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At least 19 recordsLinked to original sources
PubMed2025

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗
PubMed2026

Artificial Intelligence for Natural Products Discovery and Development.

Natural products (NPs) remain a cornerstone of modern drug discovery, offering stereochemical complexity and diverse bioactivities that precisely modulate therapeutic targets, refined through billions of years of evolution. However, their research has long been hindered by inefficient, empirical workflows, high resource consumption, structural complexity, and the "multicomponent, multi-target" nature of their mechanisms. The exponential growth of genomic, metabolomic, and spectral data has overwhelmed conventional analytical methods, exposing critical bottlenecks in handling high-dimensional, heterogeneous datasets that exceed human interpretive capacity. Artificial intelligence (AI) is emerging as a transformative paradigm to address these challenges, integrating multi-omics and chemical data to shift NP research from fragmented empiricism toward mechanism-driven, precision-oriented development. By leveraging deep learning architectures- including graph neural networks, Transformers, and diffusion-based generative models-AI enables systematic decoding of NP biosynthesis, automated structure elucidation, rational target identification, knowledge extraction from vast unstructured scientific literature, and de novo molecular design. This review comprehensively surveys recent advances in AI applications across the full NP discovery and development pipeline, encompassing genome mining, structure-based and ligand-based virtual screening, multimodal structural characterization, lead optimization, and biosynthetic pathway engineering. We further examine the emerging roles of protein-centric, molecule- centric, and multimodal foundation models, as well as large language models, in bridging genotype-to-chemotype gaps and unlocking unstructured scientific knowledge. Finally, we discuss critical challenges including data scarcity, representational limitations for complex stereochemistry, physical plausibility in generative models, and the urgent need for experimental validation, while outlining future directions toward autonomous experimentation, closed-loop optimization, and human-AI collaborative discovery.

Artificial intelligence↗
PubMed2026

Recent discovery of new enzymes in plant natural product biosynthesis.

Plants are a vast reservoir of natural products with diverse structural scaffolds, making them an invaluable source for discovering novel enzymes that catalyze unique and evolutionarily specialized metabolic transformations in biosynthetic pathways. Rapid advances in genomics, metabolomics, protein structure prediction, and heterologous pathway reconstruction have enabled the identification of numerous cryptic biosynthetic enzymes responsible for key scaffold-forming and tailoring reactions in metabolism. Particularly notable are the discoveries of plant-derived enzymes that catalyze challenging chemical transformations, including oxidative carbon-carbon bond rearrangements, atypical cycloadditions, radical-mediated coupling reactions, and iterative scaffold remodeling. This review summarizes major advances in enzyme discovery in plant natural product biosynthesis in recent years, focusing on emerging catalytic mechanisms, strategies for elucidating pathways, and evolutionary relationships, and highlights their implications for synthetic biology, metabolic engineering, and the sustainable production of valuable natural products.

Biological Products↗
PubMed2025

Discovery, biosynthesis, and bioactivities of peptidic natural products from marine sponges and sponge-associated bacteria.

Covering 2010 to 2025Sponges are benthic, sessile invertebrate metazoans that are some of the most prolific sources of natural products in the marine environment. Sponge-derived natural products are often endowed with favorable pharmaceutical bioactivities, and paired with their structural complexity, have long served as title compounds for chemical syntheses. Sponges are holobionts, in that the sponge host is associated with symbiotic and commensal microbiome. Natural products isolated from sponges can be produced by the sponge host, or the associated microbiome. Recent genomic studies have shed light on the sponge eukaryotic host as the true producer of several classes of sponge-derived peptidic natural products. In this review spanning years 2010-2025, we describe peptidic natural products isolated from the sponge hosts and the associated microbiome, detail their biosynthetic processes where known, and offer forward looking insights into future innovation in discovery and biosynthesis of peptidic natural products from marine sponges.

Porifera↗
PubMed2025

Deazapurine Amide-Bond Synthetases: a New Family of Amide-Bond-Forming Enzymes Driving the Diversity of Peptidyl Deazapurine Natural Products.

Amide bond-forming enzymes play a crucial role in generating structural diversity in natural products by assembling them from relatively simple precursors. Two distinct types of standalone amid-bond-forming enzymes are commonly involved in natural product biosynthesis, including ATP-grasp enzymes and amide bond synthetases. Here, we report a new family of amide bond synthetases that catalyze amide bond formation between deazapurine as the sole carboxylic acid substrate and various amine substrates, which we have designated as deazapurine amide bond synthetases (DABS). This evolutionarily related enzyme family plays a central role in diversifying the structures of peptidyl deazapurine natural products. Our gene mining analysis reveals that most DABS-associated biosynthetic gene clusters (BGCs) remain cryptic. Therefore, systematic characterization of these cryptic BGCs holds great potential for discovering novel peptidyl deazapurine natural products with diverse biological activities.

Biological Products↗
PubMed2026

Unveiling Aziridine-Containing Natural Products by Genomic and Spectroscopic Approaches.

Aziridine-containing natural products are prized for their potent bioactivities, yet their scarcity and poorly understood biosynthesis have limited systematic exploration. Here, we address this by integrating genome mining with a 1H-13C coupled HSQC metabolomic approach that exploits the distinctive NMR signatures of aziridines, enabling their direct detection from complex extracts. This strategy unveiled the desertolides, the first macrolides incorporating a rare terminal 2-methyl-aziridine-2-carboxylate moiety. Genetic and isotopic studies identified a dedicated biosynthetic subcluster (desA-desN) that assembles and installs this unit from glutamate, and heterologous expression confirmed the self-sufficiency of this subcluster. Direct MS evidence reveals the aziridine moiety covalently bound to the active-site Cys113 of DesN, establishing this KAS III homolog as the first dedicated aziridine-transferase and a promising tool for polyketide engineering. Bioinformatic analysis uncovered over 50 biosynthetic gene clusters, suggesting that this aziridine-associated biosynthetic logic may be more widespread than currently appreciated. This work establishes a tractable platform for the targeted discovery and engineered biosynthesis of aziridine-containing natural products, opening this underexplored pharmacophore to systematic interrogation.

Aziridines↗
PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗
PubMed2025

The Biosynthetic Pathway to the Pyrroloiminoquinone Marine Natural Product Ammosamide C.

Ammosamide C is a marine natural product containing a highly decorated pyrroloiminoquinone core. Studies on the biosynthetic gene cluster (BGC) that produces ammosamides previously revealed that they are made by a series of posttranslational modifications (PTMs). The BGC includes genes encoding a precursor peptide AmmA and four enzymes known as PEptide Aminoacyl-tRNA Ligases (PEARLs). Initial studies into the ammosamide biosynthetic pathway demonstrated Trp addition to a precursor peptide by the PEARL AmmB2. Thereafter, sequential modifications by several enzymes, including two other PEARLs lead to the formation of a peptide intermediate bearing a C-terminal diaminoquinone. In the present work, we present the biosynthetic steps that convert this intermediate to ammosamide C. The PEARL AmmB4 unexpectedly appends an arginine to the C-terminus of the aforementioned intermediate. Then, C-terminal proteolysis by the heterodimeric TldD/E-like protease Amm12/13 releases a dipeptide, which is subsequently cleaved by the dipeptidase Amm19 to produce a Trp-derived diaminoquinone. Amm3 next catalyzes the conversion of this Trp derivative to the corresponding chlorinated ammosamaic acid. Finally, a putative aminotransferase Amm20 performs an amidation, and Amm23 methylates this intermediate to arrive at ammosamide C; the order of these last two steps could not be determined definitively. This study reveals an unexpectedly lengthy route to ammosamide that illustrates the opportunistic nature of natural product biosynthesis, demonstrates a role for a PEARL that is unlike previous roles, identifies steps that are not PTMs, and adds Arg-tRNA to the growing repertoire of aminoacyl tRNAs that are used by PEARLs.

Biological Products↗
PubMed2026

The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure-activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development.

big data analytics↗
PubMed2016

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

Biological Products↗
PubMed2026

Natural products alleviate exercise-induced fatigue by modulating gut microbiota: a systematic review.

BACKGROUND: Exercise-induced fatigue critically impairs athletic performance and training quality. The gut microbiota, as a key regulator of the "gut-muscle axis," has emerged as a promising anti-fatigue target. Natural products - owing to their diverse sources, structural complexity, and favorable safety profiles - have attracted growing research interest. However, a systematic synthesis comparing their anti-fatigue effects via gut microbiota modulation across different sources is lacking. SCOPE AND APPROACH: We systematically searched PubMed, Web of Science, the Cochrane Library, and CNKI for original studies that administered natural products and concurrently assessed gut microbiota changes and anti-fatigue outcomes. Twenty-six studies (25 animal experiments and 1 human trial) were included and categorized into seven groups by source and chemical characteristics. A descriptive systematic review was conducted to identify common mechanisms and source-specific differentiations. KEY FINDINGS AND CONCLUSIONS: The enrichment of short-chain fatty acid (SCFA)-producing bacteria and the activation of the SCFA-AMPK/PGC-1α axis were shared core events across all product categories. However, source-dependent mechanistic divergences emerged: polysaccharides acted primarily as fermentable substrates with an optimal dose window; polyphenols and saponins exerted dual modulation on both microbiota and host signaling pathways; compound extracts achieved systemic synergy through functional complementation; marine- and animal-derived products exhibited unique targeting profiles and rapid action. Intestinal barrier maintenance and brain-gut axis regulation further extended the anti-fatigue repertoire. Collectively, natural products possess a solid mechanistic basis for alleviating exercise-induced fatigue via gut microbiota remodeling. The differentiated characteristics of these methods in targeting precision and pathway engagement provide a theoretical foundation for designing precision intervention strategies tailored to specific fatigue contexts.

Humans↗
PubMed2025

Seq2Saccharide: Discovering Oligosaccharides and Aminoglycosides Natural Products by Integrating Computational Mass Spectrometry and Genome Mining.

Natural oligosaccharides and aminoglycosides are important sources of new drug candidates, especially in the development of antibiotics. In the past, discovering novel saccharides has been time-consuming and costly. However, the rapid expansion of high-throughput data, including genomic and mass spectrometry data sets, has greatly increased opportunities for natural saccharide discovery. Yet, due to the complex biosynthesis pathways of saccharides, no existing method can predict their structures with high precision. To address this, we introduce Seq2Saccharide, a tool designed to automate saccharide natural product discovery by integrating both genomic and mass spectrometry data. To enhance accuracy, Seq2Saccharide predicts hundreds or thousands of putative structures for each gene cluster. The correct structure is then identified from these predictions using a mass spectral search. Benchmarks against saccharides in the MiBIG database show that Seq2Saccharide outperforms existing methods in predicting the structure of saccharides. Furthermore, mass spectrometry analysis indicates that the variable search module can correct mispredictions from genome mining. By searching genomic and mass spectrometry data of microbial strains, Seq2Saccharide correctly identified the biosynthetic gene cluster for the polysaccharide oligosaccharide trestatin B.

Aminoglycosides↗
PubMed2024

Natural product discovery in soil actinomycetes: unlocking their potential within an ecological context.

Natural products (NPs) produced by bacteria, particularly soil actinomycetes, often possess diverse bioactivities and play a crucial role in human health, agriculture, and biotechnology. Soil actinomycete genomes contain a vast number of predicted biosynthetic gene clusters (BGCs) yet to be exploited. Understanding the factors governing NP production in an ecological context and activating cryptic and silent BGCs in soil actinomycetes will provide researchers with a wealth of molecules with potential novel applications. Here, we highlight recent advances in NP discovery strategies employing ecology-inspired approaches and discuss the importance of understanding the environmental signals responsible for activation of NP production, particularly in a soil microbial community context, as well as the challenges that remain.

Soil Microbiology↗
PubMed2026

Identification of a Nonribosomal Peptide Analog With Activity Against Multiple Gram-Positive Bacteria via a Synthetic Bioinformatic Natural Product Discovery Approach.

Nonribosomal peptide (NRP) antibiotics exhibit potent biological activities and are broadly used in clinical therapy. Because most microorganisms are difficult to culture and many antibiotic biosynthetic genes are silent, traditional activity tracking approaches face major limitations in the discovery of novel NRPs. Here, based on a synthetic bioinformatic natural product (syn-BNP) discovery approach that integrates bioinformatics and chemical synthesis, a novel nonribosomal peptide synthetase (NRPS) gene cluster from the genome of Rhodococcus erythropolis D-1 was mined. A putative NRP scaffold synthesized by the NRPS encoded by this cluster was predicted. Through chemical synthesis and four rounds of structure-activity relationship (SAR) studies, 37 NRP analogs were ultimately generated. Among these analogs, ZURJC28 shows activity against multiple Gram-positive bacteria, including two drug-resistant strains. Mechanistic studies and metabolomics analyses revealed that ZURJC28 exerts membrane-disruptive activity associated with interaction with phosphatidylglycerol (PG)-enriched Gram-positive membranes, leading to membrane damage and widespread metabolic dysregulation. ZURJC28 also shows low cytotoxicity and low hemolytic activity, suggesting its preliminary in vitro safety profile.

Gram-Positive Bacteria↗
PubMed2017

A New Natural Product Analog of Blasticidin S Reveals Cellular Uptake Facilitated by the NorA Multidrug Transporter.

The permeation of antibiotics through bacterial membranes to their target site is a crucial determinant of drug activity but in many cases remains poorly understood. During screening efforts to discover new broad-spectrum antibiotic compounds from marine sponge samples, we identified a new analog of the peptidyl nucleoside antibiotic blasticidin S that exhibited up to 16-fold-improved potency against a range of laboratory and clinical bacterial strains which we named P10. Whole-genome sequencing of laboratory-evolved strains of Staphylococcus aureus resistant to blasticidin S and P10, combined with genome-wide assessment of the fitness of barcoded Escherichia coli knockout strains in the presence of the antibiotics, revealed that restriction of cellular access was a key feature in the development of resistance to this class of drug. In particular, the gene encoding the well-characterized multidrug efflux pump NorA was found to be mutated in 69% of all S. aureus isolates resistant to blasticidin S or P10. Unexpectedly, resistance was associated with inactivation of norA, suggesting that the NorA transporter facilitates cellular entry of peptidyl nucleosides in addition to its known role in the efflux of diverse compounds, including fluoroquinolone antibiotics.

Bacterial Proteins↗
PubMed2026

Natural Product Target Identification of Wheldone, a Fungal Metabolite, as a KIF11 Inhibitor in Ovarian Cancer Using the DiffPOP (Differential Protein Precipitation) Method.

Wheldone, a fungal metabolite, was identified as a cytotoxic compound in high-grade serous ovarian cancer (HGSOC). Wheldone induced caspase 3/7-dependent apoptosis and reduced migration, invasion, and spheroid growth. Wheldone stimulated apoptosis in chemoresistant HGSOC models. Wheldone treatment caused significant downregulation of HNRNPD, a DNA repair protein, and increased DNA damage that could be blocked by N-acetyl-L-cysteine. In vivo, wheldone displayed minimal toxicity but was rapidly cleared from circulation, despite in vitro metabolic stability. Wheldone treatment in vivo did not demonstrate significant reduction in tumor burden. Therefore, in order to overcome these liabilities, it was necessary to find the protein target of wheldone so that modifications can be made to improve the drug-like characteristics of the compound. Using the drug-target interaction proteomics method, differential precipitation of proteins, wheldone was found to act as an inhibitor of Kinesin superfamily protein 11 (KIF11), a motor protein essential for mitotic spindle formation. An ATPase biochemical cell-free assay confirmed direct binding and functional inhibition of KIF11. Wheldone resulted in G2/M arrest and downstream regulation of mitotic proteins such as TPX2, AURKA, and phospho-histone H3. Proteomics after treatment of wheldone in four different HGSOC cancer cell lines all supported changes consistent with mitotic spindle assembly disruption. Further, KIF11 was one of only 13 proteins upregulated in all 4 cell lines treated. Overall, wheldone was found to be a fungal metabolite that inhibits KIF11 in chemoresistant ovarian cancer, with future studies needed to improve its pharmacokinetics and delivery.

Female↗
PubMed2026

Accelerating natural product discovery, characterization and engineering by biofoundries.

Covering: From early developments to the presentNatural product (NP) discovery is increasingly constrained by low-throughput screening, repeated rediscovery, and challenges in scaling genome mining-guided validation workflows. This highlight examines how automated biofoundries are accelerating NP discovery, characterization, and engineering through integrated design-build-test-learn (DBTL) cycles. We discuss recent advances in phenotype-first and genome-first discovery strategies enabled by robotics, high-throughput pathway reconstitution, and automated screening platforms. We further highlight emerging technologies, including cell-free biosynthesis, automated culturomics, programmable chassis engineering, and AI-assisted workflow orchestration, that may enable increasingly autonomous biofoundries for scalable exploration of NP chemical space and therapeutic discovery.

Journal Article↗
PubMed2025

Biosynthesis of the 5-Isoxazolidinone-Containing Hexacyclic Structure of Parnafungin.

Parnafungins A-D (1-4) are fungal natural products that inhibit eukaryotic poly(A)-polymerase and were first discovered by Merck & Co., Inc., through a Candida albicans Fitness Test (CaFT) screening program. The biological activity of parnafungins is a result of the unique fused hexacyclic structure highlighted by a 5-isoxazolidinone (5ILD) N-heterocycle. In this work, we characterize the complete biosynthetic pathway of parnafungins through heterologous reconstitution and enzymatic assays. Nearly half of the 26-gene biosynthetic gene cluster of parnafungin is responsible for the production of a known polyketide natural product, blennolide C. Starting from the blennolide C fragment, a three-enzyme cascade involving CoA-ligase ParJ, P450 ParO, and DUF829 ParD catalyzes the formal biaryl cross-coupling between blennolide C and anthranilate. Subsequent oxidative cyclization generates a phenanthridine product that is then reduced by atypical short-chain reductase ParT. N-Hydroxylation by flavin-dependent monooxygenase ParB and subsequent lactonization catalyzed by a homologue of dienenolactone hydrolase ParF form the 5ILD ring and complete the biosynthesis of 1 and 2. Methylation of 1 forms parnafungin C (3), and lastly epoxidation forms parnafungin D (4). Together, our work revealed the chemical logic and enzymology in extending the biosynthetic pathway of a well-characterized natural product, blennolide C, to introduce considerable additional structural diversity that affords parnafungins with unique biological activity.

Molecular Structure↗