Optimal selection of 2D reversed-phase–reversed-phase HPLC separation techniques in bottom-up proteomics
Bibliographic record
Abstract
Evaluation of: Stephanowitz H, Lange S, Lang D et al. Improved two-dimensional reversed phase–reversed phase LC-MS/MS approach for identification of peptide–protein interactions. J. Proteome Res. 11(2), 1175–1183 (2011).Recent developments in bottom-up proteomics have supplanted the use of gel-based approaches in favor of multidimensional chromatographic separations of peptide mixtures followed by mass spectrometry analysis. This trend is driven by the desire to eliminate labor-intensive in-gel digestion procedures and increase proteome coverage through better recovery of proteolytic fragments. Introduction of reversed-phase–reversed-phase 2D separation techniques is one of the major improvements that have made this possible. In this article, we review recent developments in 2D HPLC and highlight variations in reversed-phase HPLC separation selectivity that allow for superior peak capacity in peptide fractionation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".