Posttranslational protein knockdown coupled to receptor tyrosine kinase activation with phosphoPROTACs
Bibliographic record
Abstract
Posttranslational knockdown of a specific protein is an attractive approach for examining its function within a system. Here we introduce phospho-dependent proteolysis targeting chimeras (phosphoPROTACs), a method to couple the conditional degradation of targeted proteins to the activation state of particular kinase-signaling pathways. We generated two phosphoPROTACs that couple the tyrosine phosphorylation sequences of either the nerve growth factor receptor, TrkA (tropomyosin receptor kinase A), or the neuregulin receptor, ErbB3 (erythroblastosis oncogene B3), with a peptide ligand for the E3 ubiquitin ligase von Hippel Lindau protein. These phosphoPROTACs recruit either the neurotrophic signaling effector fibroblast growth factor receptor substrate 2α or the survival-promoting phosphatidylinositol-3-kinase, respectively, to be ubiquitinated and degraded upon activation of specific receptor tyrosine kinases and phosphorylation of the phosphoPROTACs. We demonstrate the ability of these phosphoPROTACs to suppress the short- and long-term effects of their respective activating receptor tyrosine kinase pathways both in vitro and in vivo. In addition, we show that activation of phosphoPROTACs is entirely dependent on their kinase-mediated phosphorylation, as phenylalanine-containing null variants are inactive. Furthermore, stimulation of unrelated growth factor receptors does not induce target protein knockdown. Although comparable in efficiency to RNAi, this approach has the added advantage of providing a degree of temporal and dosing control as well as cell-type selectivity unavailable using nucleic acid-based strategies. By varying the autophosphorylation sequence of a phosphoPROTAC, it is conceivable that other receptor tyrosine kinase/effector pairings could be similarly exploited to achieve other biological effects.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".