pH- and Metal Ion-Linked Stability of the Hemopexin−Heme Complex
Bibliographic record
Abstract
Thermal denaturation of the human hemopexin-heme complex was investigated under a variety of solution conditions to identify factors that influence heme release. The midpoint temperature for the transition between the folded and folded states, T(m), of the hemopexin-ferriheme complex exhibits a significant dependence on pH. When the pH is reduced from 7 to 5 (50 mM BisTris buffer and 50 mM NaCl), T(m) decreases by approximately 23 degrees C despite the relatively higher chloride concentration that tends to stabilize the protein. The thermal stability of the hemopexin-ferroheme complex was examined at pH 7.4 to yield a T(m) that is 3.2 degrees C lower than that of the hemopexin-ferriheme complex under identical conditions. The effect of transition metal ions, which hemopexin has recently been shown to bind [Mauk, M. R., Rosell, F. I., Lelj-Garolla, B., Moore, G. R., and Mauk, A. G. (2005) Biochemistry 44, XXXX-XXXX], was also considered. Cu(2+) and Zn(2+) had the greatest effect, reducing T(m) for the transition by 4.8 and 6.5 degrees C, respectively, relative to the value for the protein in the absence of metal ions [T(m) = 64.9 degrees C [10 mM sodium phosphate buffer (pH 7.4)]]. These metal ions also interfered significantly with the recovery of the native state from the unfolded protein when the protein on returning to 20 degrees C. The current results demonstrate how the conditions within the endosomes of hepatocytes (pH approximately 5.0, [Cl(-)] approximately 60 mM) and the potential presence of transition metal ions or heme iron reduction contribute to the membrane receptor-mediated process of heme release from hemopexin.
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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) | 0.000 | 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".