Oligomerization Alters Binding Affinity between Amyloid Beta and a Modulator of Peptide Aggregation
Bibliographic record
Abstract
The soluble oligomeric form of the amyloid beta (Aβ) peptide is the major causative agent in the molecular pathogenesis of Alzheimer’s disease (AD). We have previously developed a pyrroline-nitroxyl fluorene compound (SLF) that blocks the toxicity of Aβ. Here we introduce the multiparametric surface plasmon resonance (MP-SPR) approach to quantify SLF binding and its effect on the self-association of the peptide via a label-free, real-time approach. Kinetic analysis of SLF binding to Aβ and measurements of layer thickness alterations inform on the mechanism underlying the ability of SLF to inhibit Aβ toxicity and its progression toward larger oligomeric assemblies. Depending on the oligomeric state of Aβ, distinct binding affinities for SLF are revealed. The Aβ monomer and dimer uniquely possess subnanomolar affinity for SLF via a nonspecific mode of binding. SLF binding is weaker in oligomeric Aβ, which displays an affinity for SLF on the order of 100 μM. To complement these experiments we carried out molecular docking and molecular dynamics simulations to explore how SLF interacts with the Aβ peptide. The MP-SPR results together with in silico modeling provide affinity data for the SLF-Aβ interaction and allow us to develop a new general method for examining protein aggregation.
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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 | 0.000 | 0.000 |
| 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".