Nerpa: A Tool for Discovering Biosynthetic Gene Clusters of Bacterial Nonribosomal Peptides

Interpretable adenylation domain specificity prediction using protein language models

Natural products have long been a rich source of diverse and clinically effective drug candidates. Non-ribosomal peptides (NRPs), polyketides (PKs), and NRP-PK hybrids are three classes of natural products that display a broad range of bioactivities, including antibiotic, antifungal, anticancer, and immunosuppressant activities. However, discovering these compounds through traditional bioactivity-guided techniques is costly and time-consuming, often resulting in the rediscovery of known molecules. Consequently, genome mining has emerged as a high-throughput strategy to screen hundreds of thousands of microbial genomes to identify their potential to produce novel natural products. Adenylation domains play a key role in the biosynthesis of NRPs and NRP-PKs by recruiting substrates to incrementally build the final structure. We propose MASPR, a machine learning method that leverages protein language models for accurate and interpretable predictions of A-domain substrate specificities. MASPR demonstrates superior accuracy and generalization over existing methods and is capable of predicting substrates not present in its training data, or zero-shot classification. We use MASPR to develop Seq2Hybrid, an efficient algorithm to predict the structure of hybrid NRP-PK natural products from microbial genomes. Using Seq2Hybrid, we propose putative biosynthetic gene clusters for the orphan natural products Octaminomycin A, Dityromycin, SW-163B, and JBIR-39.

Unlocking the biosynthetic potential and taxonomy of the Antarctic microbiome along temporal and spatial gradients

Extreme environments, such as Antarctica, select microbial communities that display a range of evolutionary strategies to survive and thrive under harsh environmental conditions. These include a diversity of specialized metabolites, which have the potential to be a source for new natural product discovery. Efforts using (meta)genome mining approaches to identify and understand biosynthetic gene clusters in Antarctica are still scarce, and the extent of their diversity and distribution patterns in the environment have yet to be discovered. Herein, we investigated the biosynthetic gene diversity of the biofilm microbial community of Whalers Bay, Deception Island, in the Antarctic Peninsula and revealed its distribution patterns along spatial and temporal gradients by applying metagenome mining approaches and multivariable analysis. The results showed that the Whalers Bay microbial community harbors a great diversity of biosynthetic gene clusters distributed into seven classes, with terpene being the most abundant. The phyla Proteobacteria and Bacteroidota were the most abundant in the microbial community and contributed significantly to the biosynthetic gene abundances in Whalers Bay. Furthermore, the results highlighted a significant correlation between the distribution of biosynthetic genes and taxonomic diversity, emphasizing the intricate interplay between microbial taxonomy and their potential for specialized metabolite production.IMPORTANCEThis research on antarctic microbial biosynthetic diversity in Whalers Bay, Deception Island, unveils the hidden potential of extreme environments for natural product discovery. By employing metagenomic techniques, the research highlights the extensive diversity of biosynthetic gene clusters and identifies key microbial phyla, Proteobacteria and Bacteroidota, as significant contributors. The correlation between taxonomic diversity and biosynthetic gene distribution underscores the intricate interplay governing specialized metabolite production. These findings are crucial for understanding microbial adaptation in extreme environments and hold significant implications for bioprospecting initiatives. The study opens avenues for discovering novel bioactive compounds with potential applications in medicine and industry, emphasizing the importance of preserving and exploring these polyextreme ecosystems to advance biotechnological and pharmaceutical research.

Unlocking the potential of bacterioplankton-mediated microcystin degradation and removal: A bibliometric analysis of sustainable water treatment strategies

Microcystins (MCs) constitute a significant threat to human and environmental health, urging the development of effective removal methods for these toxins. In this review, we explore the potential of MC-degrading bacteria as a solution for the removal of MCs from water. The review insights into the mechanisms of action employed by these bacteria, elucidating their ability to degrade and thus remove MCs. After, the review points out the influence of the structural conformation of MCs on their removal, particularly their stability at different water depths within different water bodies. Then, we review the crucial role played by the production of MCs in ensuring the survival and safeguarding of the enzymatic activities of Microcystis cells. This justifies the need for developing effective and sustainable methods for removing MCs from aquatic ecosystems, given their critical ecological function and potential toxicity to humans and animals. Thereafter, challenges and limitations associated with using MC-degrading bacteria in water treatment are discussed, emphasizing the need for further research to optimize the selection of bacterial strains used for MCs biodegradation. The interaction of MCs-degrading bacteria with sediment particles is also crucial for their toxin removal potential and its efficiency. By presenting critical information, this review is a valuable resource for researchers, policymakers, and stakeholders involved in developing sustainable and practical approaches to remove MCs. Our review highlights the potential of various applications of MC-degrading bacteria, including multi-soil-layering (MSL) technologies. It emphasizes the need for ongoing research to optimize the utilization of MC-degrading bacteria in water treatment, ultimately ensuring the safety and quality of water sources. Moreover, this review highlights the value of bibliometric analyses in revealing research gaps and trends, providing detailed insights for further investigations. Specifically, we discuss the importance of employing advanced genomics, especially combining various OMICS approaches to identify and optimize the potential of MCs-degrading bacteria.

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.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

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

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

Peptidomics

Peptides are biopolymers, typically consisting of 2-50 amino acids. They are biologically produced by the cellular ribosomal machinery or by non-ribosomal enzymes and, sometimes, other dedicated ligases. Peptides are arranged as linear chains or cycles, and include post-translational modifications, unusual amino acids and stabilizing motifs. Their structure and molecular size render them a unique chemical space, between small molecules and larger proteins. Peptides have important physiological functions as intrinsic signalling molecules, such as neuropeptides and peptide hormones, for cellular or interspecies communication, as toxins to catch prey or as defence molecules to fend off enemies and microorganisms. Clinically, they are gaining popularity as biomarkers or innovative therapeutics; to date there are more than 60 peptide drugs approved and more than 150 in clinical development. The emerging field of peptidomics comprises the comprehensive qualitative and quantitative analysis of the suite of peptides in a biological sample (endogenously produced, or exogenously administered as drugs). Peptidomics employs techniques of genomics, modern proteomics, state-of-the-art analytical chemistry and innovative computational biology, with a specialized set of tools. The complex biological matrices and often low abundance of analytes typically examined in peptidomics experiments require optimized sample preparation and isolation, including in silico analysis. This Primer covers the combination of techniques and workflows needed for peptide discovery and characterization and provides an overview of various biological and clinical applications of peptidomics.

Charting the Lipopeptidome of Nonpathogenic Pseudomonas

A major source of pseudomonad-specialized metabolites is the nonribosomal peptide synthetases (NRPSs) assembling siderophores and lipopeptides. Cyclic lipopeptides (CLPs) of the Mycin and Peptin families are frequently associated with, but not restricted to, phytopathogenic species. We conducted an in silico analysis of the NRPSs encoded by lipopeptide biosynthetic gene clusters in nonpathogenic Pseudomonas genomes, covering 13 chemically diversified families. This global assessment of lipopeptide production capacity revealed it to be confined to the Pseudomonas fluorescens lineage, with most strains synthesizing a single type of CLP. Whereas certain lipopeptide families are specific for a taxonomic subgroup, others are found in distant groups. NRPS activation domain-guided peptide predictions enabled reliable family assignments, including identification of novel members. Focusing on the two most abundant lipopeptide families (Viscosin and Amphisin), a portion of their uncharted diversity was mapped, including characterization of two novel Amphisin family members (nepenthesin and oakridgin). Using NMR fingerprint matching, known Viscosin-family lipopeptides were identified in 15 (type) species spread across different taxonomic groups. A bifurcate genomic organization predominates among Viscosin-family producers and typifies Xantholysin-, Entolysin-, and Poaeamide-family producers but most families feature a single NRPS gene cluster embedded between cognate regulator and transporter genes. The strong correlation observed between NRPS system phylogeny and rpoD-based taxonomic affiliation indicates that much of the structural diversity is linked to speciation, providing few indications of horizontal gene transfer. The grouping of most NRPS systems in four superfamilies based on activation domain homology suggests extensive module dynamics driven by domain deletions, duplications, and exchanges. IMPORTANCE Pseudomonas species are prominent producers of lipopeptides that support proliferation in a multitude of environments and foster varied lifestyles. By genome mining of biosynthetic gene clusters (BGCs) with lipopeptide-specific organization, we mapped the global Pseudomonas lipopeptidome and linked its staggering diversity to taxonomy of the producers, belonging to different groups within the major Pseudomonas fluorescens lineage. Activation domain phylogeny of newly mined lipopeptide synthetases combined with previously characterized enzymes enabled assignment of predicted BGC products to specific lipopeptide families. In addition, novel peptide sequences were detected, showing the value of substrate specificity analysis for prioritization of BGCs for further characterization. NMR fingerprint matching proved an excellent tool to unequivocally identify multiple lipopeptides bioinformatically assigned to the Viscosin family, by far the most abundant one in Pseudomonas and with stereochemistry of all its current members elucidated. In-depth analysis of activation domains provided insight into mechanisms driving lipopeptide structural diversification.

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.

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

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

Carbon-nitrogen bond formation to construct novel polyketide-indole hybrids from the indole-3-carbinol exposed culture of Daldinia eschscholzii

A plenty of cytochrome P450s have been annotated in the Daldinia eschosholzii genome. Inspired by the fact that some P450s have been reported to catalyze the carbon-nitrogen (C-N) bond formation, we were curious about whether hybrids through C-N bond formation could be generated in the indole-3-carbinol (I3C) exposed culture of D. eschscholzii. As expected, two skeletally undescribed polyketide-indole hybrids, designated as indolpolyketone A and B (1 and 2), were isolated and assigned to be constructed through C-N bond formation. Their structures were elucidated by 1D and 2D NMR spectra. The absolute configurations of 1 and 2 were determined by comparing the recorded and calculated electronic circular dichroism (ECD) spectra. Furthermore, the plausible biosynthetic pathways for 1 and 2 were proposed. Compounds 1 and 2 exhibited significant antiviral activity against H1N1 with IC50 values of 45.2 and 31.4 μM, respectively. In brief, compounds 1 and 2 were reported here for the first time and were the first example of polyketide-indole hybrids pieced together through C-N bond formation in the I3C-exposed culture of D. eschscholzii. Therefore, this study expands the knowledge about the chemical production of D. eschscholzii through precursor-directed biosynthesis (PDB).