Formation of oxidative and non‐oxidative dimers in metallothioneins: Implications for charge‐state analysis for structural determination
Bibliographic record
Abstract
RATIONALE: Metallothioneins (MTs) are a class of dynamic proteins that have been investigated extensively using mass spectrometric methods due to their amenability to ionization. Here we detect the formation of oxidative and non-oxidative MT dimers using high-resolution mass spectrometry (HRMS) which has previously been overlooked with lower-resolution techniques. METHODS: Recombinant human MT1a and its isolated domain fragments were analyzed by high-resolution Thermo Q-Exactive and Bruker time-of-flight (TOF) mass spectrometers. Covalent Cys modification was performed using N-ethylmalemide to probe the effect of Cys oxidation on dimer formation. RESULTS: MT and apo-βMT. Specifically, high resolution (140 k) revealed the +6 dimer peaks overlapping with the +3 charge state, but not with the other charge states (+4, +5, +6). The proteins with covalently modified Cys did not show dimer formation in any of their charge states. Apo-α and apo-βαMT also did not form dimers under the conditions tested. CONCLUSIONS: Dimerization of MT was detected for zinc metalated and certain apo-MT forms with HRMS, which was not seen with lower-resolution techniques. These dimers appear overlapped only with certain charge states, confounding their analysis for structural characterization of MTs. The Zn-MT dimers appeared to be non-oxidative; however, the formation of dimers in the apo-protein is likely dependent on Cys oxidation.
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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) | 0.000 | 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 | 0.001 |
| 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".