Identification and Structural Characterisation of Carboxy-Terminal Polypeptides and Antibody Epitopes of Alzheimer's Amyloid Precursor Protein Using High-Resolution Mass Spectrometry
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Bibliographic record
Abstract
Alzheimer's disease (AD) is the most common cause for human age-related dementia, characterised by formation of diffuse plaques in brain that are directly involved in AD pathogenesis. The major component of AD plaques is beta-amyloid, a 40 to 42 amino acid polypeptide derived from the amyloid precursor protein (APP) by proteolytic degradation involving the specific proteases, beta-and gamma-secretase acting at the N- and C- terminal cleavage site, respectively. In this study we have prepared polypeptides comprising the carboxy-terminal and transmembrane sequences of APP, by bacterial expression and chemical synthesis, as substrates for studying the C-terminal processing of APP and its interaction with the gamma-secretase complex. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) was used as a major tool for structure analysis. Immunisation of transgenic mouse models of AD with Abeta42 has been recently shown to be effective to inhibit and disaggregate Abeta-fibrils, and to reduce AD-related neuropathology and memory impairments. However, the mechanism underlying these therapeutic effects has been as yet unclear. Using proteolytic epitope excision from immune complexes in combination with FT-ICR-MS, we identified the epitope recognised by the therapeutically active antibody as the N-terminal Abeta(4-10) sequence; this soluble, nontoxic epitope opens new lead structures for AD vaccine development. A monoclonal antibody (Jonas; JmAb) directed against the cytosolic APP domain was used in studies of APP biochemistry and metabolism. Here we report the identification of the epitope recognised by the JmAb, using the combination of epitope excision and peptide mapping by FT-ICR-MS. The epitope was determined to be located at the C-terminal APP(740-747) sequence; it was confirmed by ELISA binding assays and authentic synthetic peptides and will be an efficient tool in the development of new specific vaccines. These results demonstrate high-resolution FT-ICR-MS as a powerful method for characterising biochemical pathways and molecular recognition structures of APP.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it