Advances in protein complex analysis using 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
Proteins often function as components of larger complexes to perform a specific function, and formation of these complexes may be regulated. For example, intracellular signalling events often require transient and/or regulated protein-protein interactions for propagation, and protein binding to a specific DNA sequence, RNA molecule or metabolite is often regulated to modulate a particular cellular function. Thus, characterizing protein complexes can offer important insights into protein function. This review describes recent important advances in mass spectrometry (MS)-based techniques for the analysis of protein complexes. Following brief descriptions of how proteins are identified using MS, and general protein complex purification approaches, we address two of the most important issues in these types of studies: specificity and background protein contaminants. Two basic strategies for increasing specificity and decreasing background are presented: whereas (1) tandem affinity purification (TAP) of tagged proteins of interest can dramatically improve the signal-to-noise ratio via the generation of cleaner samples, (2) stable isotopic labelling of proteins may be used to discriminate between contaminants and bona fide binding partners using quantitative MS techniques. Examples, as well as advantages and disadvantages of each approach, are presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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