A robust methodology to subclassify pseudokinases based on their nucleotide-binding properties
Bibliographic record
Abstract
Protein kinase-like domains that lack conserved residues known to catalyse phosphoryl transfer, termed pseudokinases, have emerged as important signalling domains across all kingdoms of life. Although predicted to function principally as catalysis-independent protein-interaction modules, several pseudokinase domains have been attributed unexpected catalytic functions, often amid controversy. We established a thermal-shift assay as a benchmark technique to define the nucleotide-binding properties of kinase-like domains. Unlike in vitro kinase assays, this assay is insensitive to the presence of minor quantities of contaminating kinases that may otherwise lead to incorrect attribution of catalytic functions to pseudokinases. We demonstrated the utility of this method by classifying 31 diverse pseudokinase domains into four groups: devoid of detectable nucleotide or cation binding; cation-independent nucleotide binding; cation binding; and nucleotide binding enhanced by cations. Whereas nine pseudokinases bound ATP in a divalent cation-dependent manner, over half of those examined did not detectably bind nucleotides, illustrating that pseudokinase domains predominantly function as non-catalytic protein-interaction modules within signalling networks and that only a small subset is potentially catalytically active. We propose that henceforth the thermal-shift assay be adopted as the standard technique for establishing the nucleotide-binding and catalytic potential of kinase-like domains.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".