Natural products are a rich resource of bioactive compounds for valuable applications across multiple fields such as food, agriculture, and medicine. For natural product discovery, high throughput in silico screening offers a cost-effective alternative to traditional resource-heavy assay-guided exploration of structurally novel chemical space. In this data descriptor, we report a characterized database of 67,064,204 natural product-like molecules generated using a recurrent neural network trained on known natural products, demonstrating a significant 165-fold expansion in library size over the approximately 400,000 known natural products. This study highlights the potential of using deep generative models to explore novel natural product chemical space for high throughput in silico discovery.

Natural products possess significant therapeutic potential but remain underutilized despite advances in genomics and bioinformatics. While there are approaches to activate and upregulate natural product biosynthesis in both native and heterologous microbial strains, a comprehensive strategy to elicit production of natural products as well as a generalizable and efficient method to interrogate diverse native strains collection, remains lacking. Here, we explore a flexible and robust integrase-mediated multi-pronged activation approach to reliably perturb and globally trigger antibiotics production in actinobacteria. Across 54 actinobacterial strains, our approach yielded 124 distinct activator-strain combinations which consistently outperform wild type. Our approach expands accessible metabolite space by nearly two-fold and increases selected metabolite yields by up to >200-fold, enabling discovery of Gram-negative bioactivity in tetramic acid analogs. We envision these findings as a gateway towards a more streamlined, accelerated, and scalable strategy to unlock the full potential of Nature’s chemical repertoire.

Colorimetric assays are a rapid, scalable technique well suited to enzyme activity screening. However, side reactions or chromogenic reagent instability can result in false positives or false negatives that compromise the accuracy of such assays. Here, we identify three classes of compounds incompatible with the 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) colorimetric assay for galactose oxidase activity. Dark green ABTS^·+ cationic radicals indicating enzyme activity can get quenched to yield colorless solutions or couple with substrates to form differently colored adducts, thus preventing accurate colorimetric measurements. These side reactions limit the utility of the ABTS assay and introduce uncertainty in the substrate scope to which it is applicable. We have investigated the underlying mechanisms behind these side reactions to conclude that free radical scavengers, phenols with electron-donating substituents, and β,γ-unsaturated aryl ketones are incompatible with the ABTS colorimetric assay. In search of a viable alternative, we developed an assay using 2,4-dinitrophenylhydrazine under neutral conditions with isopropyl alcohol as a solubilizing agent. The use of neutral conditions was found to be critical to avoid hydrolysis of hydrazone adducts, ensuring reproducible measurements. Our assay is compatible with free radical scavengers (R² = 0.98), phenols with electron-donating substituents (R² = 0.97), and β,γ-unsaturated aryl ketones (R² = 0.88). This modified assay enables galactose oxidase activity screening across a broader substrate scope, thus facilitating enzyme use for more practical applications.

Natural products encompass a diverse range of compounds with high impact applications in consumer care, agriculture and most notably, therapeutics. However, despite the expansive chemical repertoire indicated in genomic information of microbes, only a small subset can be obtained under laboratory conditions. To increase accessible chemical space and realize Nature’s full chemical potential, a multi-pronged genetic- and cultivation-based strategy has been employed to activate and upregulate natural product biosyntheses in native and heterologous strains. This data descriptor documents a characterized collection of 2,138 liquid chromatography-tandem mass spectrometry (LC/MS-MS) spectra of fermentation extracts from 54 native actinobacterial strains collected from soil and marine environments in Singapore, and their 459 activated mutants in 3 to 5 media. A total of 743 unique metabolites have been identified, with the activated mutants demonstrating an approximately 2-fold expansion in accessible chemical space over wild type strains. Interrogating this expanded chemical diversity with cheminformatic tools can provide direction for the discovery of novel natural products with desirable functional activity.