Benchmarking of proximity-dependent biotinylation enzymes across cellular compartments and time windows
Bibliographic record
Abstract
Abstract Proximity-dependent biotinylation has become a powerful approach for mapping protein interactions and subcellular organization in living cells. Although a growing number of engineered biotin ligases have been introduced, their performance has not been systematically evaluated across diverse cellular contexts. Here, we benchmark ten proximity ligases spanning three bacterial lineages using standardized proteomic workflows across multiple labeling durations, subcellular compartments, and two human cell types. While all enzymes efficiently detect proximal associations, they differ in labeling kinetics, background activity, and spatial specificity. TurboID exhibits the highest overall activity but generates substantial background in standard media. miniTurbo and ultraID support rapid, biotin-dependent labeling with low background, making them better suited for dynamic and time-resolved applications. However, miniTurbo showed aberrant mitochondrial localization with two cytoskeletal baits (VASP and PFN1). Across 15 diverse baits, ultraID consistently provides an excellent combination of specificity, efficiency, and spatial compatibility—including unique recovery of Golgi-resident glycosyltransferases. This study serves as a comparative resource, offering guidance for enzyme selection and experimental design in proximity proteomics.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".