R2HaPpY: Rapid-robust phosphotyrosine peptide enrichment using HaloTag-Src SH2 pY superbinder
Bibliographic record
Abstract
Phosphotyrosine signaling plays a critical role in many biological processes, from cell proliferation to immune response. Despite its importance, systems-level analysis of phosphotyrosine signaling remains a challenge due to costly enrichment reagents and labor-intensive protocols. We previously established an automated phosphotyrosine enrichment method for preparing 96 samples in parallel. Here, we further optimize this method by fusing an SH2 phosphotyrosine superbinder to the HaloTag protein. This allows simple and cost-effective preparation of enrichment beads directly from bacterial lysate, expediting reagent preparation from days to hours. Additionally, our new reagent binds phosphotyrosine peptides at higher efficiency than other enrichment reagents. Using this reagent, we detect and quantify 1,651 unique phosphotyrosine sites from EGF stimulated HeLa cells using only ~1 mg of input peptides per replicate. These include 878 regulated pY sites, many of which are low abundance and not previously detected or annotated as EGF-responsive. This streamlined and sensitive method facilitates comprehensive, quantitative mapping of tyrosine phosphorylation dynamics, enabling broader integration of phosphotyrosine signaling into multiomic and network-level models across diverse biological systems and disease states.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".