On-line strong cation exchange μ-HPLC-ESI-MS/MS for protein identification and process optimization
Bibliographic record
Abstract
We have developed an on-line strong cation exchange (SCX)-ESI-MS/MS platform for the rapid identification of proteins contained in mixtures. This platform consists of a SCX precolumn followed by a nanoflow SCX column on-line with an electrospray ion trap mass spectrometer. We also used this platform to study the dynamics of peptide separation/extraction by SCX, in particular to understand the parameters affecting the performance of SCX in multidimensional chromatography. For example, we have demonstrated that the buffer typically used for tryptic digestion of protein mixtures can have a detrimental effect on the chromatographic behaviour of peptides during SCX separations, thereby affecting certain peptide quantitation approaches that rely on reproducible peptide fractionation. We have also demonstrated that band broadening results when a step (discontinuous) gradient approach is used to displace peptides from the SCX precolumn, reducing the separation power of SCX in multidimensional chromatography. In contrast, excellent chromatographic peak shapes are observed when a defined (continuous) gradient is used. Finally, using a tryptic digest of a protein extract derived from human K562 cells, we observed that larger molecular weight peptides are identified using this on-line SCX approach compared to the more conventional reverse phase (RP) LC/MS approach. Both methods used in tandem complement each other and can lead to a greater number of peptide identifications from a given sample.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".