Kinetics of Amyloid β-Protein Degradation Determined by Novel Fluorescence- and Fluorescence Polarization-based Assays
Bibliographic record
Abstract
Proteases that degrade the amyloid beta-protein (Abeta) are important regulators of brain Abeta levels in health and in Alzheimer's disease, yet few practical methods exist to study their detailed kinetics. Here, we describe robust and quantitative Abeta degradation assays based on the novel substrate, fluorescein-Abeta-(1-40)-Lys-biotin (FAbetaB). Liquid chromatography/mass spectrometric analysis shows that FAbetaB is hydrolyzed at closely similar sites as wild-type Abeta by neprilysin and insulin-degrading enzyme, the two most widely studied Abeta-degrading proteases. The derivatized peptide is an avid substrate and is suitable for use with biological samples and in high throughput compound screening. The assays we have developed are easily implemented and are particularly useful for the generation of quantitative kinetic data, as we demonstrate by determining the kinetic parameters of FAbetaB degradation by several Abeta-degrading proteases, including plasmin, which has not previously been characterized. The use of these assays should yield additional new insights into the biology of Abeta-degrading proteases and facilitate the identification of activators and inhibitors of such enzymes.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".