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Marine cyanobacteria (blue-green algae) have been shown to possess an enormous capacity to produce structurally diverse natural products that exhibit a broad spectrum of potent biological activities, including cytotoxic, antifungal, antiparasitic, antiviral, and antibacterial activities. Using mass-spectrometry-guided fractionation together with molecular networking, cyanobacterial field collections from American Samoa and Palmyra Atoll yielded three new cyclic peptides, tutuilamides A-C. Their structures were established by spectroscopic techniques including 1D and 2D NMR, HR-MS, and chemical derivatization. Structure elucidation was facilitated by employing advanced NMR techniques including nonuniform sampling in combination with the 1,1-ADEQUATE experiment. These cyclic peptides are characterized by the presence of several unusual residues including 3-amino-6-hydroxy-2-piperidone and 2-amino-2-butenoic acid, together with a novel vinyl chloride-containing residue. Tutuilamides A-C show potent elastase inhibitory activity together with moderate potency in H-460 lung cancer cell cytotoxicity assays. The binding mode to elastase was analyzed by X-ray crystallography revealing a reversible binding mode similar to the natural product lyngbyastatin 7. The presence of an additional hydrogen bond with the amino acid backbone of the flexible side chain of tutuilamide A, compared to lyngbyastatin 7, facilitates its stabilization in the elastase binding pocket and possibly explains its enhanced inhibitory potency.

Natural product screening programs have uncovered molecules from diverse natural sources with various biological activities and unique structures. However, much is yet underexplored and additional information is hidden in these exceptional collections. We applied untargeted mass spectrometry approaches to capture the chemical space and dispersal patterns of metabolites from an in-house library of marine cyanobacterial and algal collections. Remarkably, 86% of the metabolomics signals detected were not found in other available datasets of similar nature, supporting the hypothesis that marine cyanobacteria and algae possess distinctive metabolomes. The data were plotted onto a world map representing eight major sampling sites, and revealed potential geographic locations with high chemical diversity. We demonstrate the use of these inventories as a tool to explore the diversity and distribution of natural products. Finally, we utilized this tool to guide the isolation of a new cyclic lipopeptide, yuvalamide A, from a marine cyanobacterium.

An innovative approach was developed for the discovery of new natural products by combining mass spectrometric metabolic profiling with genomic analysis and resulted in the discovery of the columbamides, a new class of di- and trichlorinated acyl amides with cannabinomimetic activity. Three species of cultured marine cyanobacteria, Moorea producens 3L, Moorea producens JHB, and Moorea bouillonii PNG, were subjected to genome sequencing and analysis for their recognizable biosynthetic pathways, and this information was then compared with their respective metabolomes as detected by MS profiling. By genome analysis, a presumed regulatory domain was identified upstream of several previously described biosynthetic gene clusters in two of these cyanobacteria, M. producens 3L and M. producens JHB. A similar regulatory domain was identified in the M. bouillonii PNG genome, and a corresponding downstream biosynthetic gene cluster was located and carefully analyzed. Subsequently, MS-based molecular networking identified a series of candidate products, and these were isolated and their structures rigorously established. On the basis of their distinctive acyl amide structure, the most prevalent metabolite was evaluated for cannabinomimetic properties and found to be moderate affinity ligands for CB1.

This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS2-based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A-I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing "atomic sort" method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.

Various algorithms comparing 2D NMR spectra have been explored for their ability to dereplicate natural products as well as determine molecular structures. However, spectroscopic artefacts, solvent effects, and the interactive effect of functional group(s) on chemical shifts combine to hinder their effectiveness. Here, we leveraged Non-Uniform Sampling (NUS) 2D NMR techniques and deep Convolutional Neural Networks (CNNs) to create a tool, SMART, that can assist in natural products discovery efforts. First, an NUS heteronuclear single quantum coherence (HSQC) NMR pulse sequence was adapted to a state-of-the-art nuclear magnetic resonance (NMR) instrument, and data reconstruction methods were optimized, and second, a deep CNN with contrastive loss was trained on a database containing over 2,054 HSQC spectra as the training set. To demonstrate the utility of SMART, several newly isolated compounds were automatically located with their known analogues in the embedded clustering space, thereby streamlining the discovery pipeline for new natural products.

Natural product screening programs have uncovered molecules from diverse natural sources with various biological activities and unique structures. However, much is yet underexplored and additional information is hidden in these exceptional collections. We applied untargeted mass spectrometry approaches to capture the chemical space and dispersal patterns of metabolites from an in-house library of marine cyanobacterial and algal collections. Remarkably, 86% of the metabolomics signals detected were not found in other available datasets of similar nature, supporting the hypothesis that marine cyanobacteria and algae possess distinctive metabolomes. The data were plotted onto a world map representing eight major sampling sites, and revealed potential geographic locations with high chemical diversity. We demonstrate the use of these inventories as a tool to explore the diversity and distribution of natural products. Finally, we utilized this tool to guide the isolation of a new cyclic lipopeptide, yuvalamide A, from a marine cyanobacterium.