Protein Trafficking Diseases: Small Molecule Approaches
Bibliographic record
Abstract
Abstract Many diseases are caused by defects in protein trafficking. Protein trafficking diseases occur when a mutant protein is recognized by the endoplasmic reticulum (ER) quality control system (ERQC), retained in the ER, and degraded in the cytosol by the proteasome rather than being trafficked to its correct site of action. Among these diseases are cystic fibrosis, lysosomal storage diseases (Fabry, Gaucher, and Tay‐Sachs), nephrogenic diabetes insipidus, oculocutaneous albinism, protein C deficiency, and many others. A characteristic of many of these diseases is that the mutant protein remains functional, but it cannot escape the stringent ER quality‐control machinery, and it is retained in the ER. This characteristic suggests that pharmacological interventions that promote the correct folding of the mutant protein would enable its escape from the ER and ameliorate the symptoms of the disease. In this review, we focus on specific examples of protein trafficking diseases in pharmacological or chemical chaperones have been shown to rescue trafficking of the mutant protein.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".