Understanding the Inhibition of the Alzheimer's Ab peptide by Human Serum Albumin
Bibliographic record
Abstract
Aggregation of the Alzheimer’s Aβ peptide in the brain and blood plasma is controlled by endogenous Aβ binding proteins. The structural basis for the interaction between the Aβ peptide and the Aβ binding proteins is critical not only to understand how Aβ amyloids are controlled in vivo, but also to guide the design of novel Aβ-self association inhibitors. However, the current knowledge of the structures of the Aβ/Aβ binding protein complexes is still sparse. This thesis focuses mainly on the interaction of the Aβ peptide with Human Serum Albumin (HSA). It is known that HSA binds ~90% of the Aβ in human plasma and prevents the Aβ self-association into amyloid fibrils. However, the mechanism of Aβ self-association inhibition by albumin was not understood prior to our work. We have shown that albumin preferentially binds toxic Aβ oligomers and fibrils inhibiting their growth into larger Aβ assemblies through a “monomer competitor” mechanism. Using a combination of NMR, domain deletion mutants, dynamic light scattering and ultrafiltration we have investigated the stoichiomery and affinity of the Aβ oligomer: HSA complexes. Our results indicate that all three domains of HSA bind Aβ oligomers and fibrils with an affinity in the 1-100 nM range. Such binding site degeneracy explains how albumin minimizes competition by other ligands such as fatty acids and drugs. Moreover we have used the soluble and NMR suitable domain 3 of albumin to dissect further the determinants of the Aβ oligomer binding to albumin at subdomain and peptide resolution. We show that both subdomains of the HSA domain 3 (i.e. 3A and 3B) bind the Aβ oligomers. In addition, we identified a peptide sequence within subdomain 3B that displays significant potency in the inhibition of Aβ self-association.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".