Engineering Competitive Magnesium Binding into the First EF-hand of Skeletal Troponin C
Bibliographic record
Abstract
The goal of this study was to examine the mechanism of magnesium binding to the regulatory domain of skeletal troponin C (TnC). The fluorescence of Trp(29), immediately preceding the first calcium-binding loop in TnC(F29W), was unchanged by addition of magnesium, but increased upon calcium binding with an affinity of 3.3 microm. However, the calcium-dependent increase in TnC(F29W) fluorescence could be reversed by addition of magnesium, with a calculated competitive magnesium affinity of 2.2 mm. When a Z acid pair was introduced into the first EF-hand of TnC(F29W), the fluorescence of G34DTnC(F29W) increased upon addition of magnesium or calcium with affinities of 295 and 1.9 microm, respectively. Addition of 3 mm magnesium decreased the calcium sensitivity of TnC(F29W) and G34DTnC(F29W) approximately 2- and 6-fold, respectively. Exchange of G34DTnC(F29W) into skinned psoas muscle fibers decreased fiber calcium sensitivity approximately 1.7-fold compared with TnC(F29W) at 1 mm [magnesium](free) and approximately 3.2-fold at 3 mm [magnesium](free). Thus, incorporation of a Z acid pair into the first EF-hand allows it to bind magnesium with high affinity. Furthermore, the data suggests that the second EF-hand, but not the first, of TnC is responsible for the competitive magnesium binding to the regulatory domain.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".