The <i>Saccharomyces cerevisiae</i> enolase‐related regions encode proteins that are active enolases
Bibliographic record
Abstract
In addition to two genes (ENO1 and ENO2) known to code for enolase (EC4.2.1.11), the Saccharomyces cerevisiae genome contains three enolase-related regions (ERR1, ERR2 and ERR3) which could potentially encode proteins with enolase function. Here, we show that products of these genes (Err2p and Err3p) have secondary and quaternary structures similar to those of yeast enolase (Eno1p). In addition, Err2p and Err3p can convert 2-phosphoglycerate to phosphoenolpyruvate, with kinetic parameters similar to those of Eno1p, suggesting that these proteins could function as enolases in vivo. To address this possibility, we overexpressed the ERR2 and ERR3 genes individually in a double-null yeast strain lacking ENO1 and ENO2, and showed that either ERR2 or ERR3 could complement the growth defect in this strain when cells are grown in medium with glucose as the carbon source. Taken together, these data suggest that the ERR genes in Saccharomyces cerevisiae encode a protein that could function in glycolysis as enolase. The presence of these enolase-related regions in Saccharomyces cerevisiae and their absence in other related yeasts suggests that these genes may play some unique role in Saccharomyces cerevisiae. Further experiments will be required to determine whether these functions are related to glycolysis or other cellular processes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| 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".