Discovery and characterization of synthetic bioinformatic natural product anticancer agents

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Many of our most important therapeutics were inspired by bacterial small molecules (natural products, NPs). Although microbial NPs display a wide range of bioactivities, they have offered their greatest utility as anticancer agents and antibiotics. The incredible success of NPs as lead structures for therapeutic development is thought to be due to their unique structural and mode of action refinement from eons of evolutionary selective pressures. Since many drug discovery programs deprioritized NPs due to unacceptably high rediscovery rates, bioinformatic analyses of genomic sequence data, whether from cultured bacteria or metagenomes, has revealed that the biosynthetic diversity accessed by traditional monoculture fermentation studies represents only a small fraction of the NPs that are actually encoded by the global microbiome. Unlocking the metabolites encoded by this large fraction of previously inaccessible biosynthetic gene clusters (BGCs) should provide structurally and mechanistically novel molecules that can serve as inspirations for new anticancer agents. Traditional NPs discovery methods rely on biological processes (i.e., transcription, translation and enzymes) to convert genetic instructions contained in bacterial genomes into novel bioactive small molecules. Unfortunately, with these methods it has not been possible to coax laboratory grown bacteria into producing all the different NPs they are capable of making. We have therefore developed a “biology free” discovery approach where, instead of decoding genetic instructions using biological processes, bioinformatic algorithms are used to predict the chemical structures produced by bacteria and then chemical synthesis is used to build these structures, which we have called Synthetic Bioinformatic NPs (syn-BNPs). This proposal is designed to bring together advanced bioinformatics, total chemical synthesis, and next-generation metagenomic methods to identify syn-BNP antiproliferative agents that are inspired by BGCs which, until now, have remained hidden in the genomes of cultured bacteria and metagenomes. Interestingly, nearly half of all drugs in clinical use today are inspired by nonribosomal peptides (NRPs) or mixed polyketide-NRPs. Fortuitously, NRP biosynthesis is unique in that bioinformatic algorithms have developed to the point where it is possible to predict many NRP structures from primary data sequence alone. Concurrently with these bioinformatic advances, robust methods for synthetically producing NRP-like structure have become simple and economical, making uncharacterized NRP BGCs model targets for syn-BNP discovery studies and a potentially rich source of mechanistically diverse and novel antiproliferative agents. With this in mind, in Aim 1 bioinformatic analysis of NRP BGCs found in publicly available data bases will be used to inspire syn-BNPs that will be screened for differential antiproliferative activity across a panel of diverse cancer lines. In Aim 2, metagenomic BGCs will be sequenced and used to inspire additional syn-BNPs for antiproliferative activity screening. In Aim 3, antiproliferative syn-BNP hits will be mechanistically studied and synthetically optimized to ready them for future more detailed in vitro and in vivo studies.