Structurally restricted Bi(III) metallation of apo-βMT1a: metal-induced tangling
Bibliographic record
Abstract
Non-toxic bismuth salts are used in anti-ulcer medications and to protect against nephrotoxicity from anticancer drugs. Bismuth salts also induce metallothionein (MT), a metal-binding protein that lacks a formal secondary structure. We report the impact on the metallation properties of Bi(III) to the 9-cysteine β fragment of MT as a function of cysteine accessibility using electrospray ionization mass spectrometry. At pH 7.4, Bi2βMT formed cooperatively. Cysteine modification shows that each Bi(III) was terminally bound to three cysteinyl thiolates. Non-cooperative Bi(III) binding was observed at pH 2.3, where cysteine accessibility is increased. However, competition from H4EDTA inhibited Bi(III) binding. When GdmCl, a well-known denaturing agent, was used to increase cysteine accessibility of the apoβMT at pH 7.4, a greater fraction of Bi3βMT formed using all nine cysteines. The change in binding profile and equilibrium of Bi2βMT was determined as a function of acidification, which changed as a result of competition with H4EDTA. There was no Bi(III) transfer between Bi2βMT, Cd3βMT, and Zn3βMT. This lack of metal exchange and the resistance towards binding the third Bi(III) suggest a rigidity in the Bi2βMT binding sites that inhibits Bi(III) mobility. These experiments emphasize the conformational control of metallation that results in substantially different metallated products: at pH 7.4 (many cysteines buried) Bi2βMT, whereas at pH 7.4 (all cysteines accessible) enhanced formation of Bi3βMT. These data suggest that the addition of the first two Bi(III) crosslinks the protein, blocking access to the remaining three cysteines for the third Bi(III), as a result of tangle formation.
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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".