Loops Govern SH2 Domain Specificity by Controlling Access to Binding Pockets
Bibliographic record
Abstract
Cellular functions require specific protein-protein interactions that are often mediated by modular domains that use binding pockets to engage particular sequence motifs in their partners. Yet, how different members of a domain family select for distinct sequence motifs is not fully understood. The human genome encodes 120 Src homology 2 (SH2) domains (in 110 proteins), which mediate protein-protein interactions by binding to proteins with diverse phosphotyrosine (pTyr)-containing sequences. The structure of the SH2 domain of BRDG1 bound to a peptide revealed a binding pocket that was blocked by a loop residue in most other SH2 domains. Analysis of 63 SH2 domain structures suggested that the SH2 domains contain three binding pockets, which exhibit selectivity for the three positions after the pTyr in a peptide, and that SH2 domain loops defined the accessibility and shape of these pockets. Despite sequence variability in the loops, we identified conserved structural features in the loops of SH2 domains responsible for controlling access to these surface pockets. We engineered new loops in an SH2 domain that altered specificity as predicted. Thus, selective blockage of binding subsites or pockets by surface loops provides a molecular basis by which the diverse modes of ligand recognition by the SH2 domain may have evolved and provides a framework for engineering SH2 domains and designing SH2-specific inhibitors.
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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".