Resolving protein interactions and complexes by affinity purification followed by label‐based quantitative mass spectrometry
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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".