Development of Online pH Gradient-Eluted Strong Cation Exchange Nanoelectrospray-Tandem Mass Spectrometry for Proteomic Analysis Facilitating Basic and Histidine-Containing Peptides Identification
Bibliographic record
Abstract
A novel one-dimensional online pH gradient-eluted strong cation exchange-nanoelectrospray ionization-tandem mass spectrometry (SCX-nano-ESI-MS/MS) method was developed for protein identification and tested with a mixture of six standard proteins, total lysate of HuH7 and N2a cells, as well as membrane fraction of N2a cells. This method utilized an online nanoflow SCX column in a nano-LC system coupled with a nanoelectrospray high-resolution mass spectrometer. Protein digests were separated on a nanoflow SCX column with a pH gradient and directly introduced into a mass spectrometer through nanoelectrospray ionization. More than five thousand unique peptides were identified in each 90 min LC-MS/MS run using 500 nanogram of protein digest either from total cell lysate or from membrane fraction. The unique peptide overlap between online strong cation exchange nano-ESI-MS/MS (SCXLC-MS/MS) and reverse phase nano-ESI-MS/MS (RPLC-MS/MS) is only ≤30%, which indicated these two methods were complementary to each other. The correlation coefficient of retention time and theoretical isoelectric point (pI) of identified peptides in SCXLC-MS/MS was higher than 0.4, which showed that peptides elution in SCXLC-MS/MS was dependent on their charge states. Furthermore, SCXLC-MS/MS showed identification capability for a higher proportion of basic peptides compared to the RPLC-MS/MS method, especially for histidine-containing peptides. Our SCXLC-MS/MS method is an excellent alternative method to the RPLC-MS/MS method for analysis of standard proteins, total cell and membrane proteomes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".