Mutation of F417 but not of L418 or L420 in the lipid binding domain decreases the activity of triacylglycerol hydrolase
Bibliographic record
Abstract
Human triacylglycerol hydrolase (hTGH) has been shown to play a role in hepatic lipid metabolism. Triacylglycerol hydrolase (TGH) hydrolyzes insoluble carboxylic esters at lipid/water interfaces, although the mechanism by which the enzyme adsorbs to lipid droplets is unclear. Three-dimensional modeling of hTGH predicts that catalytic residues are adjacent to an alpha-helix that may mediate TGH/lipid interaction. The helix contains a putative neutral lipid binding domain consisting of the octapeptide FLDLIADV (amino acid residues 417-424) with the consensus sequence FLXLXXXn (where n is a nonpolar residue and X is any amino acid except proline) identified in several other proteins that bind or metabolize neutral lipids. Deletion of this alpha-helix abolished the lipolytic activity of hTGH. Replacement of F417 with alanine reduced activity by 40% toward both insoluble and soluble esters, whereas replacement of L418 and L420 with alanine did not. Another potential mechanism of increasing TGH affinity for lipid is via reversible acylation. Molecular modeling predicts that C390 is available for covalent acylation. However, neither chemical modification of C390 nor mutation to alanine affected activity. Our findings indicate that F417 but not L418, L420, or C390 participates in substrate hydrolysis by hTGH.
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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 | 0.001 | 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".