Substrate specificity and the role of a putative transmembrane segment of diacylglycerol kinase epsilon
Bibliographic record
Abstract
We have shown that the putative transmembrane domain of the epsilon isoform of diacylglycerol kinase (DGKe) is not required for enzyme activity or for substrate specificity. We transfected COS7 cells with the gene for human DGKe or with a gene for a truncated form (DGKDe), both of which had a FLAG tag at the amino terminus. The DGKDe lacks 40 N‐terminal amino acids including a segment corresponding to a hydrophobic, putative transmembrane helix (residues 19–40). Enzyme‐catalyzed rates in a mixed micellar assay, as a function of diacylglycerol or ATP substrate concentration, were analyzed by Michaelis‐Menten kinetics. The full length and the truncated enzymes were both more specific for 1‐stearoyl‐2‐arachidonoyl‐sn‐glycerol than for 1,2‐dioleoyl‐sn‐glycerol to comparable extents. The results show that the truncated form of the enzyme maintains substrate specificity for lipids with an arachidonoyl moiety present at the sn‐2 position. The truncation increases the catalytic rate constant for the two substrates. The putative transmembrane domain is unique for DGKe among the DGK isoforms and it may have a role either in protein‐protein interactions or in targeting the protein to specific domains in the membrane. In vitro studies and molecular modeling of the peptide corresponding to the putative transmembrane domain show a tendency for oligomerization of the full length protein.
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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) | 0.001 | 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".