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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".