Resolving protein interactions and complexes by affinity purification followed by label‐based quantitative mass spectrometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Label-based quantitative mass spectrometry analysis of affinity purified complexes, with its built-in negative controls and relative ease of use, is an increasingly popular choice for defining protein-protein interactions and multiprotein complexes. This approach, which differentially labels proteins/peptides from two or more populations and combines them prior to analysis, permits direct comparison of a protein pulldown (e.g. affinity purified tagged protein) to that of a control pulldown (e.g. affinity purified tag alone) in a single mass spectrometry (MS) run, thus avoiding the variability inherent in separate runs. The use of quantitative techniques has been driven in large part by significant improvements in the resolution and sensitivity of high-end mass spectrometers. Importantly, the availability of commercial reagents and open source identification/quantification software has made these powerful techniques accessible to nonspecialists. Benefits and drawbacks of the most popular labeling-based approaches are discussed here, and key steps/strategies for the use of labeling in quantitative immunoprecipitation experiments detailed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 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 it