Phosphorylation as a candidate regulatory mechanism for effector recruitment to tankyrase
Bibliographic record
Abstract
Unprocessed, original data for Broadway et al., 2025 Phosphorylation as a candidate regulatory mechanism for effector recruitment to tankyrase Benjamin J Broadway1,2 Katie Pollock1,2,4, Nora Cronin1,3, Robert Rottapel5,6,7, Frank Sicheri8,9, Sebastian Guettler1,2✉ 1Division of Structural Biology, The Institute of Cancer Research (ICR), London, UK. 2Division of Cancer Biology, The Institute of Cancer Research (ICR), London, UK. 3Present affiliation: The Francis Crick Institute, London, UK. 4Present affiliation: Cancer Research Horizons, CRUK Scotland Institute, Glasgow, UK. 5Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada. 6Departments of Medicine, Medical Biophysics and Immunology, University of Toronto, Toronto, Ontario, Canada. 7Division of Rheumatology, St. Michael's Hospital, Toronto, Ontario, Canada. 8The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada. 9Departments of Molecular Genetics and Biochemistry, University of Toronto, Ontario, Canada. Summary: The TNKS and TNKS2 enzymes, collectively termed tankyrase, contribute to various cell functions, such as cell signalling, regulating glucose levels and maintaining chromosome ends. Tankyrase recruits its binding partners via a short peptide sequence called the tankyrase-binding motif (TBM), which binds to repeating portions of tankyrase known as ankyrin repeat clusters (ARCs). However, it is not yet clear if this binding event can be regulated. Our study suggests that adding a phosphate group to the eighth amino acid in the TBM, a reaction accomplished by protein kinases, can enhance the binding between tankyrase’s ARC domains and a subset of binding partners. By interrogating the human proteome, we find evidence that many binding partners can be phosphorylated at position eight in their TBMs. Overall, this suggests that phosphorylation of TBMs could be a way to specifically recruit binding partners to tankyrase, or stabilise their interaction with tankyrase, providing a novel means to control tankyrase function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".