Structural determinants of the interactions of catechins with Aβ oligomers and lipid membranes
Bibliographic record
Abstract
The aberrant self-assembly of intrinsically disordered proteins (IDPs) into soluble oligomers and their interactions with biological membranes underlie the pathogenesis of numerous neurodegenerative diseases, including Alzheimer's disease. Catechins have emerged as useful tools to reduce the toxicity of IDP oligomers by modulating their interactions with membranes. However, the structural determinants of catechin binding to IDP oligomers and membranes remain largely elusive. Here, we assemble a catechin library by combining several naturally occurring chemical modifications and, using a coupled NMR-statistical approach, we map at atomic resolution the interactions of such library with the Alzheimer's-associated amyloid-beta (Aβ) oligomers and model membranes. Our results reveal multiple catechin affinity drivers and show that the combination of affinity-reducing covalent changes may lead to unexpected net gains in affinity. Interestingly, we find that the positive cooperativity is more prevalent for Aβ oligomers than membrane binding, and that the determinants underlying catechin recognition by membranes are markedly different from those dissected for Aβ oligomers. Notably, we find that the unanticipated positive cooperativity arises from the critical regulatory role of the gallate catechin moiety, which recruits previously disengaged substituents into the binding interface and leads to an overall greater compaction of the receptor-bound conformation. Overall, the previously elusive structural attributes mapped here provide an unprecedented foundation to establish structure-activity relationships of catechins.
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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) | 0.000 | 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".