Structural Implications of STAT3 and STAT5 SH2 Domain Mutations
Bibliographic record
Abstract
Src Homology 2 (SH2) domains arose within metazoan signaling pathways and are involved in protein regulation of multiple pleiotropic cascades. In signal transducer and activator of transcription (STAT) proteins, SH2 domain interactions are critical for molecular activation and nuclear accumulation of phosphorylated STAT dimers to drive transcription. Sequencing analysis of patient samples has revealed the SH2 domain as a hotspot in the mutational landscape of STAT proteins although the functional impact for the vast majority of these mutations remains poorly characterized. Despite several well resolved structures for SH2 domain-containing proteins, structural data regarding the distinctive STAT-type SH2 domain is limited. Here, we review the unique features of STAT-type SH2 domains in the context of all currently reported STAT3 and STAT5 SH2 domain clinical mutations. The genetic volatility of specific regions in the SH2 domain can result in either activating or deactivating mutations at the same site in the domain, underscoring the delicate evolutionary balance of wild type STAT structural motifs in maintaining precise levels of cellular activity. Understanding the molecular and biophysical impact of these disease-associated mutations can uncover convergent mechanisms of action for mutations localized within the STAT SH2 domain to facilitate the development of targeted therapeutic interventions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".