On the Mechanism of Nonspecific Inhibitors of Protein Aggregation: Dissecting the Interactions of α-Synuclein with Congo Red and Lacmoid
Bibliographic record
Abstract
Increasing evidence links the misfolding and aberrant self-assembly of proteins with the molecular events that underlie a range of neurodegenerative diseases, yet the mechanistical details of these processes are still poorly understood. The fact that many of these proteins are intrinsically unstructured makes it particularly challenging to develop strategies for discovering small molecule inhibitors of their aggregation. We present here a broad biophysical approach that enables us to characterize the mechanisms of interaction between alpha-synuclein, a protein whose aggregation is closely connected with Parkinson's disease, and two small molecules, Congo red and Lacmoid, which inhibit its fibrillization. Both compounds are found to interact with the N-terminal and central regions of the monomeric protein although with different binding mechanisms and affinities. The differences can be attributed to the chemical nature of the compounds as well as their abilities to self-associate. We further show that alpha-synuclein binding and aggregation inhibition are mediated by small oligomeric species of the compounds that interact with distinct regions of the monomeric protein. These findings provide potential explanations of the nonspecific antiamyloid effect observed for these compounds as well as important mechanistical information for future drug discovery efforts targeting the misfolding and aggregation of intrinsically unstructured proteins.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".