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Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.Advances in computational omics technologies are enabling access to the hidden diversity of natural products, and artificial intelligence approaches are facilitating key steps in harnessing the therapeutic potential of such compounds, including biological activity prediction. This article discusses synergies between these fields to effectively identify drug candidates from the plethora of molecules produced by nature, and how to address the challenges in realizing the potential of these synergies.

Genome mining has become a key technology to exploit natural product diversity. Although initially performed on a single-genome basis, the process is now being scaled up to mine entire genera, strain collections and microbiomes. However, no bioinformatic framework is currently available for effectively analyzing datasets of this size and complexity. In the present study, a streamlined computational workflow is provided, consisting of two new software tools: the ‘biosynthetic gene similarity clustering and prospecting engine’ (BiG-SCAPE), which facilitates fast and interactive sequence similarity network analysis of biosynthetic gene clusters and gene cluster families; and the ‘core analysis of syntenic orthologues to prioritize natural product gene clusters’ (CORASON), which elucidates phylogenetic relationships within and across these families. BiG-SCAPE is validated by correlating its output to metabolomic data across 363 actinobacterial strains and the discovery potential of CORASON is demonstrated by comprehensively mapping biosynthetic diversity across a range of detoxin/rimosamide-related gene cluster families, culminating in the characterization of seven detoxin analogues. Two bioinformatic tools, BiG-SCAPE and CORASON, enable sequence similarity network and phylogenetic analysis of gene clusters and their families across hundreds of strains and in large datasets, leading to the discovery of new natural products.

All known triterpenes are generated by triterpene synthases (TrTSs) from squalene or oxidosqualene 1 . This approach is fundamentally different from the biosynthesis of short-chain (C 10 –C 25 ) terpenes that are formed from polyisoprenyl diphosphates 2 – 4 . In this study, two fungal chimeric class I TrTSs, Talaromyces verruculosus talaropentaene synthase (TvTS) and Macrophomina phaseolina macrophomene synthase (MpMS), were characterized. Both enzymes use dimethylallyl diphosphate and isopentenyl diphosphate or hexaprenyl diphosphate as substrates, representing the first examples, to our knowledge, of non-squalene-dependent triterpene biosynthesis. The cyclization mechanisms of TvTS and MpMS and the absolute configurations of their products were investigated in isotopic labelling experiments. Structural analyses of the terpene cyclase domain of TvTS and full-length MpMS provide detailed insights into their catalytic mechanisms. An AlphaFold2-based screening platform was developed to mine a third TrTS, Colletotrichum gloeosporioides colleterpenol synthase (CgCS). Our findings identify a new enzymatic mechanism for the biosynthesis of triterpenes and enhance understanding of terpene biosynthesis in nature. Chimeric triterpene synthases are identified that catalyse non-squalene-dependent triterpene biosynthesis.

Natural products have traditionally been rich sources for drug discovery. In order to clear the road toward the discovery of unknown natural products, biologists need dereplication strategies that identify known ones. Here we report DEREPLICATOR+, an algorithm that improves on the previous approaches for identifying peptidic natural products, and extends them for identification of polyketides, terpenes, benzenoids, alkaloids, flavonoids, and other classes of natural products. We show that DEREPLICATOR+ can search all spectra in the recently launched Global Natural Products Social molecular network and identify an order of magnitude more natural products than previous dereplication efforts. We further demonstrate that DEREPLICATOR+ enables cross-validation of genome-mining and peptidogenomics/glycogenomics results. New natural products can be identified via mass spectrometry by excluding all known ones from the analysis, a process called dereplication. Here, the authors extend a previously published dereplication algorithm to different classes of secondary metabolites.

RODEO, an algorithm developed for RiPP natural product discovery, was applied to map out the gene clusters that encode and tailor lasso peptides and led to the identification and characterization of several new lasso peptide topologies. Ribosomally synthesized and post-translationally modified peptide (RiPP) natural products are attractive for genome-driven discovery and re-engineering, but limitations in bioinformatic methods and exponentially increasing genomic data make large-scale mining of RiPP data difficult. We report RODEO (Rapid ORF Description and Evaluation Online), which combines hidden-Markov-model-based analysis, heuristic scoring, and machine learning to identify biosynthetic gene clusters and predict RiPP precursor peptides. We initially focused on lasso peptides, which display intriguing physicochemical properties and bioactivities, but their hypervariability renders them challenging prospects for automated mining. Our approach yielded the most comprehensive mapping to date of lasso peptide space, revealing >1,300 compounds. We characterized the structures and bioactivities of six lasso peptides, prioritized based on predicted structural novelty, including one with an unprecedented handcuff-like topology and another with a citrulline modification exceptionally rare among bacteria. These combined insights significantly expand the knowledge of lasso peptides and, more broadly, provide a framework for future genome-mining efforts.

Novel antibiotics are urgently needed to address the looming global crisis of antibiotic resistance. Historically, the primary source of clinically used antibiotics has been microbial secondary metabolism. Microbial genome sequencing has revealed a plethora of uncharacterized natural antibiotics that remain to be discovered. However, the isolation of these molecules is hindered by the challenge of linking sequence information to the chemical structures of the encoded molecules. Here, we present PRISM 4, a comprehensive platform for prediction of the chemical structures of genomically encoded antibiotics, including all classes of bacterial antibiotics currently in clinical use. The accuracy of chemical structure prediction enables the development of machine-learning methods to predict the likely biological activity of encoded molecules. We apply PRISM 4 to chart secondary metabolite biosynthesis in a collection of over 10,000 bacterial genomes from both cultured isolates and metagenomic datasets, revealing thousands of encoded antibiotics. PRISM 4 is freely available as an interactive web application at http://prism.adapsyn.com . Large-scale sequencing efforts have uncovered a large number of secondary metabolic pathways, but the chemicals they synthesise remain unknown. Here the authors present PRISM 4, which predicts the chemical structures encoded by microbial genome sequences, including all classes of bacterial antibiotics in clinical use.

As sessile organisms, plants evolved elaborate metabolic systems that produce a plethora of specialized metabolites as a means to survive challenging terrestrial environments. Decades of research have revealed the genetic and biochemical basis for a multitude of plant specialized metabolic pathways. Nevertheless, knowledge is still limited concerning the selective advantages provided by individual and collective specialized metabolites to the reproductive success of diverse host plants. Here we review the biological functions conferred by various classes of plant specialized metabolites in the context of the interaction of plants with their surrounding environment. To achieve optimal multifunctionality of diverse specialized metabolic processes, plants use various adaptive mechanisms at subcellular, cellular, tissue, organ and interspecies levels. Understanding these mechanisms and the evolutionary trajectories underlying their occurrence in nature will ultimately enable efficient bioengineering of desirable metabolic traits in chassis organisms. Decades of research have identified the biochemical basis of many plant specialized metabolic pathways. This Review highlights the biological context of these pathways and how recent advances have extended the new frontiers of phytochemistry.

Antimicrobial resistance seriously threatened human health, and new antimicrobial agents are desperately needed. As one of the largest classes of plant secondary metabolite, flavonoids can be widely found in various parts of the plant, and their antibacterial activities have been increasingly paid attention to. Based on the physicochemical parameters and antibacterial activities of sixty-six flavonoids reported, two regression equations between their ACD/LogP or LogD 7.40 and their minimum inhibitory concentrations (MICs) to gram-positive bacteria were established with the correlation coefficients above 0.93, and then were verified by another sixty-eight flavonoids reported. From these two equations, the MICs of most flavonoids against gram-positive bacteria could be roughly calculated from their ACD/LogP or LogD 7.40 , and the minimum MIC was predicted as approximately 10.2 or 4.8 μM, more likely falls into the range from 2.6 to 10.2 μM, or from 1.2 to 4.8 μM. Simultaneously, both tendentiously concave regression curves indicated that the lipophilicity is a key factor for flavonoids against gram-positive bacteria. Combined with the literature analyses, the results also suggested that the cell membrane is the main site of flavonoids acting on gram-positive bacteria, and which likely involves the damage of phospholipid bilayers, the inhibition of the respiratory chain or the ATP synthesis, or some others.

Substrate inhibition of enzymes can be a major obstacle to the production of valuable chemicals in engineered microorganisms. Here, we show substrate inhibition of lycopene cyclase as the main limitation in carotenoid biosynthesis in Yarrowia lipolytica . To overcome this bottleneck, we exploit two independent approaches. Structure-guided protein engineering yields a variant, Y27R, characterized by complete loss of substrate inhibition without reduction of enzymatic activity. Alternatively, establishing a geranylgeranyl pyrophosphate synthase-mediated flux flow restrictor also prevents the onset of substrate inhibition by diverting metabolic flux away from the inhibitory metabolite while maintaining sufficient flux towards product formation. Both approaches result in high levels of near-exclusive β-carotene production. Ultimately, we construct strains capable of producing 39.5 g/L β-carotene at a productivity of 0.165 g/L/h in bioreactor fermentations (a 1441-fold improvement over the initial strain). Our findings provide effective approaches for removing substrate inhibition in engineering pathways for efficient synthesis of natural products. Substrate inhibition has not been widely studied in the context of synthetic biology and metabolic engineering. Here, the authors report removal of lycopene substrate inhibition by two different strategies and enable high carotenoid productivity in Yarrowia lipolytica .

Editorial Summary Most microbial biosynthetic gene clusters are inactive under laboratory culture conditions. A CRISPR–Cas9 genome-editing approach in Streptomyces species enables the targeted activation of silent gene clusters and production of encoded natural products. Here we report an efficient CRISPR–Cas9 knock-in strategy to activate silent biosynthetic gene clusters (BGCs) in streptomycetes. We applied this one-step strategy to activate multiple BGCs of different classes in five Streptomyces species and triggered the production of unique metabolites, including a novel pentangular type II polyketide in Streptomyces viridochromogenes . This potentially scalable strategy complements existing activation approaches and facilitates discovery efforts to uncover new compounds with interesting bioactivities.