Proximity‐Dependent Biotinylation for Identification of Interacting Proteins
Bibliographic record
Abstract
Complex interaction networks orchestrate key cellular processes including but not limited to transcription, translation, metabolism, and cell signaling. Delineating these interactions will aid in deciphering the regulation and function of these pathways and potential for manipulation. Proximity-dependent biotin identification (BioID) is quickly gaining popularity as a powerful tool for identifying novel protein-protein and proximity-based interactions in live cells. This technique relies on a promiscuous biotin ligase, which is fused to a protein of interest and, upon expression in the desired cell, will biotinylate proximal endogenous proteins. In vivo protein-protein interactions can be very transient and occur momentarily to facilitate signaling or a metabolic function. BioID is useful in identifying these weak and/or transient interactions that are not detected by traditional methods such as yeast two-hybrid or affinity purification. Here, we outline a BioID protocol that can be used as a workflow to guide a new application. © 2016 by John Wiley & Sons, Inc.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".