Tissue transglutaminase acylation: Proposed role of conserved active site Tyr and Trp residues revealed by molecular modeling of peptide substrate binding
Bibliographic record
Abstract
Transglutaminases (TGases) catalyze the cross-linking of peptides and proteins by the formation of gamma-glutamyl-epsilon-lysyl bonds. Given the implication of tissue TGase in various physiological disorders, development of specific tissue TGase inhibitors is of current interest. To aid in the design of peptide-based inhibitors, a better understanding of the mode of binding of model peptide substrates to the enzyme is required. Using a combined kinetic/molecular modeling approach, we have generated a model for the binding of small acyl-donor peptide substrates to tissue TGase from red sea bream. Kinetic analysis of various N-terminally derivatized Gln-Xaa peptides has demonstrated that many CBz-Gln-Xaa peptides are typical in vitro substrates with K(M) values between 1.9 mM and 9.4 mM, whereas Boc-Gln-Gly is not a substrate, demonstrating the importance of the CBz group for recognition. Our binding model of CBz-Gln-Gly on tissue TGase has allowed us to propose the following steps in the acylation of tissue TGase. First, the active site is opened by displacement of conserved W329. Second, the substrate Gln side chain enters the active site and is stabilized by hydrophobic interaction with conserved residue W236. Third, a hydrogen bond network is formed between the substrate Gln side chain and conserved residues Y515 and the acid-base catalyst H332 that helps to orient and activate the gamma-carboxamide group for nucleophilic attack by the catalytic sulphur atom. Finally, an H-bond with Y515 stabilizes the oxyanion formed during the reaction.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".