Development of a <i>De Novo</i> Protein Binder that Inhibits the Alpha Kinase eEF2K
Bibliographic record
Abstract
SUMMARY Elongation factor 2 kinase (eEF2K) is calmodulin activated and phosphorylates eEF2, a GTPase, that regulates global translation. When eEF2K phosphorylates eEF2, protein translation is halted. This process may be critical to studying how diseases like cancer dysregulate protein synthesis. eEF2K is an alpha kinase and not targeted by conventional kinase inhibitors. Traditional methods of structure-based drug design are incredibly time consuming and expensive, which may involve screening large libraries of small molecules. We have generated de novo small binder proteins (∼10kDa) - using RFDiffusion and ProteinMPNN. One promising de novo binder protein we produced, ‘CAM1’ binds to a hydrophobic patch on the calmodulin binding domain of eEF2K with nanomolar affinity as determined by isothermal titration calorimetry. This binder, in vitro , significantly reduces eEF2 peptide phosphorylation, comparable to the gold-standard small molecule eEF2K inhibitor, A-484954. The predicted structure of CAM1 is a helical bundle which has been confirmed by circular dichroism spectroscopy. Impressively, CAM1 has a melting temperature >80 0 C, and is produced recombinantly in bacteria, greater than 5 mg / culture liter. We have also determined that CAM1 transfection significantly reduces mammalian HeLa cell proliferation comparable to A-484954 treatment and inhibits the phosphorylation of eEF2. Our de novo binder, the first to our knowledge to inhibit an alpha kinase, and the first non-competitive eEF2K inhibitor, establishes an alternative method of targeting atypical kinase activity.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".