Binding Affinities for Models of Biologically Available Potential Cu(II) Ligands Relevant to Alzheimer's Disease: An ab Initio Study
Bibliographic record
Abstract
A systematic study of the binding affinities of the model biological ligands X: = (CH3)2S, CH3S-, CH3NH2, 4-CH3-imidazole (MeImid), C6H5O-, and CH3CO2- to (NH3)i(H2O)3-iCu(II)-H2O (i = 3, 2, 1, 0) complexes has been carried out using quantum chemical calculations. Geometries have been obtained at the B3LYP/ 6-31G(d) level of theory, and binding energies, Delta, relative to H2O as a ligand, have been calculated at the B3LYP/6-311+G(2df,2p)//B3LYP/6-31G(d) level. Solvation effects have been included using the COSMO model, and the relative binding free energies in aqueous solution (Delta) have been determined at pH 7 for processes that are pH dependent. CH3S- (Delta = -16.0 to -53.5 kJ mol(-1)) and MeImid (Delta = -18.5 to -35.2 kJ mol(-1)) give the largest binding affinities for Cu(II). PhO- and (CH3)2S are poor ligands for Cu(II), Delta = 20.6 to -9.7 and 19.8 to -3.7 kJ mol(-1), respectively. The binding affinities for CH3NH2 range from -0.8 to -15.0 kJ mol(-1). CH3CO2- has Cu(II) binding affinities in the ranges Delta = -13.5 to -32.4 kJ mol(-1) if an adjacent OH bond is available for hydrogen bonding and Delta = 10.1 to -4.6 kJ mol(-1) if this interaction is not present. In the context of copper coordination by the Abeta peptide of Alzheimer's disease, the binding affinities suggest preferential binding of Cu(II) to the three histidine residues plus a lysine or the N-terminus. For a 3N1O Cu(II) ligand arrangement, it is more probable that the oxygen ligand comes from an aspartate/glutamate residue side chain than from the tyrosine at position 10. Methionine appears unlikely to be a Cu(II) ligand in Abeta.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".