Proximity-dependent biotinylation mediated by TurboID to identify protein–protein interaction networks in yeast
Bibliographic record
Abstract
The use of proximity-dependent biotinylation assays coupled to mass spectrometry (PDB-MS) has changed the field of protein-protein interaction studies. However, despite the recurrent and successful use of BioID-based protein-protein interactions screening in mammalian cells, the implementation of PDB-MS in yeast has not been effective. Here, we report a simple and rapid approach in yeast to effectively screen for proximal and interacting proteins in their natural cellular environment by using TurboID, a recently described version of the BirA biotin ligase. Using the protein arginine methyltransferase Rmt3 and the RNA exosome subunits, Rrp6 and Dis3, the application of PDB-MS in yeast by using TurboID was able to recover protein-protein interactions previously identified using other biochemical approaches and provided new complementary information for a given protein bait. The development of a rapid and effective PDB assay that can systematically analyze protein-protein interactions in living yeast cells opens the way for large-scale proteomics studies in this powerful model organism.
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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 | 0.001 | 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) | 0.001 | 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".