Conformational resolution of nucleotide cycling and effector interactions for multiple small GTPases determined in parallel
Bibliographic record
Abstract
Small GTPases alternatively bind GDP/GTP guanine nucleotides to gate signaling pathways that direct most cellular processes. Numerous GTPases are implicated in oncogenesis, particularly the three RAS isoforms HRAS, KRAS, and NRAS and the RHO family GTPase RAC1. Signaling networks comprising small GTPases are highly connected, and there is some evidence of direct biochemical cross-talk between their functional G-domains. The activation potential of a given GTPase is contingent on a codependent interaction with the nucleotide and a Mg 2+ ion, which bind to individual variants with distinct affinities coordinated by residues in the GTPase nucleotide-binding pocket. Here, we utilized a selective-labeling strategy coupled with real-time NMR spectroscopy to monitor nucleotide exchange, GTP hydrolysis, and effector interactions of multiple small GTPases in a single complex system. We provide insight into nucleotide preference and the role of Mg 2+ in activating both WT and oncogenic mutant enzymes. Multiplexing revealed guanine nucleotide exchange factor (GEF), GTPase-activating protein (GAP), and effector-binding specificities in mixtures of GTPases and resolved that the three related RAS isoforms are biochemically equivalent. This work establishes that direct quantitation of the nucleotide-bound conformation is required to accurately determine an activation potential for any given GTPase, as small GTPases such as RAS-like proto-oncogene A (RALA) or the G12C mutant of KRAS display fast exchange kinetics but have a high affinity for GDP. Furthermore, we propose that the G-domains of small GTPases behave autonomously in solution and that nucleotide cycling proceeds independently of protein concentration but is highly impacted by Mg 2+ abundance.
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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 | 0.000 |
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".