High pH reversed‐phase chromatography as a superior fractionation scheme compared to off‐gel isoelectric focusing for complex proteome analysis
Bibliographic record
Abstract
MS/MS is the technology of choice for analyzing complex protein mixtures. However, due to the intrinsic complexity and dynamic range present in higher eukaryotic proteomes, prefractionation is an important step to maximize the number of proteins identified. Off-gel IEF (OG-IEF) and high pH RP (Hp-RP) column chromatography have both been successfully utilized as a first-dimension peptide separation technique in shotgun proteomic experiments. Here, a direct comparison of the two methodologies was performed on ex vivo peripheral blood mononuclear cell lysate. In 12-fraction replicate analysis, Hp-RP resulted in more peptides and proteins identified than OG-IEF fractionation. Distributions of peptide pIs and hydropathy did not reveal any appreciable bias in either technique. Resolution, defined here as the ability to limit a specific peptide to one particular fraction, was significantly better for Hp-RP. This leads to a more uniform distribution of total and unique peptides for Hp-RP across all fractions collected. These results suggest that fractionation by Hp-RP over OG-IEF is the better choice for typical complex proteome analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".