Identification of polyketide biosynthetic gene clusters that harbor self-resistance target genes

Background Polyketide secondary metabolites have been a rich source of antibiotic discovery for decades. Thousands of novel polyketide synthase (PKS) gene clusters have been identified in recent years with advances in DNA sequencing. However, experimental characterization of novel and useful PKS activities remains complicated. As a result, computational tools to analyze sequence data are essential to identify and prioritize potentially novel PKS activities. Here we exploit the concept of genetically-encoded self-resistance to identify and rank biosynthetic gene clusters for their potential to encode novel antibiotics. Results To identify PKS genes that are likely to produce an antibacterial compound, we developed an automated method to identify and catalog clusters that harbor potential self-resistance genes. We manually curated a list of known self-resistance genes and searched all NCBI genome databases for homologs of these self-resistance genes in biosynthetic gene clusters. The algorithm takes into account (1) the distance of the potential self-resistance gene to a core enzyme in the biosynthetic gene cluster; (2) the presence of a duplicated housekeeping copy of the self-resistance gene; (3) the presence of close homologs of the biosynthetic gene cluster in diverse species also harboring the putative self-resistance gene; (4) evidence for coevolution of the self-resistance gene and core biosynthetic gene; and (5) self-resistance gene ubiquity. We generated a catalog of 190 unique PKS clusters whose products likely target known enzymes of antibacterial importance. We also present an expanded set of putative self-resistance genes that may be useful in identifying small molecules active against novel microbial targets. Conclusions We developed a bioinformatic approach to identify and rank biosynthetic gene clusters that likely harbor self-resistance genes and may produce compounds with antibacterial properties. We compiled a list of putative self-resistance genes for novel antibacterial targets, and of orphan PKS clusters harboring these targets. These catalogues are a resource for discovery of novel antibiotics.