A single-chain antibody-based AID2 system for conditional degradation of GFP-tagged and untagged proteins
Bibliographic record
Abstract
Abstract Protein knockdown using an improved auxin-inducible degron (AID2) technology has proven to be a powerful tool for studying protein function. The current approach requires the fusion of target proteins with a degron tag, a process typically achieved through CRISPR knock-in. However, knock-in remains challenging in non-model organisms and humans, limiting the broader applicability of AID2. To overcome this limitation, we developed a single-chain antibody AID2 (scAb-AID2) system. This approach employs an adaptor composed of a single-chain antibody fused with a degron, which recognises a target protein and induces rapid degradation in the presence of the inducer 5-Ph-IAA. We demonstrated that scAb-AID2, in combination with an anti-GFP nanobody, degraded GFP-fused proteins in human cells and C. elegans . Furthermore, we showed that endogenous p53 and H/K-RAS were conditionally degraded in cells expressing an adaptor encoding an anti-p53 nanobody and -RAS monobody, respectively, and led to aphidicolin sensitivity in cell culture and growth inhibition in mouse xenografts. This study paves the way for broader application of AID2-based target depletion in model and non-model organisms and for advancing therapeutic strategies.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".