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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.005 |
| 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".