Macrocyclic effect of auxiliary ligand on the gas‐phase dissociation of ternary copper(II)–GGX complexes
Bibliographic record
Abstract
Abstract Previous studies into the dissociation of [Cu II (dien)peptide] . 2+ ions (dien = diethylenetriamine) have shown that NH‐containing auxiliary ligands do not favor the formation of [peptide] . + species; instead, they promote proton‐transfer reactions, especially for peptides containing basic amino residues. Formation of radical cationic tripeptides of the form GGX . + [GGX = glycylglycyl(residue X)] becomes feasible upon substituting the open‐chain tridentate ligand dien with its analogous cyclic ligand, 1,4,7‐triazacyclononane (9‐aneN 3 ); i.e., from [Cu II (9‐aneN 3 )GGX] . 2+ ions. Similar enhancements occur when using 1,4,7,10‐tetraoxacyclododecane (12‐crown‐4) in place of its open‐chain analog, 2,5,8,11‐tetraoxadecane (triglyme). We have demonstrated that a sterically encumbered auxiliary macrocyclic ligand within [Cu II (L)GGX] . 2+ complex ions [where L = 9‐aneN 3 or 12‐crown‐4] facilitates the formation of radical cationic peptides through gas‐phase fragmentation. We verified our experimental observations by examining the reactivities of a series of 19 tripeptides of the type GGX that differ only in the identity of their C‐terminal residue. The energy of the electron‐transfer reaction correlates well with the bond‐dissociation energy of the peptide–Cu(II) interaction; the presence of a constrained macrocyclic ligand weakens metal–peptide chelation through steric repulsion between the ligand and the peptide, and this situation may lead to more favorable radical cationic peptide formation. Copyright © 2006 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".