Arachidonoyl substrate specificity of diacylglycerol kinases
Bibliographic record
Abstract
Diacylglycerol kinases (DGKs) are an important class of lipid signaling enzymes. DGKε is unique among DGK isoforms in having specificity for DAG substrates with an arachidonate moiety. This contributes to the enrichment of lipid intermediates in the PI‐cycle with arachidonate. The importance of DGKε in neuronal function has been demonstrated in studies with knockout mice. We have shown that removal of 58 residues from the amino terminus of this protein, including the membrane‐inserting segment, had a negligible effect on substrate specificity. It is intriguing that the domain, responsible for DGKε specificity for DAG substrates with an arachidonate moiety, still is unknown. We have now identified a region of DGKε containing four conserved residues, very similar to those found responsible for recognition of arachidonic acid in lipoxygenases. We demonstrated that several mutations within this region of DGKε significantly impact enzyme activity, decreasing it to less than 2% of the activity of wild‐type DGKε. Moreover, the mutants showed a lower ratio of the enzyme activity with a DAG substrate containing an arachidonate moiety to substrates without an arachidonate moiety. The relationship of this finding to the substrate specificity of other isoforms of DGK is also explored. (Supported by the Natural Sciences and Engineering Research Council of Canada, grant 9848)
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".