P4‐343: Biophysical studies on the interactions of BETA‐AMYLOID and pseudopeptidic inhibitors of BETA‐AMYLOID oligomerization: Molecular dynamics simulations
Bibliographic record
Abstract
In Alzheimer's disease (AD) brain the amyloid beta (Aβ) peptide appears in senile plaques but the neurotoxic forms are soluble oligomers, most likely in association with redox active metals, e.g., copper. One way to stop Aβ neurotoxic activity is to prevent oligomerization and its interaction with copper. It was shown that Aβ binds to itself in the His13-Asp23 region of Aβ, and Cu +/Cu 2+ binds in the His13-His14 region. Aβ bound to copper incites processes that lead to the production of radicals responsible for death of neuron cells. In our lab several pseudopeptides (ligands) are designed to bind with Aβ (Lys16-Asp23) (S. Roy, PhD Dissertation, 2010). In order to avoid attack by antibodies the ligands are composed of eight amino acid residues. The ligands were designed to make antiparallel or parallel beta-sheets with Aβ (13–23), and are composed of both L- and D-handed residues. Selective N-methylation prevents propagation of the β-sheets into the toxic oligomeric form. This poster describes the results of a study using molecular dynamics simulations on the structures and energetic of the ligands and the ligand/Aβ (13–23) complexes, including estimates of the binding affinity of the ligands. We found that our ligands can compete successfully with Aβ(13–23) even though the dimerization interaction is relatively stronger. Cu(I) binds to His13 and His14. The effect of Cu(I)-binding to Ab is initially simulated by protonation of the His residues and the effects on ligand binding are reported. The results suggest modifications to the design to compete for the copper binding site of Aβ.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".