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

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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).