Effects of Cu Ions and Explicit Water Molecules on the Copper Binding Domain of Amyloid Precursor Protein APP(131–189): A Molecular Dynamics Study
Bibliographic record
Abstract
Amyloid precursor protein (APP) is a cell-surface trans-membrane glycoprotein that appears to play an important role in in vivo Cu ion homeostasis. This protein includes a copper-binding-domain (CuBD) fragment consisting of residues 124-189, of which His147, His151, Tyr168, and possibly Met170 comprise the explicit Cu-binding site (CuBS). Molecular dynamics (MD) simulations are carried out on Cu-free and Cu-bound APP models, based on crystal structures including residues 131-189 obtained from the Protein Data Bank, to confirm the site of Cu-ion binding and to elucidate the effects of the oxidation state of the Cu ions (default GROMACS parameters modeled only the electrostatic binding to the Cu ions at the CuBS) and explicit water molecules on the conformational properties of the 131-189 residue portion of the CuBD. MD trajectory analysis demonstrated a conformational change of Met170. The sulfur atom of Met170 moves closer to the Cu(II) ion and away from Cu(I), and this change may play an important role in the reduction of Cu(II) and the release of Cu(I). Two explicit water molecules were included in the MD simulations. These water molecules that bind strongly to the Cu ions via their lone pair electrons result in a significant modification of the binding interactions with the other residues at the CuBS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".