Expanding the biocatalytic and oxidative landscape of the old yellow enzyme family
Bibliographic record
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 proven biocatalytic applications, the OYE family remains relatively underexplored: ~0.1% of identified members have been experimentally characterized. Here, integrated bioinformatics and synthetic biology techniques were employed to systematically organize and screen the natural diversity of the OYE family. Using protein similarity networks, the known and unknown regions of >115,000 members of the OYE family were broadly explored to identify phylogenetic and sequence-based trends. From this analysis, 118 diverse and novel enzymes were characterized across the family to greatly expand the biocatalytic diversity of known OYEs. In particular, widespread reverse, oxidative chemistry was discovered among OYE family members at ambient conditions. Individually, 14 potential biocatalysts were identified exhibiting enhanced catalytic activity or altered stereospecificity when compared to previously characterized OYEs. Two of these enzymes were crystallized to better understand their unique activity, revealing 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 hydrogenation and oxidation. This multidisciplinary strategy could be adapted to comprehensively characterize the biocatalytic potential of other enzyme families that have yet to be explored.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".