Identification of a common motif for the recognition of moieties containing polyunsaturated fatty acids
Bibliographic record
Abstract
The pattern of residues L/I‐X (2–4) ‐R‐X (2) ‐L‐X (3–4) ‐G, in which ‐X (n) ‐ represents n residues of any amino acids, is found in several enzymes acting on polyunsaturated fatty acids. For enzymes that do exhibit preference for polyunsaturated fatty acid‐containing lipids, generally only one isoform has this specificity and it is also the only isoform with this motif. One example we study is diacylglycerol kinase epsilon (DGKε). It is the only one of the 10 mammalian isoforms of DGK that exhibits arachidonoyl specificity and is the only isoform with the above motif. Mutations of the essential residues in this motif result in loss of arachidonoyl specificity. Furthermore, DGKα can be converted to an enzyme having this motif by substituting only one residue. When DGKα was mutated to gain the motif, the enzyme also gained some arachidonoyl specificity. This motif is also present in an isoform of phosphatidylinositol‐4‐phosphate‐5‐kinase (PIP5K) that we showed had arachidonoyl specificity for its substrate. Single residue mutations within the motif of this isoform result in loss of activity against an arachidonoyl substrate. We also demonstrate the importance of acyl chain specificity for the phosphatidic acid activation of PIP5K, and that this activation is dependent on the substrate. This is the first demonstration of a motif that endows specificity for an acyl chain.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".