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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".