The binding of zinc and copper ions to nerve growth factor is differentially affected by pH: implications for cerebral acidosis
Bibliographic record
Abstract
It has recently been shown that transition metal cations Zn2+ and Cu2+ bind to histidine residues of nerve growth factor (NGF) and other neurotrophins (a family of proteins important for neuronal survival) leading to their inactivation. Experimental data and theoretical considerations indicate that transition metal cations may destabilize the ionic form of histidine residues within proteins, thereby decreasing their pK(a) values. Because the release of transition metal cations and acidification of the local environment represent important events associated with brain injury, the ability of Zn2+ and Cu2+ to bind to neurotrophins in acidic conditions may alter neuronal death following stroke or as a result of traumatic injury. To test the hypothesis that metal ion binding to neurotrophins is influenced by pH, the effects of Zn2+ and Cu2+ on NGF conformation, receptor binding and NGF tyrosine kinase (trkA) receptor signal transduction were examined under conditions mimicking cerebral acidosis (pH range 5.5-7.4). The inhibitory effect of Zn2+ on biological activities of NGF is lost under acidic conditions. Conversely, the binding of Cu2+ to NGF is relatively independent of pH changes within the studied range. These data demonstrate that Cu2+ has greater binding affinity to NGF than Zn2+ at reduced pH, consistent with the higher affinity of Cu2+ for histidine residues. These findings suggest that cerebral acidosis associated with stroke or traumatic brain injury could neutralize the Zn2+-mediated inactivation of NGF, whereas corresponding pH changes would have little or no influence on the inhibitory effects of Cu2+. The importance of His84 of NGF for transition metal cation binding is demonstrated, confirming the involvement of this residue in metal ion coordination.
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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".