Computational design and evaluation of optimal bait sets for scalable proximity proteomics
Bibliographic record
Abstract
Abstract The spatial organization of proteins in eukaryotic cells can be explored by identifying nearby proteins using proximity-dependent biotinylation approaches like BioID. BioID defines the localization of thousands of endogenous proteins in human cells when used on hundreds of bait proteins. However, this high bait number restricts the approach’s usage and gives these datasets limited scalability for context-dependent spatial profiling. To make subcellular proteome mapping across different cell types and conditions more practical and cost-effective, we developed a comprehensive benchmarking platform and multiple metrics to assess how well a given bait subset can reproduce an original BioID dataset. We also introduce GENBAIT, which uses a genetic algorithm to optimize bait subset selection, to derive bait subsets predicted to retain the structure and coverage of two large BioID datasets using less than a third of the original baits. This flexible solution is poised to improve the intelligent selection of baits for contextual studies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".