Optimizing proximity proteomics on the EvoSep-timsTOF LC-MS system
Bibliographic record
Abstract
Proximity-dependent biotinylation (PDB) is a powerful means of exploring the cellular environments in which proteins reside. Expressing a protein of interest (bait) fused to a biotin ligase and adding biotin induces the covalent biotinylation of proximal partners, which are recovered on streptavidin beads and identified by MS. However, a major technical limitation of PDB is peptide carryover into subsequent MS runs. This is mitigated via lengthy inter-sample washing, considerably lowering throughput. This study aims to optimize PDB sample acquisition using an EvoSep LC system coupled to a timsTOF mass spectrometer, which has higher throughput and sensitivity than our current system, with less carryover. Our efforts resulted in an ~15-fold increase in throughput using the 60 samples-per-day (SPD) gradient with better sensitivity, identifying nearly double the proteins found by our previously standardized workflow. Significance scoring also revealed more sensitive detection of high-confidence proximal interactions (~1.5-fold) for five well-characterized baits, validating the new experimental workflow. Importantly, carryover was extremely limited, even without inter-sample washing, and limited to easily filterable abundant proteins. Without washing, this method can process 60 samples per day, using 1/16th of the sample amount previously required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.003 |
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".