Pseudopeptide Amyloid Aggregation Inhibitors: In Silico, Single Molecule and Cell Viability Studies
Bibliographic record
Abstract
Neurodegeneration in Alzheimer's disease (AD) is defined by pathology featuring amyloid-β (Aβ) deposition in the brain. Aβ monomers themselves are generally considered to be nontoxic, but misfold into β-sheets and aggregate to form neurotoxic oligomers. One suggested strategy to treat AD is to prevent the formation of toxic oligomers. The SG inhibitors are a class of pseudopeptides designed and optimized using molecular dynamics (MD) simulations for affinity to Aβ and experimentally validated for their ability to inhibit amyloid-amyloid binding using single molecule force spectroscopy (SMFS). In this work, we provide a review of our previous MD and SMFS studies of these inhibitors and present new cell viability studies that demonstrate their neuroprotective effects against Aβ(1-42) oligomers using mouse hippocampal-derived HT22 cells. Two of the tested SG inhibitors, predicted to bind Aβ in anti-parallel orientation, demonstrated neuroprotection against Aβ(1-42). A third inhibitor, predicted to bind parallel to Aβ, was not neuroprotective. Myristoylation of SG inhibitors, intended to enhance delivery across the blood-brain barrier (BBB), resulted in cytotoxicity. This is the first use of HT22 cells for the study of peptide aggregation inhibitors. Overall, this work will inform the future development of peptide aggregation inhibitors against Aβ toxicity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".