Molecular dynamics studies of a β-sheet blocking peptide with the full-length amyloid beta peptide of Alzheimer’s disease
Bibliographic record
Abstract
The region encompassing residues 13–23 of the amyloid beta peptide (Aβ(13–23)) of Alzheimer’s disease is the self-recognition site that initiates toxic oligomerization and fibrillization. A number of pseudopeptides have been designed to bind to Aβ(13–23) and been computationally shown to do so with high affinity. More interactions are available in full-length Aβ than are available in the shorter peptide. We describe herein a study by molecular dynamics (MD) of nine distinct complexes formed by one such pseudopeptide, SGA1, with full-length beta amyloid, Aβ(1–42). The relative stabilities of the Aβ–SGA1 complexes were estimated by a combination of MD and ab initio methods. The most stable complex, designated AB1, was found to be one in which SGA1 is bound to the self-recognition site of Aβ(1–42) in an antiparallel β-sheet fashion. Another complex, designated AB3, also involved SGA1 binding to the self-recognition region of Aβ(1–42), albeit with lower affinity. In both AB1 and AB3, SGA1 formed antiparallel β-sheets but to opposite edges of Aβ. A complex, AB4, with similar stability to AB3, was found with a parallel β-sheet in the self-recognition site. A fourth complex, AB7, also with similar stability, formed a parallel β-sheet in the hydrophobic central region of Aβ. In all cases, complexation of SGA1 induced extensive β-sheet structure in Aβ(1–42).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".