Correctors of Protein Trafficking Defects Identified by a Novel High‐Throughput Screening Assay
Why this work is in the frame
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Bibliographic record
Abstract
High-throughput small-molecule screens hold great promise for identifying compounds with potential therapeutic value in the treatment of protein-trafficking diseases such as cystic fibrosis (CF) and nephrogenic diabetes insipidus (NDI). The approach usually involves expressing the mutant form of the gene in cells and assaying function in a multiwell format when cells are exposed to libraries of compounds. Although such functional assays are useful, they do not directly test the ability of a compound to correct defective trafficking of the protein. To address this we have developed a novel corrector-screening assay for CF, in which the appearance of the mutant protein at the cell surface is measured. We used this assay to screen a library of 2000 compounds and have isolated several classes of trafficking correctors that had not previously been identified. This novel screening approach to protein-trafficking diseases is robust and general, and could enable the selection of molecules that could be translated rapidly to a clinical setting.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it