Decoding the Chemical Universe of Plants

Plants are an incredibly rich source of pharmaceutically relevant specialized metabolites (small molecules). However, deciphering the biochemistry that occurs in non-model plant species is a highly challenging task. My lab is developing generalized computational workflows for connecting biosynthetic gene sequences (obtained using RNA sequencing) to their downstream metabolites (detected using mass spectrometry). To this end, we have developed EnzymeExplorer, a targeted machine learning pipeline for predicting the enzymatic functions of terpene synthases directly from their amino acid sequences and DreaMS, a self-supervised foundation machine learning model for tandem mass spectrometry, which outperforms state-of-the-art methods in a range of different prediction tasks. We are further building on these foundations towards the final goal: a full computational characterization of the chemodiversity and biosynthetic potential of each plant species using easy-to-obtain experimental datasets.