Bibliographic record
Abstract
The number of reported cases of chronic arsenic poisoning is on the rise throughout the world, making the study of the long-term effects of arsenic critical. As(3+) binds readily to biological thiols, including mammalian metallothionein (MT), which is an ubiquitous sulfur-rich metalloprotein known to coordinate a wide range of metals. The two-domain mammalian protein binds divalent metals (M) into two metal-thiolate clusters with stoichiometries of M(3)S(cys9) (beta) and M(4)S(cys11) (alpha). We report that As(3+) binds with stoichiometries of As(3)S(cys9) (beta) and As(3)S(cys11) (alpha) to the recombinant human metallothionein (rhMT) isoform 1a protein. Further, we report the complete kinetic analysis of the saturation reactions of the separate alpha and beta domains of rhMT with As(3+). Speciation in the metalation reactions was determined using time- and temperature-resolved electrospray ionization mass spectrometry. The binding reaction of As(3+) to the alpha and beta MT domains is shown to be noncooperative and involves three sequential, bimolecular metalation steps. The analyses allow for the first time the complete simulation of the experimental data for the stepwise metalation reaction of MT showing the relative concentrations of the metal-free, apo MT and each of the As-MT intermediate species as a function of time and temperature. At room temperature (298 K) and pH 3.5, the individual rate constants for the first, second, and third As(3+) binding to apo-alphaMT are 5.5, 6.3, and 3.9 M(-)(1) s(-)(1) and for apo-betaMT the constants are 3.6, 2.0, and 0.6 M(-)(1) s(-)(1). The activation energy for formation of As(1)-H(6)-betaMT is 32 kJ mol(-)(1), for As(2)-H(3)-betaMT it is 35 kJ mol(-)(1), for As(3)-betaMT it is 29 kJ mol(-)(1), for As(1)-H(8)-alphaMT it is 33 kJ mol(-)(1), for As(2)-H(5)-alphaMT it is 29 kJ mol(-)(1), and for As(3)-H(2)-alphaMT it is 23 kJ mol(-)(1).
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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) | 0.002 | 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".