCL enzymes can remove GSH adducts from C-glutathionylated peptides with dl- or ll-lanthionine stereochemistry. These two advances will aid synthetic biology-driven genome mining efforts to discover new lanthipeptides.

Genome mining unveils a class of ribosomal peptides with two amino termini

The era of inexpensive genome sequencing and improved bioinformatics tools has reenergized the study of natural products, including the ribosomally synthesized and post-translationally modified peptides (RiPPs). In recent years, RiPP discovery has challenged preconceptions about the scope of post-translational modification chemistry, but genome mining of new RiPP classes remains an unsolved challenge. Here, we report a RiPP class defined by an unusual ( S )- N 2 , N 2 -dimethyl-1,2-propanediamine (Dmp)-modified C -terminus, which we term the daptides. Nearly 500 daptide biosynthetic gene clusters (BGCs) were identified by analyzing the RiPP Recognition Element (RRE), a common substrate-binding domain found in half of prokaryotic RiPP classes. A representative daptide BGC from Microbacterium paraoxydans DSM 15019 was selected for experimental characterization. Derived from a C -terminal threonine residue, the class-defining Dmp is installed over three steps by an oxidative decarboxylase, aminotransferase, and methyltransferase. Daptides uniquely harbor two positively charged termini, and thus we suspect this modification could aid in membrane targeting, as corroborated by hemolysis assays. Our studies further show that the oxidative decarboxylation step requires a functionally unannotated accessory protein. Fused to the C -terminus of the accessory protein is an RRE domain, which delivers the unmodified substrate peptide to the oxidative decarboxylase. This discovery of a class-defining post-translational modification in RiPPs may serve as a prototype for unveiling additional RiPP classes through genome mining.

Recent Advances in Discovery, Bioengineering, and Bioactivity-Evaluation of Ribosomally Synthesized and Post-translationally Modified Peptides

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are of increasing interest in natural products as well as drug discovery. This empowers not only the unique chemical structures and topologies in natural products but also the excellent bioactivities such as antibacteria, antifungi, antiviruses, and so on. Advances in genomics, bioinformatics, and chemical analytics have promoted the exponential increase of RiPPs as well as the evaluation of biological activities thereof. Furthermore, benefiting from their relatively simple and conserved biosynthetic logic, RiPPs are prone to be engineered to obtain diverse analogues that exhibit distinct physiological activities and are difficult to synthesize. This Review aims to systematically address the variety of biological activities and/or the mode of mechanisms of novel RiPPs discovered in the past decade, albeit the characteristics of selective structures and biosynthetic mechanisms are briefly covered as well. Almost one-half of the cases are involved in anti-Gram-positive bacteria. Meanwhile, an increasing number of RiPPs related to anti-Gram-negative bacteria, antitumor, antivirus, etc., are also discussed in detail. Last but not least, we sum up some disciplines of the RiPPs' biological activities to guide genome mining as well as drug discovery and optimization in the future.

Metabolomics- and systems-biology-guided discovery of metabolite lead compounds and druggable targets

Metabolomics enables the comprehensive and unbiased analysis of metabolites and lipids in biological systems. In conjunction with high-throughput activity screening, big data and synthetic biology, metabolomics can guide the discovery of lead compounds with pharmacological activity from natural sources and the gut microbiome. In combination with other omics, metabolomics can further unlock the elucidation of compound toxicity, the mode of action and novel druggable targets of disease. Here, we discuss the workflows, limitations and future opportunities to leverage metabolomics and big data in conjunction with systems and synthetic biology for streamlining the discovery and development of molecules of pharmaceutical interest.

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.

Artificial intelligence as a smart approach to develop antimicrobial drug molecules: A paradigm to combat drug-resistant infections

Antimicrobial resistance (AMR) is a silent pandemic with the third highest global mortality. The antibiotic development pipeline is scarce even though AMR has escalated uncontrollably. Artificial intelligence (AI) is a revolutionary approach, accelerating drug discovery because of its fast pace, cost efficiency, lower labor requirements, and fewer chances of failure. AI has been used to discover several beta-lactamase inhibitors and antibiotic alternatives from antimicrobial peptides (AMPs), nonribosomal peptides, bacteriocins, and marine natural products. The significant recent increase in the use of AI platforms by pharmaceutical companies could result in the discovery of efficient antibiotic alternatives with lower chances of resistance generation.

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.

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.

Navigating and expanding the roadmap of natural product genome mining tools

Natural products are structurally highly diverse and exhibit a wide array of biological activities. As a result, they serve as an important source of new drug leads. Traditionally, natural products have been discovered by bioactivity-guided fractionation. The advent of genome sequencing technology has resulted in the introduction of an alternative approach towards novel natural product scaffolds: Genome mining. Genome mining is an in-silico natural product discovery strategy in which sequenced genomes are analyzed for the potential of the associated organism to produce natural products. Seemingly universal biosynthetic principles have been deciphered for most natural product classes that are used to detect natural product biosynthetic gene clusters using pathway-encoded conserved key enzymes, domains, or motifs as bait. Several generations of highly sophisticated tools have been developed for the biosynthetic rule-based identification of natural product gene clusters. Apart from these hard-coded algorithms, multiple tools that use machine learning-based approaches have been designed to complement the existing genome mining tool set and focus on natural product gene clusters that lack genes with conserved signature sequences. In this perspective, we take a closer look at state-of-the-art genome mining tools that are based on either hard-coded rules or machine learning algorithms, with an emphasis on the confidence of their predictions and potential to identify non-canonical natural product biosynthetic gene clusters. We highlight the genome mining pipelines' current strengths and limitations by contrasting their advantages and disadvantages. Moreover, we introduce two indirect biosynthetic gene cluster identification strategies that complement current workflows. The combination of all genome mining approaches will pave the way towards a more comprehensive understanding of the full biosynthetic repertoire encoded in microbial genome sequences.