Structures and Stabilities of Fe<sup>2+/3+</sup> Complexes Relevant to Alzheimer’s Disease: An ab Initio Study
Bibliographic record
Abstract
Iron is one of the most abundant metals found in senile plaques of post mortem patients with Alzheimer's disease. However, the interaction mode between iron ions and β-amyloid peptide as well as their precise affinity is unknown. In this study we apply ab initio computational methodology to calculate binding energies of Fe(2+/3+) with the His13-His14 sequence of Aβ, as well as other important ligands such as His6 and Tyr10. Calculations were carried out at the "MP2/6-311+G(2df,2p)"//B3LYP/6-31+G(d) level of theory and solvent effects included by the IEFPCM procedure. Several reaction paths for the binding of imidazole, phenol, and the His13-His14 fragment (modeled by N-(2-(1H-imidazol-4-yl)ethyl)-3-(1H-imidazol-4-yl)propanamide) were sequentially explored. The results show that the most stable complexes containing His13-His14 and phenolate of Tyr10 are the pentacoordinated [Fe(2+)(O-HisHis)(PhO(-))(H(2)O)](+) and [Fe(3+)(N-HisHis)(PhO(-))(H(2)O)](+) compounds and that simultaneous coordination of tyrosine and His13-His14 to Fe(2+/3+) is thermodynamically favorable in water at physiological pH. Computed Raman spectra confirm the conclusion obtained by Miura et al. ( Biochemistry 2000 , 39 , 7024 ) that tyrosine is coordinated to Fe(3+) but do not exclude coordination of imidazoles. Finally, calculations of standard reduction potentials indicate that phenol coordination reduces the redox activity of the iron/Aβ complexes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".