A Comprehensive Immunoreceptor Phosphotyrosine-based Signaling Network Revealed by Reciprocal Protein–Peptide Array Screening
Bibliographic record
Abstract
Cells of the immune system communicate with their environment through immunoreceptors. These receptors often harbor intracellular tyrosine residues, which, when phosphorylated upon receptor activation, serve as docking sites to recruit downstream signaling proteins containing the Src Homology 2 (SH2) domain. A systematic investigation of interactions between the SH2 domain and the immunoreceptor tyrosine-based regulatory motifs (ITRM), including inhibitory (ITIM), activating (ITAM), or switching (ITSM) motifs, is critical for understanding cellular signal transduction and immune function. Using the B cell inhibitory receptor CD22 as an example, we developed an approach that combines reciprocal or bidirectional phosphopeptide and SH2 domain array screens with in-solution binding assays to identify a comprehensive SH2-CD22 interaction network. Extending this approach to 194 human ITRM sequences and 78 SH2 domains led to the identification of a high-confidence immunoreceptor interactome containing 1137 binary interactions. Besides recapitulating many previously reported interactions, our study uncovered numerous novel interactions. The resulting ITRM-SH2 interactome not only helped to fill many gaps in the immune signaling network, it also allowed us to associate different SH2 domains to distinct immune functions. Detailed analysis of the NK cell ITRM-mediated interactions led to the identification of a network nucleated by the Vav3 and Fyn SH2 domains. We showed further that these SH2 domains have distinct functions in cytotoxicity. The bidirectional protein-peptide array approach described herein may be applied to the numerous other peptide-binding modules to identify potential protein-protein interactions in a systematic and reliable manner.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".