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 techniques are well-suited to high-throughput characterization of natural products, 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.

A major goal in natural product discovery programs is to rapidly dereplicate known entities from complex biological extracts. We demonstrate here that molecular networking, an approach that organizes MS/MS data based on chemical similarity, is a powerful complement to traditional dereplication strategies. Successful dereplication with molecular networks requires MS/MS spectra of the natural product mixture along with MS/MS spectra of known standards, synthetic compounds, or well-characterized organisms, preferably organized into robust databases. This approach can accommodate different ionization platforms, enabling cross correlations of MS/MS data from ambient ionization, direct infusion, and LC-based methods. Molecular networking not only dereplicates known molecules from complex mixtures, it also captures related analogs, a challenge for many other dereplication strategies. To illustrate its utility as a dereplication tool, we apply mass spectrometry-based molecular networking to a diverse array of marine and terrestrial microbial samples, illustrating the dereplication of 58 molecules including analogs.

Novel cationic antimicrobial peptides typified by structures such as KKKKKKAAXAAWAAXAA-NH 2 , where X ‫؍‬ Phe/Trp, and several of their analogues display high activity against a variety of bacteria but exhibit no hemolytic activity even at high dose levels in mammalian erythrocytes. To elucidate their mechanism of action and source of selectivity for bacterial membranes, phospholipid mixtures mimicking the compositions of natural bacterial membranes (containing anionic lipids) and mammalian membranes (containing zwitterionic lipids ؉ cholesterol) were challenged with the peptides. We found that peptides readily inserted into bacterial lipid mixtures, although no insertion was detected in model "mammalian" membranes. The depth of peptide insertion into model bacterial membranes was estimated by Trp fluorescence quenching using doxyl groups variably positioned along the phospholipid acyl chains. Peptide antimicrobial activity generally increased with increasing depth of peptide insertion. The overall results, in conjunction with molecular modeling, support an initial electrostatic interaction step in which bacterial membranes attract and bind peptide dimers onto the bacterial surface, followed by the "sinking" of the hydrophobic core segment to a peptide sequence-dependent depth of ϳ2.5-8 Å into the membrane, largely parallel to the membrane surface. Antimicrobial activity was likely enhanced by the fact that the peptide sequences contain AXXXA sequence motifs, which promote their dimerization, and possibly higher oligomerization, as assessed by SDS-polyacrylamide gel analysis and fluorescence resonance energy transfer experiments. The high selectivity of these peptides for nonmammalian membranes, combined with their activity toward a wide spectrum of Gram-negative and Gram-positive bacteria and yeast, while retaining water solubility, represent significant advantages of this class of peptides.

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.

Cyanobacteria are major sources of oxygen, nitrogen, and carbon in nature. In addition to the importance of their primary metabolism, some cyanobacteria are prolific producers of unique and bioactive secondary metabolites. Chemical investigations of the cyanobacterial genus Moorea have resulted in the isolation of over 190 compounds in the last two decades. However, preliminary genomic analysis has suggested that genome-guided approaches can enable the discovery of novel compounds from even well-studied Moorea strains, highlighting the importance of obtaining complete genomes. We report a complete genome of a filamentous tropical marine cyanobacterium, Moorea producens PAL, which reveals that about one-fifth of its genome is devoted to production of secondary metabolites, an impressive four times the cyanobacterial average. Moreover, possession of the complete PAL genome has allowed improvement to the assembly of three other Moorea draft genomes. Comparative genomics revealed that they are remarkably similar to one another, despite their differences in geography, morphology, and secondary metabolite profiles. Gene cluster networking highlights that this genus is distinctive among cyanobacteria, not only in the number of secondary metabolite pathways but also in the content of many pathways, which are potentially distinct from all other bacterial gene clusters to date. These findings portend that future genome-guided secondary metabolite discovery and isolation efforts should be highly productive.

ABSTRACT:Novel cationic antimicrobial peptides (CAPs) designed in our lab-typified by sequences such as KKKKKKAAX-AAXAAXAA-NH 2 , where X 5 Phe/Trp-display high antibacterial activity but exhibit little or no hemolytic activity towards human red blood cells even at high doses.To clarify the mechanism of their selectivity for bacterial versus mammalian membranes and to increase our understanding of the relationships between primary sequence and bioactivity, a library of derivatives was prepared by increasing segmental hydrophobicity, in which systematic substitutions of Ala for two, three, or four Leu residues were made. Conformationally constrained dimeric and cyclic derivatives were also synthesized. The peptides were examined for activity against pathogenic bacteria (Pseudomonas aeruginosa),

Cyanotoxins obtained from a freshwater cyanobacterial collection at Green Lake, Seattle during a cyanobacterial harmful algal bloom in the summer of 2014 were studied using a new approach based on molecular networking analysis of liquid chromatography-tandem mass spectrometry (LC-MS/MS) data. This MS-networking approach is particularly well suited for the detection of new cyanotoxin variants, and resulted in the discovery of three new cyclic peptides, namely microcystin-MhtyR (6) which comprised about half of the total microcystin content in the bloom, and ferintoic acids C (12) and D (13). Structure elucidation of 6 was aided by a new microscale methylation procedure. Metagenomic analysis of the bloom using the 16S-ITS rRNA region identified Microcystis aeruginosa as the predominant cyanobacterium in the sample. Fragments of the putative biosynthetic genes for the new cyanotoxins were also identified, and their sequences correlated to the structure of the isolated cyanotoxins.

Untargeted liquid chromatography-MS (LC-MS) is used to rapidly profile crude natural product (NP) extracts; however, the quantity of data produced can become difficult to manage. Molecular networking based on MS/MS data visualizes these complex data sets to aid their initial interpretation. Here, we developed an additional visualization step for the molecular networking workflow to provide relative and absolute quantitation of a specific compound in an extract. The new visualization also facilitates combination of several metabolomes into one network, and so was applied to an MS/MS data set from 20 crude extracts of cultured marine cyanobacteria. The resultant network illustrates the high chemical diversity present among marine cyanobacteria. It is also a powerful tool for locating producers of specific metabolites. In order to dereplicate and identify culture-based sources of known compounds, we added MS/MS data from 60 pure NPs and NP analogs to the 20-strain network. This dereplicated six metabolites directly and offered structural information on up to 30 more. Most notably, our visualization technique allowed us to identify and quantitatively compare several producers of the bioactive and biosynthetically intriguing lipopeptide malyngamide C. Our most prolific producer, a Panamanian strain of Okeania hirsuta (PAB10FEB10-01), was found to produce at least 0.024mg of malyngamide C per mg biomass (2.4%, w/dw) and is now undergoing genome sequencing to access the corresponding biosynthetic machinery.