Unlocking the chemical potential of plants: Predicting function from DNA sequence for complex enzyme superfamilies

Plants are a rich source of drugs and other useful molecules. Examples include the anticancer drugs taxol (from yew trees) and vinblastine and vincristine (from Madagascan periwinkle); the antimalaria compound artemisinin from wormwood; the sweetener stevioside from sweetleaf; and flavours and fragrances such as menthol and limonene, from mint and citrus, respectively. While the biosynthetic pathways for ~50 plant natural products have been so far characterised, plants are known to make hundreds of thousands of diverse chemicals for which the biosynthetic pathways are unknown. Based on our knowledge of the overall classes of enzymes that we associate with plant natural product biosynthesis it has become clear from studying the sequences of plant genomes that plants have the potential to encode far more chemical diversity than has previously been appreciated. However, although we can identify genes in genome that 'look guilty' because they are predicted to encode enzymes belonging to certain major enzyme classes, this does not tell us exactly what specific chemical transformations these individual enzymes carry out. This project brings expertise in plant natural product pathway discovery and elucidation together with powerful computational approaches to tackle the challenges of decoding the information hidden in plant genomes, deducing the relationship between the structure and function of large enzyme superfamilies, and understanding mechanisms of metabolic diversification in the Plant Kingdom. To achieve this, we will focus on a major class of plant natural products known as the triterpenes, since they are one of the largest and most structurally diverse classes of plant natural products with many health, agronomic and industrial applications.

Our strategy is to integrate powerful data-driven computational approaches with experimental investigation of enzyme function to understand the functions and kingdom-specific expansion of an exemplar complex enzyme superfamily - the triterpene synthases (TTSs). The TTS enzyme superfamily is an ideal test case for our purposes, since these enzymes are able to generate an enormous diversity of cyclized triterpene scaffolds from a single common precursor molecule. Through iterative cycles of computational and experimental investigations we aim to develop sophisticated predictive analytic approaches that will enable us to relate DNA sequence to enzyme function with ever-increasing power and resolution, and in so doing to generate and test hypotheses about enzyme function, mechanisms and evolution. Our aims are to: (1) experimentally determine the chemical diversity encoded by diverse members of the TTS superfamily selected based on our initial CATH-FunFam classification; (2) expand the sequence data for the CATH TTS superfamily and integrate sequence- and structure-based computational approaches to refine our strategies for identifying TTS features implicated in determination of product specificity and for functional classification, and test TTS function predictions; (3) exploit a novel machine learning approach to predict known and novel TTSs; (4) understand TTS function and diversification by determining the product specificities of natural and engineered TTS variants, guided by computational predictions from (1)-(3).