Understanding the Molecular Basis for the Inhibition of the Alzheimer's Aβ-Peptide Oligomerization by Human Serum Albumin Using Saturation Transfer Difference and Off-Resonance Relaxation NMR Spectroscopy
Bibliographic record
Abstract
Human serum albumin (HSA) inhibits the formation of amyloid beta-peptide (Abeta) fibrils in human plasma. However, currently it is not known how HSA affects the formation of the highly toxic soluble diffusible oligomers that occur in the initial stages of Abeta fibrillization. We have therefore investigated by solution NMR the interaction of HSA with the Abeta(12-28) peptide, which has been previously shown to provide a reliable and stable model for the early prefibrillar oligomers as well as to contain key determinants for the recognition by albumin. For this purpose we propose a novel NMR approach based on the comparative analysis of Abeta in its inhibited and filtrated states monitored through both saturation transfer difference and recently developed nonselective off-resonance relaxation experiments. This combined NMR strategy reveals a mechanism for the oligomerization inhibitory function of HSA, according to which HSA targets preferentially the soluble oligomers of Abeta(12-28) rather than its monomeric state. Specifically, HSA caps the exposed hydrophobic patches located at the growing and/or transiently exposed sites of the Abeta oligomers, thereby blocking the addition of further monomers and the growth of the prefibrillar assemblies. The proposed model has implications not only for the pharmacological treatment of Alzheimer's disease specifically but also for the inhibition of oligomerization in amyloid-related diseases in general. In addition, the proposed NMR approach is expected to be useful for the investigation of the mechanism of action of other oligomerization inhibitors as well as of other amyloidogenic systems.
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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 | 0.001 | 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 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".