A Rapid <i>in vivo</i> Pipeline to Identify Small Molecule Inhibitors of Amyloid Aggregation
Bibliographic record
Abstract
Abstract Amyloids are associated with over 50 human diseases and have inspired significant effort to identify small molecule remedies. Here, we present a novel in vivo platform that efficiently yields small molecule disruptors of amyloid formation. We previously identified small molecules that kill the nematode C. elegans by forming membrane-piercing crystals in the pharynx cuticle, which is rich in amyloid-like material. We show here that many of these molecules are known amyloid-binders whose crystal-formation in the pharynx can be blocked by amyloid-binding dyes. Furthermore, we found that amyloid fibrils can seed small molecule crystal formation in vitro . These observations suggest that small molecule crystals are seeded by the cuticle’s amyloid-like material. We asked whether this phenomenon could be exploited to identify additional molecules that interfere with the ability of amyloids to seed higher-order structures. We screened 2560 compounds and identified 85 crystal suppressors, which we found to be 10-fold enriched in known amyloid disruptors relative to a random set. Of the uncharacterized suppressors, we found 25% to inhibit Ab42 fibril nucleation and/or extension in vitro , which is a hit rate that far exceeds other screening methodologies. Hence, screens for suppressors of crystal formation can efficiently reveal small molecules with amyloid-disrupting potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".