Intact proteome fractionation strategies compatible with mass spectrometry
Bibliographic record
Abstract
Proteome fractionation refers to separation at the level of intact proteins. Proteome fractionation may precede sample digestion and subsequent peptide-level separation and detection (i.e., bottom-up mass spectrometry [MS]). For top-down MS, proteome fractionation acts as a stand-alone separation platform, since intact proteins are directly analyzed by the mass spectrometer. Regardless of the MS identification strategy, separation of intact proteins has clear benefits as a result of decreasing sample complexity. However, this stage of the workflow also creates considerable challenges, which are generally absent from the counterpart peptide separation experiment. For example, maintaining protein solubility is a key concern before, during and after separation. To this end, surfactants such as sodium dodecyl sulfate may be employed during fractionation, so long as they are eliminated prior to MS. In this article, current strategies for proteome fractionation in a MS-compatible format are reviewed, illustrating the challenges and outlooks on this important aspect of proteomics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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".