Validation and Characterization of a Novel Peptide That Binds Monomeric and Aggregated β-Amyloid and Inhibits the Formation of Neurotoxic Oligomers
Bibliographic record
Abstract
Although the formation of β-amyloid (Aβ) deposits in the brain is a hallmark of Alzheimer disease (AD), the soluble oligomers rather than the mature amyloid fibrils most likely contribute to Aβ toxicity and neurodegeneration. Thus, the discovery of agents targeting soluble Aβ oligomers is highly desirable for early diagnosis prior to the manifestation of a clinical AD phenotype and also more effective therapies. We have previously reported that a novel 15-amino acid peptide (15-mer), isolated via phage display screening, targeted Aβ and attenuated its neurotoxicity (Taddei, K., Laws, S. M., Verdile, G., Munns, S., D'Costa, K., Harvey, A. R., Martins, I. J., Hill, F., Levy, E., Shaw, J. E., and Martins, R. N. (2010) Neurobiol. Aging 31, 203-214). The aim of the current study was to generate and biochemically characterize analogues of this peptide with improved stability and therapeutic potential. We demonstrated that a stable analogue of the 15-amino acid peptide (15M S.A.) retained the activity and potency of the parent peptide and demonstrated improved proteolytic resistance in vitro (stable to t = 300 min, c.f. t = 30 min for the parent peptide). This candidate reduced the formation of soluble Aβ42 oligomers, with the concurrent generation of non-toxic, insoluble aggregates measuring up to 25-30 nm diameter as determined by atomic force microscopy. The 15M S.A. candidate directly interacted with oligomeric Aβ42, as shown by coimmunoprecipitation and surface plasmon resonance/Biacore analysis, with an affinity in the low micromolar range. Furthermore, this peptide bound fibrillar Aβ42 and also stained plaques ex vivo in brain tissue from AD model mice. Given its multifaceted ability to target monomeric and aggregated Aβ42 species, this candidate holds promise for novel preclinical AD imaging and therapeutic strategies.
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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 | 0.001 | 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 | 0.001 | 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".