A roadmap for natural product discovery based on large-scale genomics and metabolomics

A global bioinformatic classification of >11,000 biosynthetic gene clusters from >800 bacterial genomes and cross-correlation with metabolomics data from nearly 200 strains sets the stage for targeted natural product discovery. Actinobacteria encode a wealth of natural product biosynthetic gene clusters, whose systematic study is complicated by numerous repetitive motifs. By combining several metrics, we developed a method for the global classification of these gene clusters into families (GCFs) and analyzed the biosynthetic capacity of Actinobacteria in 830 genome sequences, including 344 obtained for this project. The GCF network, comprising 11,422 gene clusters grouped into 4,122 GCFs, was validated in hundreds of strains by correlating confident mass spectrometric detection of known small molecules with the presence or absence of their established biosynthetic gene clusters. The method also linked previously unassigned GCFs to known natural products, an approach that will enable de novo, bioassay-free discovery of new natural products using large data sets. Extrapolation from the 830-genome data set reveals that Actinobacteria encode hundreds of thousands of future drug leads, and the strong correlation between phylogeny and GCFs frames a roadmap to efficiently access them.