Split Chloramphenicol Acetyl-Transferase Assay Reveals Self-Ubiquitylation-Dependent Regulation of UBE3B
Bibliographic record
Abstract
Split reporter protein-based genetic section systems are widely used to identify and characterize protein-protein interactions (PPI). The assembly of split markers that antagonize toxins, rather than required for synthesis of missing metabolites, facilitates the seeding of high density of cells and selective growth. Here we present a newly developed split chloramphenicol acetyltransferase (split-CAT) -based genetic selection system. The N terminus fragment of CAT is fused downstream of the protein of interest and the C terminus fragment is tethered upstream to its postulated partner. We demonstrate the system's advantages for the study of PPIs. Moreover, we show that co-expression of a functional ubiquitylation cascade where the target and ubiquitin are tethered to the split-CAT fragments results in ubiquitylation-dependent selective growth. Since proteins do not have to be purified from the bacteria and due to the high sensitivity of the split-CAT reporter, detection of challenging protein cascades and post-translation modifications is enabled. In addition, we demonstrate that the split-CAT system responds to small molecule inhibitors and molecular glues (GLUTACs). The absence of ubiquitylation-dependent degradation and deubiquitylation in E. coli significantly simplify the interpretation of the results. We harnessed the developed system to demonstrate that like NEDD4, UBE3B also undergoes self-ubiquitylation-dependent inactivation. We show that self-ubiquitylation of UBE3B on K665 induces oligomerization and inactivation in yeast and mammalian cells respectively. Finally, we showcase the advantages of split-CAT in the study of human diseases by demonstrating that mutations in UBE3B that cause Kaufman oculocerebrofacial syndrome exhibit clear E. coli growth phenotypes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".