Signal transducer and activator of transcription 3 (STAT3) inhibitor, S3I-201, acts as a potent and non-selective alkylating agent
Bibliographic record
Abstract
// Daniel P. Ball 1, * , Andrew M. Lewis 1, * , Declan Williams 2, 3 , Diana Resetca 4 , Derek J. Wilson 2, 3 , Patrick T. Gunning 4 1 Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario, L5L 1C6, Canada 2 Department of Chemistry, York University, Toronto, Ontario, M3J 1P3, Canada 3 Department of Chemistry, Center for Research in Mass Spectrometry, York University, Toronto, Ontario, M3J 1P3, Canada 4 Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5G 1L, Canada * These authors have contributed equally to this work Correspondence to: Patrick T. Gunning, e-mail: patrick.gunning@utoronto.ca Keywords: STAT3, S3I-201, NSC 74859, covalent modification, oncology Received: October 15, 2015 Accepted: January 29, 2016 Published: March 02, 2016 ABSTRACT The Signal Transducer and Activator of Transcription 3 (STAT3) oncogene is a master regulator of many human cancers, and a well-recognized target for therapeutic intervention. A well known STAT3 inhibitor, S3I-201 (NSC 74859), is hypothesized to block STAT3 function in cancer cells by binding the STAT3 SH2 domain and disrupt STAT3 protein complexation events. In this study, liquid chromatography tandem mass spectrometry analysis revealed that STAT3, in the presence of S3I-201, showed a minimum of five specific sites of modification, cysteine’s 108, 259, 367, 542, and 687. Moreover, a prepared fluorescently labeled chemical probe of S3I-201 (DB-6-055) revealed that S3I-201 non-specifically and globally alkylated intracellular proteins at concentrations consistent with S3I-201’s reported IC 50 . These data are consistent with the hypothesis that S3I-201 is a sub-optimal probe for interrogating STAT3-related cell biology.
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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) | 0.002 | 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".