Optimizing Proximity Proteomics on the EvoSep‐timsTOF LC–MS System
Bibliographic record
Abstract
Proximity-dependent biotinylation (BioID) 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 (preys), which are recovered on streptavidin beads and identified by MS. However, a major technical limitation of BioID is peptide carryover into subsequent MS runs. This is typically mitigated via lengthy intersample wash cycles, which lowers throughput considerably. The aim of this study was to optimize BioID 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 gradient with better sensitivity, and 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 intersample washing, and limited to abundant proteins that are easily filtered during data analyses. Without washing, the newly optimized method can process 60 samples per day, using half of the sample amount previously required. SUMMARY: Proximity-dependent biotinylation (PDB) coupled with MS is a powerful approach to characterize subcellular protein localization. However, the carry-over of peptides from the abundant proteins into subsequent MS runs is problematic. While this was previously mitigated by lengthy wash cycles of the chromatography column, this ultimately lowered throughput. The introduction of the EvoSep chromatography system and more sensitive MS instrumentation has enabled robust and fast analysis with lower sample amounts and minimal carry-over. To date, there has been no systematic evaluation of this EvoSep-timsTOF instrumentation for PDB, nor a direct comparison to a previously standardized workflow. This study compares the identifications between these acquisition setups, recommends sample loading and gradient settings for the EvoSep-timsTOF, and investigates the high-confidence proximal interactors identified. The results highlight the necessity of optimization of scoring approaches for PDB alongside faster MS methods to maximize recovery of known high-confidence proximal interactors. Importantly, the EvoSep-timsTOF system substantially increases the effective throughput of MS acquisition, as washing between samples could be eliminated without compromising the recovery of bona fide proximal interactors, likely due to both carry-over reduction from both the EvoSep chromatography system and the decreased sample load.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 0.002 |
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".