Biochemical Classification of Disease-associated Mutants of RAS-like Protein Expressed in Many Tissues (RIT1)
Bibliographic record
Abstract
RAS-like protein expressed in many tissues 1 (RIT1) is a disease-associated RAS subfamily small guanosine triphosphatase (GTPase). Recent studies revealed that germ-line and somatic RIT1 mutations can cause Noonan syndrome (NS), and drive proliferation of lung adenocarcinomas, respectively, akin to RAS mutations in these diseases. However, the locations of these RIT1 mutations differ significantly from those found in RAS, and do not affect the three mutational "hot spots" of RAS. Moreover, few studies have characterized the GTPase cycle of RIT1 and its disease-associated mutants. Here we developed a real-time NMR-based GTPase assay for RIT1 and investigated the effect of disease-associated mutations on GTPase cycle. RIT1 exhibits an intrinsic GTP hydrolysis rate similar to that of H-RAS, but its intrinsic nucleotide exchange rate is ∼4-fold faster, likely as a result of divergent residues near the nucleotide binding site. All of the disease-associated mutations investigated increased the GTP-loaded, activated state of RIT1 in vitro, but they could be classified into two groups with different intrinsic GTPase properties. The S35T, A57G, and Y89H mutants exhibited more rapid nucleotide exchange, whereas F82V and T83P impaired GTP hydrolysis. A RAS-binding domain pulldown assay indicated that RIT1 A57G and Y89H were highly activated in HEK293T cells, whereas T83P and F82V exhibited more modest activation. All five mutations are associated with NS, whereas two (A57G and F82V) have also been identified in urinary tract cancers and myeloid malignancies. Characterization of the effects on the GTPase cycle of RIT1 disease-associated mutations should enable better understanding of their role in disease processes.
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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 | 0.001 | 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".