Mapping Protein-Protein Interactions Using Data-Dependent Acquisition Without Dynamic Exclusion
Bibliographic record
Abstract
Abstract Systematic analysis of affinity-purified samples by liquid chromatography coupled to mass spectrometry (LC-MS) requires high coverage, reproducibility, and sensitivity. Data-independent acquisition (DIA) approaches improve the reproducibility of protein-protein interaction detection by alleviating the stochasticity of data-dependent acquisition (DDA). However, the need for library generation and lack of multiplexing capabilities reduces their throughput, and analysis pipelines are still being optimized. In previous work using cell lysates, a fast MS/MS acquisition method with no dynamic exclusion (noDE) provided a comparable number of identifications and more accurate MS/MS intensity-based quantification than an optimized DDA method with dynamic exclusion (DE). Here, we have further optimized the noDE strategy for the analysis of protein-protein interactions and show that it provides better sensitivity and identifies more high confident interactors than the optimized DDA with DE and DIA approaches. TOC
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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) | 0.000 | 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 | 0.000 | 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".