Changes in Signal Transducer and Activator of Transcription 3 (STAT3) Dynamics Induced by Complexation with Pharmacological Inhibitors of Src Homology 2 (SH2) Domain Dimerization
Bibliographic record
Abstract
The activity of the transcription factor signal transducer and activator of transcription 3 (STAT3) is dysregulated in a number of hematological and solid malignancies. Development of pharmacological STAT3 Src homology 2 (SH2) domain interaction inhibitors holds great promise for cancer therapy, and a novel class of salicylic acid-based STAT3 dimerization inhibitors that includes orally bioavailable drug candidates has been recently developed. The compounds SF-1-066 and BP-1-102 are predicted to bind to the STAT3 SH2 domain. However, given the highly unstructured and dynamic nature of the SH2 domain, experimental confirmation of this prediction was elusive. We have interrogated the protein-ligand interaction of STAT3 with these small molecule inhibitors by means of time-resolved electrospray ionization hydrogen-deuterium exchange mass spectrometry. Analysis of site-specific evolution of deuterium uptake induced by the complexation of STAT3 with SF-1-066 or BP-1-102 under physiological conditions enabled the mapping of the in silico predicted inhibitor binding site to the STAT3 SH2 domain. The binding of both inhibitors to the SH2 domain resulted in significant local decreases in dynamics, consistent with solvent exclusion at the inhibitor binding site and increased rigidity of the inhibitor-complexed SH2 domain. Interestingly, inhibitor binding induced hot spots of allosteric perturbations outside of the SH2 domain, manifesting mainly as increased deuterium uptake, in regions of STAT3 important for DNA binding and nuclear localization.
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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".