Quantitative Analysis of Proteome Coverage and Recovery Rates for Upstream Fractionation Methods in Proteomics
Bibliographic record
Abstract
The proteome of any cell or even any subcellular fraction remains too complex for complete analysis by one dimension of liquid chromatography-tandem mass spectrometry (LC-MS/MS). Hence, to achieve greater depth of coverage for a proteome of interest, most groups routinely subfractionate the sample prior to LC-MS/MS so that the material entering LC-MS/MS is less complex than the original sample. Protein and/or peptide fractionation methods that biochemists have used for decades, such as strong cation exchange chromatography (SCX), isoelectric focusing (IEF) and SDS-PAGE, are the most common prefractionation methods used currently. There has, as yet, been no comprehensive, controlled evaluation of the relative merits of the various methods, although some binary comparisons have been made. Here, we compare the most popular methods for fractionating samples at both the protein and peptide level, replicating all analyses to provide estimates of the variability in the analyses and controlling precisely for instrument time dedicated to each analysis, as well as directly measuring the recovery of protein or peptide from each fractionation procedure. For maximal proteome coverage, SDS-PAGE is very clearly the most effective method tested, with more than 90% of the entire data set found. When considering the amount of material recovered after each fractionation procedure, solution-based IEF and SCX performed similarly, with approximately 80% of the input being recovered.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".