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.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a chemically diverse class of metabolites. Many RiPPs show potent biological activities that make them attractive starting points for drug development. A promising approach for the discovery of new classes of RiPPs is genome mining. However, the accuracy of genome mining is hampered by the lack of signature genes shared across different RiPP classes. One way to reduce false-positive predictions is by complementing genomic information with metabolomics data. In recent years, several new approaches addressing such integrative genomics and metabolomics analyses have been developed. In this review, we provide a detailed discussion of RiPP-compatible software tools that integrate paired genomics and metabolomics data. We highlight current challenges in data integration and identify opportunities for further developments targeting new classes of bioactive RiPPs.

Microbial competition for trace metals shapes their communities and interactions with humans and plants. Many bacteria scavenge trace metals with metallophores, small molecules that chelate environmental metal ions and transport them back into the cell. Our incomplete knowledge of metallophores diversity stymies our ability to fight infectious diseases and harness beneficial microbiome interactions. The majority of known metallophores are non-ribosomal peptides (NRPs), which feature metal-chelating moieties rarely found in other classes of natural products. NRP metallophore production may be predicted by genome mining, where genomes are scanned for homologs of known biosynthetic gene clusters (BGCs). However, accurately detecting NRP metallophore biosynthesis currently requires expert manual inspection. Here, we introduce automated identification of NRP metallophore BGCs through a comprehensive detection algorithm, newly implemented in antiSMASH. Custom-designed profile hidden Markov models detect genes encoding the biosynthesis of most known NRP metallophore chelating moieties (2,3-dihydroxybenzoate, hydroxamates, salicylate, β-hydroxyamino acids, graminine, Dmaq, and the pyoverdine chromophore), achieving 97% precision and 78% recall against manual curation. We leveraged the algorithm, in combination with transporter gene detection, to detect NRP metallophore BGCs in 15,562 representative bacterial genomes and predict that 25% of all non-ribosomal peptide synthetases encode metallophore production. BiG-SCAPE clustering of 2,562 NRP metallophore BGCs revealed that significant diversity remains unexplored, including new combinations of chelating groups. Additionally, we find that Cyanobacteria are severely understudied and should be the focus of more metallophore isolation efforts. The inclusion of NRP metallophore detection in antiSMASH version 7 will aid non-expert researchers and facilitate large-scale investigations into metallophore biology.

A grand challenge in microbial ecology is disentangling the traits of individual populations within complex communities. Various cultivation-independent approaches have been used to infer traits based on the presence of marker genes. However, marker genes are not linked to traits with complete fidelity, nor do they capture important attributes, such as the timing of gene expression or coordination among traits. To address this, we present an approach for assessing the trait landscape of microbial communities by statistically defining a trait attribute as a shared transcriptional pattern across multiple organisms. Leveraging the KEGG pathway database as a trait library and the Enhanced Biological Phosphorus Removal (EBPR) model microbial ecosystem, we demonstrate that a majority (65%) of traits present in 10 or more genomes have niche-differentiating expression attributes. For example, while many genomes containing high-affinity phosphorus transporter pstABCS display a canonical attribute (e.g. up-regulation under phosphorus starvation), we identified another attribute shared by many genomes where transcription was highest under high phosphorus conditions. Taken together, we provide a novel framework for unravelling the functional dynamics of uncultivated microorganisms by assigning trait-attributes through genome-resolved time-series metatranscriptomics.

ABSTRACT Marine sponges and their microbial symbiotic communities are rich sources of diverse natural products (NPs) that often display biological activity, yet little is known about the global distribution of NPs and the symbionts that produce them. Since the majority of sponge symbionts remain uncultured, it is a challenge to characterize their NP biosynthetic pathways, assess their prevalence within the holobiont, and measure the diversity of NP biosynthetic gene clusters (BGCs) across sponge taxa and environments. Here, we explore the microbial biosynthetic landscapes of three high-microbial-abundance (HMA) sponges from the Atlantic Ocean and the Mediterranean Sea. This data set reveals striking novelty, with