SILAC kinase screen identifies potential MASTL substrates
Bibliographic record
Abstract
Microtubule-associated serine/threonine kinase-like (MASTL) has emerged as a critical regulator of mitosis and as a potential oncogene in a variety of cancer types. To date, Arpp-19/ENSA are the only known substrates of MASTL. However, with the roles of MASTL expanding and increased interest in development of MASTL inhibitors, it has become critical to determine if there are additional substrates and what the optimal consensus motif for MASTL is. Here we utilized a whole cell lysate in vitro kinase screen combined with stable isotope labelling of amino acids in cell culture (SILAC) to identify potential substrates and the residue preference of MASTL. Using the related AGC kinase family members AKT1/2, the kinase screen identified several known and new substrates highly enriched for the validated consensus motif of AKT. Applying this method to MASTL identified 59 phospho-sites on 67 proteins that increased in the presence of active MASTL. Subsequent in vitro kinase assays suggested that MASTL may phosphorylate hnRNPM, YB1 and TUBA1C under certain in vitro conditions. Taken together, these data suggest that MASTL may phosphorylate several additional substrates, providing insight into the ever-increasing biological functions and roles MASTL plays in driving cancer progression and therapy resistance.
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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".