Biphasic Affinity Chromatographic Approach for Deep Tyrosine Phosphoproteome Analysis
Bibliographic record
Abstract
Tyrosine phosphorylation (pTyr) is important for normal physiology and implicated in many human diseases, particularly cancer. Identification of pTyr sites is critical to dissecting signaling pathways and understanding disease pathologies. However, compared with serine/threonine phosphorylation (pSer/pThr), the analysis of pTyr at the proteome level is more challenging due to its low abundance. Here, we developed a biphasic affinity chromatographic approach where Src SH2 superbinder was coupled with NeutrAvidin affinity chromatography, for tyrosine phosphoproteome analysis. With the use of competitive elution agent biotin-pYEEI, this strategy can distinguish high-affinity phosphotyrosyl peptides from low-affinity ones, while the excess competitive agent is readily removed by using NeutrAvidin agarose resin in an integrated tip system. The excellent performance of this system was demonstrated by analyzing tyrosine phosphoproteome of Jurkat cells from which 3,480 unique pTyr sites were identified. The biphasic affinity chromatography method for deep Tyr phosphoproteome analysis is rapid, sensitive, robust, and cost-effective. It is widely applicable to the global analysis of the tyrosine phosphoproteome associated with tyrosine kinase signal transduction.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".