Structure-enabled enzyme function prediction unveils elusive terpenoid biosynthesis in archaea
Samusevich, R.; Hebra, T.; Bushuiev, R.; Engst, M.; Kulhanek, J.; Bushuiev, A.; Smith, J. D.; Calounova, T.; Smrckova, H.; Molineris, M.; Schwartz, R.; Tajovska, A.; Perkovic, M.; Chatpatanasiri, R.; Kampranis, S. C.; Major, D. T.; Sivic, J.; Pluskal, T.
Show abstract
The exponential growth of uncharacterized enzyme sequences in genomic repositories demands novel tools for functional annotation. Here, we combined alignment-driven structural domain analysis with protein language models to create EnzymeExplorer, a machine-learning pipeline for enzyme function prediction. We applied this approach to terpene synthases, which present an ideal model case because they catalyze complex carbocationic rearrangements whose product outcomes cannot be predicted from active site residues alone. We detected new structural domains and achieved state-of-the-art performance on function prediction. By analyzing the UniRef90 database, we identified terpene synthases overlooked by current computational methods. Remarkably, we uncovered and experimentally validated the widespread biosynthesis of terpenoids in archaea. Our approach offers a powerful framework for characterizing enzyme "dark matter" in the rapidly expanding genomic and metagenomic datasets.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.