γ-Glutamyl transpeptidase acylation with peptidic substrates: free energy relationships measured by an HPLC kinetic assay
Bibliographic record
Abstract
gamma-Glutamyl transpeptidase (GGT, EC 2.3.2.2) is a highly glycosylated heterodimeric enzyme linked to the external cellular membrane that catalyzes the hydrolysis of glutathione as well as the transfer of its gamma-glutamyl group to amino acids and dipeptides in a transpeptidation reaction. The measurement of both the hydrolytic and transpeptidation activity of this important enzyme is a challenge, since its native substrates are not highly chromogenic. We have developed an HPLC-based method for the quantitative photometric detection of numerous enzyme substrates and products, after their pre-column derivation with dabsyl chloride. The broad applicability of this method was demonstrated in the kinetic investigation of transpeptidation reactions of rat kidney GGT with glutathione, its native substrate, as well as a series of pertinent glutathione analogues. The pH-rate profile constructed for glutathione confirmed the dependence on the ionisation state of at least two residues. Analysis of the free-energy relationships in the series of synthetic peptidic substrate analogues revealed the importance of enzyme-substrate interactions unrelated to amine leaving group basicity during the acylation step. These results are further interpreted in the context of the recently published structure for a similar GGT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".