Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing for NP structures. An ideal semantic ontology for the classification of NPs should go beyond the simple presence/absence of chemical substructures, but also include the taxonomy of the producing organism, the nature of the biosynthetic pathway, and/or their biological properties. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs, and especially so when analyzing large numbers of NPs. Here, we introduce NPClassifier, a deep-learning tool for the automated structural classification of NPs from their counted Morgan fingerprints. NPClassifier is expected to accelerate and enhance NP discovery by linking NP structures to their underlying properties.

Significance: Natural products research seems to be at a critical juncture in terms of its relevance to modern biological science. We have evaluated this landscape of chemical diversity to ask key questions, including the following. How has the rate of discovery of new natural products progressed over the past 70 y? Has natural product structural novelty changed as a function of time? Has the rate of novel discovery declined in recent years? Does exploring novel taxonomic space afford an advantage in terms of novel compound discovery? Is it possible to estimate how close we are to describing all of the chemical space covered by natural products? And, finally, is there still value in exploring natural products space for novel biologically active natural products?

Significance: As global CO ₂ levels rise and fossil fuel abundance decreases, the development of alternative fuels becomes increasingly imperative. Biologically derived fuels, and specifically those from microalgae, are promising sources, but improvements throughout the production process are required to reduce cost. Increasing lipid yields in microalgae without compromising growth has great potential to improve economic feasibility. We report that disrupting lipid catabolism is a practical approach to increase lipid yields in microalgae without affecting growth or biomass. We developed transgenic strains through targeted metabolic engineering that show increased lipid accumulation, biomass, and lipid yields. The target enzyme’s ubiquity suggests that this approach can be applied broadly to improve the economic feasibility of algal biofuels in other groups of microalgae.

The filamentous cyanobacterial genus Moorea gen. nov., described here under the provisions of the International Code of Botanical Nomenclature, is a cosmopolitan pan-tropical group abundant in the marine benthos. Members of the genus Moorea are photosynthetic (containing phycocyanin, phycoerythrin, allophycocyanin and chlorophyll a), but non-diazotrophic (lack heterocysts and nitrogenase reductase genes). The cells (discoid and 25–80 µm wide) are arranged in long filaments (<10 cm in length) and often form extensive mats or blooms in shallow water. The cells are surrounded by thick polysaccharide sheaths covered by a rich diversity of heterotrophic micro-organisms. A distinctive character of this genus is its extraordinarily rich production of bioactive secondary metabolites. This is matched by genomes rich in polyketide synthase and non-ribosomal peptide synthetase biosynthetic genes which are dedicated to secondary metabolism. The encoded natural products are sometimes responsible for harmful algae blooms and, due to morphological resemblance to the genus Lyngbya , this group has often been incorrectly cited in the literature. We here describe two species of the genus Moorea: Moorea producens sp. nov. (type species of the genus) with 3L^T as the nomenclature type, and Moorea bouillonii comb. nov. with PNG5-198^R as the nomenclature type.