Molecular basis of JAK kinase regulation guiding therapeutic approaches: Evaluating the JAK3 pseudokinase domain as a drug target
Bibliographic record
Abstract
Janus kinases (JAK1-3, TYK2) are critical mediators of cytokine signaling and their role in hematological and inflammatory and autoimmune diseases has sparked widespread interest in their therapeutic targeting. JAKs have unique tandem kinase structure consisting of an active tyrosine kinase domain adjacent to a pseudokinase domain that is a hotspot for pathogenic mutations. The development of JAK inhibitors has focused on the active kinase domain and the developed drugs have demonstrated good clinical efficacy but due to off-target inhibition cause also side-effects and carry a black box warning limiting their use. Our understanding of the regulatory function of the pseudokinase domain in physiological and pathological signaling has improved substantially. The pseudokinase domain maintains the inactive state of JAKs in the absence of cytokine stimulation but it has also a key role in physiological and mutation-driven activation process. Furthermore, the pseudokinase domain has favourable structural characteristics for selective targeting of cytokine signaling, such as unique mode of ATP-binding, and the first pseudokinase targeting inhibitor for TYK2 has been approved for clinical use. Here we describe the recent functional and structural knowledge of JAK signaling and their therapeutic targeting, and present data evaluating the druggability of the JAK3 pseudokinase domain. • JAK kinases are important drug targets in inflammatory diseases and hematological malignancies. • Most current JAK inhibitors target conserved kinase domain and lack selectivity limiting their use. • JAK pseudokinase domain has suitable characteristics for selective inhibition by small compounds. • JAK3 pseudokinase domain was found to be a suitable target for therapeutic targeting.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".