From pockets to products: a data-driven framework for classification of monoterpene synthases

Monoterpene synthases (mTSs) are a large family of enzymes, which have promising industrial applications, yet remain difficult to engineer due to complex and poorly understood sequence-function relationships. Here, we present a structure-based machine learning (ML) framework that accurately predicts whether a mTS produces linear or cyclic monoterpene products. Our approach identifies active site properties, which are used as ML features, enabling functional predictions that go beyond sequence alone. As residue positioning is important for monoterpene synthesis, we created an algorithm to identify structurally conserved residues in the active sites of mTSs, identifying new and existing motifs essential for both general catalysis and specific cyclization steps. This integrated workflow thus provides insights into terpene synthases that were previously inaccessible and offers a generalizable strategy for probing and engineering other poorly understood enzyme families. Future work could see this approach used to guide the rational design of mTSs and other hard-to-engineer enzyme families.