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.
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 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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".