Surface Loops in a Single SH2 Domain Are Capable of Encoding the Spectrum of Specificity of the SH2 Family*
Bibliographic record
Abstract
Src homology 2 (SH2) domains play an essential role in cellular signal transduction by binding to proteins phosphorylated on Tyr residue. Although Tyr phosphorylation (pY) is a prerequisite for binding for essentially all SH2 domains characterized to date, different SH2 domains prefer specific sequence motifs C-terminal to the pY residue. Because all SH2 domains adopt the same structural fold, it is not well understood how different SH2 domains have acquired the ability to recognize distinct sequence motifs. We have shown previously that the EF and BG loops that connect the secondary structure elements on an SH2 domain dictate its specificity. In this study, we investigated if these surface loops could be engineered to encode diverse specificities. By characterizing a group of SH2 variants selected by different pY peptides from phage-displayed libraries, we show that the EF and BG loops of the Fyn SH2 domain can encode a wide spectrum of specificities, including all three major specificity classes ( p + 2, p + 3 and p + 4) of the SH2 domain family. Furthermore, we found that the specificity of a given variant correlates with the sequence feature of the bait peptide used for its isolation, suggesting that an SH2 domain may acquire specificity by co-evolving with its ligand. Intriguingly, we found that the SH2 variants can employ a variety of different mechanisms to confer the same specificity, suggesting the EF and BG loops are highly flexible and adaptable. Our work provides a plausible mechanism for the SH2 domain to acquire the wide spectrum of specificity observed in nature through loop variation with minimal disturbance to the SH2 fold. It is likely that similar mechanisms may have been employed by other modular interaction domains to generate diversity in specificity.
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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 | 0.000 | 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) | 0.001 | 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".