Locating Pb<sup>2+</sup>and Zn<sup>2+</sup>in Zinc Finger-Like Peptides Using Mass Spectrometry
Bibliographic record
Abstract
The binding preferences of Pb(2+)and Zn(2+) in doubly charged complexes with zinc finger-like 12-residue peptides (Pep), [Mn(Pep-2(n-1)H)](2+) have been explored using tandem mass spectrometry. The peptides were synthesized strategically by blocking the N-terminus with an acetyl group and with four cysteine and/or histidine residues in positions 2, 5, 8, and 11, arranged in different motifs: CCHH, CHCH, and CCCC. The MS(2) spectra of the Pb(2+) and Zn(2+) complexes show multiple losses of water and a single methane loss and these provide a sensitive method for locating the metal dication and so elucidating its coordination. The elimination of a methane molecule indicated the position of the metal at the Cys2 residue. Whereas lead was observed to preferentially bind to cysteine residues, zinc was found to primarily bind to histidine residues and secondarily to cysteine residues. Preferential binding of lead to cysteine is preserved in the complexes with more than one Pb(2+). Key to the mechanism of the loss of water and methane is the metal dication withdrawing electrons from the proximal amidic nitrogen. This acidic nitrogen loses its hydrogen to an amidic oxygen situated four atoms away leading to formation of a five-member ring and the elimination of water.
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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) | 0.000 | 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) | 0.001 | 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".