Disease-causing point-mutations in metal-binding domains of Wilson disease protein decrease stability and increase structural dynamics
Bibliographic record
Abstract
After cellular uptake, Copper (Cu) ions are transferred from the chaperone Atox1 to the Wilson disease protein (ATP7B) for incorporation into Cu-dependent enzymes in the secretory pathway. Human ATP7B is a large multi-domain membrane-spanning protein which, in contrast to homologues in other organisms, has six similar cytoplasmic metal-binding domains (MBDs). The reason for multiple MBDs is proposed to be indirect modulation of enzymatic activity and it is thus intriguing that point mutations in MBDs can promote Wilson disease. We here investigated, in vitro and in silico, the biophysical consequences of clinically-observed Wilson disease mutations, G85V in MBD1 and G591D in MBD6, incorporated in domain 4. Because G85 and G591 correspond to a conserved Gly found in all MBDs, we introduced the mutations in the well-characterized MBD4. We found the mutations to dramatically reduce the MBD4 thermal stability, shifting the midpoint temperature of unfolding by more than 20 °C. In contrast to wild type MBD4 and MBD4D, MBD4V adopted a misfolded structure with a large β-sheet content at high temperatures. Molecular dynamic simulations demonstrated that the mutations increased backbone fluctuations that extended throughout the domain. Our findings imply that reduced stability and enhanced dynamics of MBD1 or MBD6 is the origin of ATP7B dysfunction in Wilson disease patients with the G85V or G591D mutation.
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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".