Macroporous reversed‐phase separation of proteins combined with reversed‐phase separation of phosphopeptides and tandem mass spectrometry for profiling the phosphoproteome of MDA‐MB‐231 cells
Bibliographic record
Abstract
A new method of combining macroporous RP (mRP) protein fractionation with RPLC peptide separation MS/MS is reported for profiling the phosphoproteome of a complex sample. In this method, an mRP-C18 column was used to fractionate the proteins from a whole cell lysate of a breast cancer cell line, MDA-MB-231, into 38 fractions. Each fraction was subjected to tryptic digestion, sequential phosphopeptide enrichment by immobilized metal ion affinity chromatography and titanium dioxide (TiO2 ), followed by capillary RPLC-MS/MS analysis. For comparison, the conventional method of using strong cation exchange RPLC separation of peptides combined with MS/MS was also used for analyzing the phosphoproteome. Replicate experiments by the mRP-RPLC method identified 1585 distinct phosphoproteins with 4519 phosphopeptides, compared to 1585 phosphoproteins with 4297 phosphopeptides by strong cation exchange RPLC, with a total of 1947 phosphoproteins and 6278 phosphopeptides identified from the combined results. While the two methods have similar ability in the identification of the phosphoproteome, they produce complementary information. The phosphoproteins identified in this study, including 67 novel phosphorylation sites from 56 breast cancer related proteins, can serve as the entry point for future validation with biological implications in breast cancer. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD000948 and DOI 10.6019/PXD000948.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".