Extreme climates may threaten crop-growing soil, limiting access to plant metabolites used for pharmaceuticals, flavors, and fragrances. Microbial cell factories could be a viable replacement by scaling up metabolite production, reducing land use and avoiding polluting extraction, but engineering them requires understanding the plant enzymes that produce these metabolites. Many such compounds are synthesized through Cytochrome P450 enzymes (CYPs), which catalyze substrate oxidation. We focus on a CYP subfamily that converts valencene to nootkatone, a popular grapefruit-flavored compound. However, only nine true positive and eight true negative valencene-catalyzing CYPs are known, limiting our understanding of which CYPs work and why. This motivates the search for additional natural valencene-catalyzing CYPs.

First, we modeled all true positives with heme and valencene. The negative set and 180,000 plant CYP candidates were modeled with heme only, superimposing valencene from their closest true positive homolog. Next, seven sequence- and structure-based embedding methods were compared across full proteins and binding site regions, using cosine similarity, embedding-based Needleman-Wunsch alignment, and optimal transport. Finally, logistic regression predicted catalysis using six features: best, top-3 mean, and median similarity to true positives and negatives for each embedding and similarity measure.

Frame2seq, a structure-guided masked language model, produced the most informative embeddings, achieving the highest three-fold cross-validation AUC closest to leave-one-out performance despite limited training data. We subjected 180,000 plant CYPs to the model, generating a ranked candidate list which we sent to collaborators for experimental validation. This will expand our dataset and deepen our understanding of valencene catalysis.

Co-authors: Sara Tolosa-Alarcón, Emre Cicekyurt, Miguel Romero-Durana, Alfonso Valencia