Delivery of <scp>AKT1</scp> phospho‐forms to human cells reveals differential substrate selectivity
Bibliographic record
Abstract
Abstract Protein kinase B (AKT1) is a serine/threonine kinase that regulates fundamental cellular processes, including cell survival, proliferation, and metabolism. AKT1 activity is controlled by two regulatory phosphorylation sites (Thr308, Ser473) that stimulate a downstream signaling cascade through phosphorylation of many target proteins. At either or both regulatory sites, hyperphosphorylation is associated with poor survival outcomes in many human cancers. Our previous biochemical and chemoproteomic studies showed that the phosphorylated forms of AKT1 have differential selectivity toward peptide substrates. Here, we investigated AKT1‐dependent activity in human cells, using a cell‐penetrating peptide (transactivator of transcription, TAT) to deliver inactive AKT1 or active phospho‐variants to cells. We used enzyme engineering and genetic code expansion relying on a phosphoseryl‐transfer RNA (tRNA) synthetase (SepRS) and tRNA Sep pair to produce TAT‐tagged AKT1 with programmed phosphorylation at one or both key regulatory sites. We found that all TAT‐tagged AKT1 variants were efficiently delivered into human embryonic kidney (HEK 293T) cells and that only the phosphorylated AKT1 (pAKT1) variants stimulated downstream signaling. All TAT‐pAKT1 variants induced glycogen synthase kinase (GSK)‐3α phosphorylation, as well as phosphorylation of ribosomal protein S6 at Ser240/244, demonstrating stimulation of downstream AKT1 signaling. Fascinatingly, only the AKT1 variants phosphorylated at S473 (TAT‐pAKT1 S473 or TAT‐pAKT1 T308,S473 ) were able to increase phospho‐GSK‐3β levels. Although each TAT‐pAKT1 variant significantly stimulated cell proliferation, cells transduced with TAT‐pAKT1 T308 grew significantly faster than with the other pAKT1 variants. The data demonstrate differential activity of the AKT1 phospho‐forms in modulating downstream signaling and proliferation in human cells.
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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) | 0.001 | 0.000 |
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".