Tau-Derived-Hexapeptide <sup>306</sup>VQIVYK<sup>311</sup> Aggregation Inhibitors: Nitrocatechol Moiety as A Pharmacophore In Drug Design
Bibliographic record
Abstract
The nitrocatechol derivatives tolcapone (1) and entacapone (2), used as adjunctive therapy in the treatment of Parkinson's disease, were investigated for their potential to inhibit the tau-derived-hexapeptide 306VQIVYK311. They were compared to small molecules that contain similar pharmacophores including the catechol derivatives (dopamine 3 and epinephrine 4), nitroderivatives (nifedipine 5 and chloramphenicol 6), nitrocatechol isomers (7 and 8), and a tolcapone derivative (13) lacking the nitrocatechol moiety. The aggregation kinetics by thioflavin S fluorescence assay indicates that both tolcapone (1) and entacapone (2) exhibit antiaggregation properties. These findings were supported by transmission electron microscopy (TEM) and circular dichroism (CD) spectroscopy measurements which suggest that the nitrocatechol (3,4-dihydroxy-5-nitrophenyl) moiety is a suitable pharmacophore in the design of new tau-aggregation inhibitors. Furthermore, tolcapone (1) was identified as most active compound with antiaggregation activity (46% inhibition of fluorescence intensity at 50 μM), which was supported by TEM data. The in silico steric zipper model of the tau-derived-hexapeptide 306VQIVYK311 indicates that the 3,4-dihydroxy-substituent present in tolcapone (1) and entacapone (2) underwent polar contacts with lysine side chains (VQIVYK), whereas the charged 5-nitrosubstituent was in close contact with lysine side chain present in the steric zipper region suggesting the critical role of a nitrocatechol (3,4-dihydroxy-5-nitrophenyl) pharmacophore present in tolcapone (1) and entacapone (2) in tau-hexapeptide binding and prevention of β-sheet assembly. Our results have significant implications in the design and development of tau-aggregation inhibitors.
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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".