The Hidden Biocatalytic Potential of the Old Yellow Enzyme Family
Bibliographic record
Abstract
Abstract The rapid advancement of sequencing technology has created an immense reservoir of protein sequence-function information that has yet to be fully utilized for fundamental or biocatalytic applications. For example, ene reductases from the ‘old yellow enzyme’ (OYE) family catalyze the asymmetric hydrogenation of activated alkenes with enhanced stereoselectivity - key transformations for sustainable production of pharmaceutical and industrial synthons. Despite the proven biocatalytic application, the OYE family remains relatively underexplored with only 0.1% of identified members having any experimental characterization. Here, a platform of integrated bioinformatics and synthetic biology techniques was employed to systematically organize and screen the natural diversity of the OYE family. Using protein similarity networks, the known and unknown regions of the >115,000 members of the OYE family were broadly explored to identify phylogenetic and sequence-based trends. From this analysis, 118 novel enzymes were characterized across the family to broadly explore and expand the biocatalytic performance and substrate scope of known OYEs. Over a dozen novel enzymes were identified exhibiting enhanced catalytic activity or altered stereospecificity. Beyond well-established ene reduction, we detected widespread occurrence of oxidative chemistry amongst OYE family members at ambient conditions. Crystallography studies of selected OYEs yielded structures for two enzymes, contributing to a better understanding of their unique performance. Their structures revealed an unusual loop conformation within a novel OYE subclass. Overall, our study significantly expands the known functional and chemical diversity of OYEs while identifying superior biocatalysts for asymmetric reduction and oxidation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".