Nanobodies against the metal binding domains of ATP7B as tools to study copper transport in the cell
Bibliographic record
Abstract
Nanobodies are genetically engineered single domain antibodies derived from the unusual heavy-chain only antibodies found in llamas and camels. The small size of the nanobodies and flexible selection schemes make them uniquely versatile tools for protein biochemistry and cell biology. We have developed a panel of nanobodies against the metal binding domains of the human copper transporter ATP7B, a multidomain membrane protein with a complex regulation of enzymatic activity and intracellular localization. To enable the use of the nanobodies as tools to investigate copper transport in the cell, we characterized their binding sites and affinity by isothermal titration calorimetry and NMR. We have identified nanobodies against each of the first four metal binding domains of ATP7B, with a wide affinity range, as evidenced by dissociation constants from below 10-9 to 10-6 M. We found both the inhibitory and activating nanobodies among those tested. The diverse properties of the nanobodies make the panel useful for the structural studies of ATP7B, immunoaffinity purification of the protein, modulation of its activity in the cell, protein dynamics studies, and as mimics of copper chaperone ATOX1, the natural interaction partner of ATP7B.
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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 | 0.001 |
| 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".