Curcumin Binding to Beta Amyloid: A Computational Study
Bibliographic record
Abstract
Curcumin, a chemical constituent present in the spice turmeric, is known to prevent the aggregation of amyloid peptide implicated in the pathophysiology of Alzheimer's disease. While curcumin is known to bind directly to various amyloid aggregates, no systematic investigations have been carried out to understand its ability to bind to the amyloid aggregates including oligomers and fibrils. In this study, we constructed computational models of (i) Aβ hexapeptide (16) KLVFFA(21) octamer steric-zipper β-sheet assembly and (ii) full-length Aβ fibril β-sheet assembly. Curcumin binding in these models was evaluated by molecular docking and molecular dynamics (MD) simulation studies. In both the models, curcumin was oriented in a linear extended conformation parallel to fiber axis and exhibited better stability in the Aβ hexapeptide (16) KLVFFA(21) octamer steric-zipper model (Ebinding = -10.05 kcal/mol) compared to full-length Aβ fibril model (Ebinding = -3.47 kcal/mol). Analysis of MD trajectories of curcumin bound to full-length Aβ fibril shows good stability with minimum Cα-atom RMSD shifts. Interestingly, curcumin binding led to marked fluctuations in the (14) HQKLVFFA(21) region that constitute the fibril spine with RMSF values ranging from 1.4 to 3.6 Å. These results show that curcumin binding to Aβ shifts the equilibrium in the aggregation pathway by promoting the formation of non-toxic aggregates.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".