Characterization of PROTAC specificity and endogenous protein interactomes using ProtacID
Bibliographic record
Abstract
Here we describe ProtacID, a flexible BioID (proximity-dependent biotinylation)-based approach to identify PROTAC-proximal proteins in living cells. ProtacID analysis of VHL- and CRBN-recruiting PROTACs targeting a number of different proteins (localized to chromatin or cellular membranes, and tested across six different human cell lines) demonstrates how this technique can be used to validate PROTAC degradation targets and identify non-productive (i.e. non-degraded) PROTAC-interacting proteins, addressing a critical need in the field of PROTAC development. We also demonstrate that ProtacID can be used to characterize native, endogenous multiprotein complexes without the use of antibodies, or modification of the protein of interest with epitope tags or biotin ligase tagging. PROTAC development has surged in popularity, however our ability to characterize PROTAC specificity in living cells has lagged behind. Here, the authors develop ProtacID, a flexible proximity-dependent biotinylation (BioID)-based approach to identify PROTAC-protein interactions in living cells.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".