Removal of Metal from Carboxypeptidase A Proceeds via a Split Pathway: Implications for the General Mechanisms of Metalloenzyme Inactivation by Chelating Agents
Bibliographic record
Abstract
The chelation of protein-bound metal ions is typically thought to follow either a dissociative (D) or an associative (A) path. While the former mechanism involves the spontaneous dissociation of the metal from the protein prior to chelation, the latter route is characterized by the formation of an intermediate protein–metal–chelator ternary complex. Using the prototypical zinc protease carboxypeptidase A (CPA) and a variety of charged and neutral chelating agents, we demonstrate that inactivation of the enzyme (and likely other metalloproteins) proceeds through a split pathway with contributions from both D- and A-type mechanisms. In the case of charged chelators such as ethylenediaminetetraacetic acid (EDTA), the proportions of both paths could be tuned over a wide range through variation of the chelator concentration and the ionic strength, I (from ∼95% A type at low I values to ∼5% at high I values). By measuring the EDTA concentration and time dependence of CPA inactivation and fitting the obtained kinetic data to a modified time-dependent inhibition model, we obtained the rate constants for the A and D paths ( k inact and k off, respectively) and the inhibition constant ( K I ) for the formation of the CPA–Zn 2+ –EDTA ternary complex, indicating that the decreased contribution of the A-type mechanism at high ionic strengths originates from a large (40-fold; at I = 0.5 M) increase in K I . This observation might be related to a triarginine motif in CPA that electrostatically steers negatively charged substrates into the active site and may therefore also guide carboxylate-bearing chelators toward the Zn 2+ ion.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".