Peptides based on the presenilin‐APP binding domain inhibit APP processing and Aβ production through interfering with the APP transmembrane domain
Bibliographic record
Abstract
Presenilins (PSENs) form the catalytic component of the γ‐secretase complex, responsible for intramembrane proteolysis of amyloid precursor protein (APP) and Notch, among many other membrane proteins. Previously, we identified a PSEN1‐binding domain in APP, encompassing half of the transmembrane domain following the amyloid β (Aβ) sequence. Based on this, we designed peptides mimicking this interaction domain with the aim to selectively block APP processing and Aβ generation through interfering with enzyme‐substrate binding. We identified a peptide sequence that, when fused to a virally derived translocation peptide, significantly lowered Aβ production (IC 50 : 317 nM) in cell‐free and cell‐based assays using APP‐carboxy terminal fragment as a direct γ‐secretase substrate. Being derived from the APP sequence, this inhibitory peptide did not affect NotchΔE γ‐cleavage, illustrating specificity and potential therapeutic value. In cell‐based assays, the peptide strongly suppressed APP shedding, demonstrating that it exerts the inhibitory effect already upstream of γ‐secretase, most likely through steric hindrance.—Esselens, C., Sannerud, R., Gallardo, R., Baert, V., Kaden, D., Serneels, L., De Strooper, B., Rousseau, F., Multhaup, G., Schymkowitz, J., Langedijk, J. P. M., Annaert, W. Peptides based on the presenilin‐APP binding domain inhibit APP processing and Aβ production through interfering with the APP transmembrane domain. FASEB J. 26, 3765–3778 (2012). www.fasebj.org
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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".