Regulation of mTOR Complex 1 (mTORC1) by Raptor Ser863 and Multisite Phosphorylation
Bibliographic record
Abstract
The rapamycin-sensitive mTOR complex 1 (mTORC1) promotes protein synthesis, cell growth, and cell proliferation in response to growth factors and nutritional cues. To elucidate the poorly defined mechanisms underlying mTORC1 regulation, we have studied the phosphorylation of raptor, an mTOR-interacting partner. We have identified six raptor phosphorylation sites that lie in two centrally localized clusters (cluster 1, Ser(696)/Thr(706) and cluster 2, Ser(855)/Ser(859)/Ser(863)/Ser(877)) using tandem mass spectrometry and generated phosphospecific antibodies for each of these sites. Here we focus primarily although not exclusively on raptor Ser(863) phosphorylation. We report that insulin promotes mTORC1-associated phosphorylation of raptor Ser(863) via the canonical PI3K/TSC/Rheb pathway in a rapamycin-sensitive manner. mTORC1 activation by other stimuli (e.g. amino acids, epidermal growth factor/MAPK signaling, and cellular energy) also promote raptor Ser(863) phosphorylation. Rheb overexpression increases phosphorylation on raptor Ser(863) as well as on the five other identified sites (e.g. Ser(859), Ser(855), Ser(877), Ser(696), and Thr(706)). Strikingly, raptor Ser(863) phosphorylation is absolutely required for raptor Ser(859) and Ser(855) phosphorylation. These data suggest that mTORC1 activation leads to raptor multisite phosphorylation and that raptor Ser(863) phosphorylation functions as a master biochemical switch that modulates hierarchical raptor phosphorylation (e.g. on Ser(859) and Ser(855)). Importantly, mTORC1 containing phosphorylation site-defective raptor exhibits reduced in vitro kinase activity toward the substrate 4EBP1, with a multisite raptor 6A mutant more strongly defective that single-site raptor S863A. Taken together, these data suggest that complex raptor phosphorylation functions as a biochemical rheostat that modulates mTORC1 signaling in accordance with environmental cues.
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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 | 0.001 |
| 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".