Orthology‐based analysis helps map evolutionary diversification and predict substrate class use of <scp>BAHD</scp> acyltransferases
Bibliographic record
Abstract
Large enzyme families catalyze metabolic diversification by virtue of their ability to use diverse chemical scaffolds. How enzyme families attain such functional diversity is not clear. Furthermore, duplication and promiscuity in such enzyme families limits their functional prediction, which has produced a burgeoning set of incompletely annotated genes in plant genomes. Here, we address these challenges using BAHD acyltransferases as a model. This fast-evolving family expanded drastically in land plants, increasing from one to five copies in algae to approximately 100 copies in diploid angiosperm genomes. Compilation of >160 published activities helped visualize the chemical space occupied by this family and define eight different classes based on structural similarities between acceptor substrates. Using orthologous groups (OGs) across 52 sequenced plant genomes, we developed a method to predict BAHD acceptor substrate class utilization as well as origins of individual BAHD OGs in plant evolution. This method was validated using six novel and 28 previously characterized enzymes and helped improve putative substrate class predictions for BAHDs in the tomato genome. Our results also revealed that while cuticular wax and lignin biosynthetic activities were more ancient, anthocyanin acylation activity was fixed in BAHDs later near the origin of angiosperms. The OG-based analysis enabled identification of signature motifs in anthocyanin-acylating BAHDs, whose importance was validated via molecular dynamic simulations, site-directed mutagenesis and kinetic assays. Our results not only describe how BAHDs contributed to evolution of multiple chemical phenotypes in the plant world but also propose a biocuration-enabled approach for improved functional annotation of plant enzyme families.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".