Insights on the Binding of Thioflavin Derivative Markers to Amyloid-Like Fibril Models from Quantum Chemical Calculations
Bibliographic record
Abstract
Thioflavin-T (ThT) is one of the most widely used dyes for staining and identifying amyloid fibrils, which share a common parallel in register β-sheet structure. Unfortunately, ThT is a charged molecule, which limits its ability to cross the blood brain barrier and its use as an efficient dye for in vivo detection of amyloid fibrils. For this reason, several uncharged ThT derivatives have been designed and their binding properties to Aβ fibrils studied by fluorescence assays. However, there are still many unknowns on the binding mechanism and the role of noncovalent interactions on the affinity of these ligands toward β-sheet structures. The present contribution analyzes the binding of ThT (1) and neutral ThT derivatives (2-7) to a β-sheet model by means of quantum chemical B3LYP-D calculations and including solvent effects with the continuum CPCM method. Results show that, in all cases, ligand binding is mainly driven by dispersion interactions. In addition, ligands with -NH groups display hydrogen bond interactions with CO groups of the peptide strand, increasing the intrinsic affinity toward the β-sheet surface. Solvent effects notably reduce the affinity of charged ThT, as compared to neutral systems, due to its larger solvation energy. As a result, neutral derivatives display significantly higher affinities than ThT in solution, in agreement with experimental observations. Analysis of the hydrogen bonding network of the β-sheet structure indicates that stacking interactions upon ligand binding induce a shortening of interstrand hydrogen bonding, suggesting a strengthening of the β-sheet.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".