Researchers develop AI-powered system to predict functions of thousands of plant enzymes
Scientists have developed a computational framework combining a large language model database and machine learning classifiers to functionally annotate BAHD acyltransferases, a large family of plant enzymes involved in specialized metabolism. The team assembled FuncZymeDB-BAHD, a curated database of over 2,700 enzyme activities, and built two complementary prediction tools—one phylogenomics-based and one AI-based—that together annotated nearly all enzymes tested. The work addresses a major bottleneck in plant biology where genomic sequencing has far outpaced our understanding of what individual genes actually do.
A research team has created a scalable pipeline for predicting the biochemical functions of BAHD acyltransferases, a large and diverse family of plant enzymes that modify a wide range of metabolites. At the core of the framework is FuncZymeDB-BAHD, a database of 2,705 curated enzyme-acceptor-donor activity records covering 336 characterized BAHDs from 156 plant species—representing a two-to-six-fold expansion over existing resources like Swiss-Prot. The first prediction tool, FuncPred-OG, uses orthologous group mapping to link uncharacterized enzymes to well-studied relatives, successfully annotating over half of BAHDs across 85 plant proteomes, with five novel predictions subsequently validated through in vitro experiments. For enzymes that FuncPred-OG could not annotate, a second tool, FuncPred-AI, applies logistic regression classifiers trained on protein language model embeddings, achieving precision-recall scores and correct-hit rates as high as 93% and providing at least one probable functional annotation for 99.9% of the nearly 9,000 BAHDs in the dataset. The entire workflow and underlying databases have been deployed as a public web portal, which the authors estimate could reduce the time researchers spend selecting candidate enzymes for experimental study from days to minutes. The authors present the framework as a generalizable template applicable to other enzyme families beyond BAHDs.
What's missing
As a preprint posted on bioRxiv, this work has not yet undergone formal peer review, so the reported performance metrics and validation claims should be interpreted with that caveat. The study validates only five novel FuncPred-OG predictions experimentally, leaving the broader accuracy of AI-based predictions (FuncPred-AI) largely unvalidated in wet-lab settings. It is also unclear how performance generalizes to plant species or enzyme subfamilies underrepresented in the training data.
What different sources said
- bioRxivCenter
Systematic functional annotation of thousands of BAHD acyltransferases in plant genomes using Protein Language Model and phylogenomic tools
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