Biochemical Clustering of Monomeric GTPases of the Ras Superfamily
Bibliographic record
Abstract
To date phylogeny has been used to compare entire families of proteins based on their nucleotide or amino acid sequence. Here we developed a novel analytical platform allowing a systematic comparison of protein families based on their biochemical properties. This approach was validated on the Rho subfamily of GTPases. We used two high throughput methods, referred to as AlphaScreen and FlashPlate, to measure nucleotide binding capacity, exchange, and hydrolysis activities of small monomeric GTPases. These two technologies have the characteristics to be very sensitive and to allow homogenous and high throughput assays. To analyze and integrate the data obtained, we developed an algorithm that allows the classification of GTPases according to their enzymatic activities. Integration and hierarchical clustering of these results revealed unexpected features of the small Rho GTPases when compared with primary sequence-based trees. Hence we propose a novel phylobiochemical classification of the Ras superfamily of GTPases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".