Structural conservation and divergence across the Receptor Tyrosine Kinase superfamily
Bibliographic record
Abstract
Abstract Members of the Receptor Tyrosine Kinase (RTK) superfamily are regulators of cellular signaling, playing essential roles in cellular growth, differentiation, and survival. Dysregulation of RTKs leads to diseases such as cancer, diabetes, and inflammatory disorders, making them important therapeutic targets. Despite extensive research on RTKs, the structural diversity and evolutionary relationships across the superfamily are not fully understood. Here, we systematically compared structural conservation and divergence among 245 extracellular domains from 54 RTKs, across 18 RTK families. Using experimentally-resolved structures and AlphaFold2 models, we conducted an all-versus-all structural alignment to explore domain architecture and quantify significant structural similarities within the RTK superfamily. We curated a comprehensive database encompassing PDB structures, 3D folds, ligand-binding properties, and sequence information of all RTK domains analyzed ( https://fasslero.github.io/RTK-domains ). Our analysis revealed numerous inter-family similarities and remote evolutionary connections, in particular among ligand-binding domains (LBDs), and distinct structural domain types and unexpected dissimilarities among domains previously classified as related. Our work highlights the intricate balance between structural conservation and divergence in RTKs and sheds light on the evolutionary mechanisms shaping this critical superfamily.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".