Multifaceted targeted protein degradation systems for different cellular compartments
Bibliographic record
Abstract
Selective protein degradation maintains cellular homeostasis, but this process is disrupted in many diseases. Targeted protein degradation (TPD) approaches, built upon existing cellular mechanisms, are promising methods for therapeutically regulating protein levels. Here, we review the diverse palette of tools that are now available for doing so throughout the gene expression pathway and in specific cellular compartments. These include methods for directly removing targeted proteins via the ubiquitin proteasome system with proteolysis targeting chimeras (PROTACs) or dephosphorylation targeting chimeras (DEPTACs). Similar effects can also be achieved through the lysosomal system with autophagy-targeting chimeras (AUTACs), autophagosome tethering compounds (ATTECs), and lysosome targeting chimeras (LYTACs). Other methods act upstream to degrade RNAs (ribonuclease targeting chimeras; RIBOTACs) or transcription factors (transcription factor targeting chimeras; TRAFTACs), offering control throughout the gene expression process. We highlight the evolution and function of these methods and discuss their clinical implications in diverse disease contexts.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".