Investigating the Interactions of Fatty Acids, Amyloid Beta & SARS-CoV-2 Spike Protein Fragment
Bibliographic record
Abstract
There is no cure for Alzheimer’s disease (AD), and the negative implications of having AD were further exacerbated in recent years, as patients with dementia are at the highest risk for mortality upon contracting COVID-19. The amyloid cascade theory postulates that AD is caused by toxic aggregates of amyloid beta (Aβ) peptide. The main objective of this project was to design, synthesize and evaluate a library of fatty acid derivatives based on docosahexaenoic acid (DHA), oleic acid (OA), eicosapentaenoic acid (EPA), linoleic acid (LNA), and α-linolenic acid (ALA) as inhibitors of Aβ42 aggregation. 10 fatty acid derivatives were synthesized, characterized, and evaluated for Aβ42 aggregation inhibition activity using thioflavin T-based Aβ42 aggregation kinetics assays. The methyl ester derivatives were found to be the most promising inhibitors, with the LNA derivative methyl (9Z,12Z)-octadeca-9,12-dienoate (2a) being the most potent (61% inhibition at 25 μM). Transmission electron microscopy (TEM) experiments confirmed the anti-aggregation activity of 2a, and computational modeling studies suggest that the evaluated fatty acid derivatives bind in a narrow channel at the interface of the N- and C-termini in the Aβ42 pentamer model. Furthermore, the fatty acid derivatives were not toxic to HT22 mouse hippocampal cells (cell viability ~94–104% at 25 μM). Our secondary objective was to evaluate amyloidogenic peptide fragment FKNIDGYFKI derived from the SARS-CoV-2 spike protein for its ability to promote Aβ42 aggregation. Interestingly, the decapeptide was found to inhibit Aβ42 aggregation at all tested concentrations (~37–52%). In summary, thesis outcomes demonstrate that fatty acid derivatives and spike peptide fragment exhibit anti-Aβ42 activity by direct binding and have the potential to be used as novel pharmacological tools to study Aβ aggregation and to design novel therapies to treat AD.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".