Molecular dynamics study on the folding and metallation of the individual domains of metallothionein
Bibliographic record
Abstract
De novo synthesis of metallothionein (MT) initially forms the metal-free protein, which must, in a posttranslational reaction, coordinate metal ions via the cysteine sulfur ligands to form the fully folded protein structure. In this article, we use molecular dynamics (MD) and molecular mechanics (MM) to investigate the metal-dependent folding steps of the individual domains of recombinant human metallothionein (MT). The divalent metals were removed sequentially from the metal-sulfur M4(Scys)11 and M3(Scys)9 clusters within the alpha- and beta- domains of MT, respectively, after protonation of the previously coordinating sulfurs. With each of the four (alpha) or three (beta) sites defined, an order of metal release could be determined on the basis of a comparison of the strain energies for each combination by selecting the lowest energy demetallated conformations. The effect of an additional noninteracting, 34-residue peptide sequence on the demetallation order was assessed when bound to either the N- or C-termini of the individual domain fragments to identify the differences in cluster stability between one- and two-domain proteins. The N-terminal-bound peptide had no effect on the order of metal removal; however, addition to the C-terminus significantly altered the sequence. The number of hydrogen bonds was calculated for each energy-minimized demetallated structure and was increased on metal removal, indicating a possible stabilization mechanism for the protein structure via a hydrogen-bonding network. On complete demetallation, the cysteinyl sulfurs were shown to move to the exterior surface of the peptide chain.
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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 | 0.000 | 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".