Ó³»­´«Ã½

Microorganisms produce a wealth of specialized metabolites, which play important roles in
microbiome ecology and provide a rich resource for natural product drug discovery. Genome
sequence data has revealed that only a tiny fraction of the chemical diversity of these natural
products has been unearthed. Here, I will highlight recent work performed in my research
group and with the wider community on developing and applying computational and artificial
intelligence approaches to systematically map the biosynthetic diversity and elucidate the
functionality of these microbial specialized metabolites. Specifically, I will highlight new
methods to chart biosynthetic diversity, and to predict chemical (sub)structures and functions
of metabolites from omics data. Furthermore, I will highlight approaches used to prioritize
biosynthetic space for those genes that are most likely to mediate key microbiome-associated
phenotypes of interest, with examples from human as well as plant microbiomes. All in all,
these computational approaches are facilitating smarter and more targeted automated
genome mining of microbial metabolites to uncover the hidden chemistry of life and elucidate
its roles in microbe-microbe and host-microbe interactions.