Nonretentive Solid-Phase Extraction of Phosphorylated Peptides from Complex Peptide Mixtures for Detection by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry
Bibliographic record
Abstract
Widespread interest in protein phosphorylation has led to the development of a variety of methods for the analysis of phosphoproteomes of different types of organisms. Many applications involve pretreatment of the sample before mass spectrometric measurement and can crucially improve the detection efficiency of individual phosphopeptides. Despite intense research efforts, separation and extraction of phosphorylated peptides, especially multiphosphorylated ones, remain challenging tasks and need to be further explored and expanded with unconventional approaches. In this study, we describe the application of nonretentive solid-phase extraction (SPE) to the analysis of phosphopeptides using the highly cross-linked polystyrene-divinylbenzene material Strata-X. This study indicates that the procedure allows for the preferential extraction of phosphopeptides regardless of their extent of phosphorylation. The Strata-X material primarily retains nonphosphorylated peptides by hydrophobic interaction, whereas the inherent hydrophilicity of phosphorylated peptides leads to their partitioning into the aqueous phase. Phosphopeptides that were rapidly segregated out of tryptic digest mixtures and collected in the early aqueous fractions generated intense signals in mass spectra. The method was developed using SPE Strata-X columns, then suited for detection and sequencing of phosphopeptides by miniaturizing the system to the scale of custom-made microcolumns. This provided fast isolation of phosphopeptides from protein digests along with direct MALDI on-target deposition. The possibility of on-target washing during sample preparation is also presented.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".