Informatic strategies for the discovery and characterization of peptidic natural products

Abstract

Microbial natural products have served a key role in the development of clinically relevant drugs. Despite significant interest, traditional strategies in their characterization have lead to diminishing returns, leaving this field stagnant. Recently developed technologies such as low-cost, high-throughput genome sequencing and high-resolution mass spectrometry allow for a much richer experimental strategy, allowing us to gather data at an unprecedented scale. Naive efforts in analyzing genomic data have already revealed the wealth of natural products encoded within diverse bacterial phylogenies. Herein, I leverage these technologies through the development of specialized computational platforms cognizant of existing natural products and their biosynthesis in order to reinvigorate our drug discovery protocols.

As a first, I present a strategy for the targeted isolation of novel and structurally divergent ribosomally synthesized and post-translationally modified peptides (RiPPs). Specifically, this software platform is able to directly compare genomically encoded RiPPs to previously characterized chemical scaffolds, allowing for the identification of bacterial strains producing these specialized, and previously unstudied metabolites. Further, using metabolomics data, I have developed a strategy that facilitates direct identification and targeted isolation of these uncharacterized RiPPs. Through these set of tools, we were able to successfully isolate a structurally unique lasso peptide from a previously unexplored \textit{Streptomyces} isolate.

With the technological rise of genomic sequencing, it is now possible to survey polymicrobial environments with remarkable detail. Through the use of metagenomics, we can survey the presence and abundances of bacteria, and further metatranscriptomics is able to reveal the expression of their biosynthetic pathways. Here, I developed a platform which is able to identify microbial peptides exclusively found within the human microbiome, and further characterize their putative antimicrobial properties. Through this endeavour, we identified a bacterially encoded peptide that can effectively protect against pathogenic \textit{Clostridium difficile} infections.

With the wealth of publicly available multi-omics datasets, these works in conjunction demonstrate the potential of informatics strategies in the advancement of natural product discovery.