Metallobiological Necklaces: Mass Spectrometric and Molecular Modeling Study of Metallation in Concatenated Domains of Metallothionein
Bibliographic record
Abstract
The ubiquitous protein metallothionein (MT) has proven to be a major player not only in the homeostasis of Cu(I) and Zn(II), but also binds all the Group 11 and 12 metals. Metallothioneins are characterised by the presence of numerous cys-x-cys and cys-cys motifs in the sequence and are found naturally with either one domain or two, linked, metal-binding domains. The use of chains of these metal-thiolate domains offers the possibility of creating chemically tuneable and, therefore, chemically dependent electrochemical or photochemical surface modifiers or as nanomachinery with nanomechanical properties. In this work, the metal-binding properties of the Cd(4)-containing domain of alpha-rhMT1a assembled into chains of two and three concatenated domains, that is, "necklaces", have been studied by spectrometric techniques, and the interactions within the structures modelled and interpreted by using molecular dynamics. These chains are metallated with 4, 8 or 12 Cd(II) ions to the 11, 22, and 33 cysteinyl sulfur atoms in the alpha-rhMT1a, alphaalpha-rhMT1a, and alphaalphaalpha-rhMT1a proteins, respectively. The effect of pH on the folding of each protein was studied by ESI-MS and optical spectroscopy. MM3/MD simulations were carried out over a period of up to 500 ps by using force-field parameters based on the reported structural data. These calculations provide novel information about the motion of the clustered metallated, partially demetallated, and metal-free peptide chains, with special interest in the region of the metal-binding site. The MD energy/time trajectory conformations show for the first time the flexibility of the metal-sulfur clusters and the bound amino acid chains. We report unexpected and very different sizes for the metallated and demetallated proteins from the combination of experimental data, with molecular dynamics simulations.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".