Genetic diversity, predictive protein structures, and interaction networks of Cysteine-Rich Receptor-Like Kinases in <i>Arabidopsis thaliana</i>
Bibliographic record
Abstract
Abstract Cysteine-rich receptor-like kinases (CRKs) are a large subfamily of plant receptor-like kinases (RLKs) implicated in immunity and development, yet their ligands, interaction partners, and mechanistic roles remain poorly defined. We combined population-genetic analyses and AlphaFold-based structural prediction to characterise the Arabidopsis thaliana CRK family. Phylogenetic reconstruction from 69 natural accessions resolved five well-supported CRK clades. Nucleotide diversity (π) and neutrality tests revealed heterogeneous diversity across loci, with evidence of both positive and negative selection pressure acting on different CRKs. AlphaFold models of CRK extracellular domains (ECDs) recapitulate the DUF26 structure observed in Plasmodesmata Localizing Protein (PDLP)5/PDLP8 and ginkbilobin-2 but display distinct biochemical properties and disulfide-bond topologies. Pairwise AlphaFold dimer modelling of all 780 CRK-ECD combinations produced 145 high-confidence interaction models; ∼78% of these adopt a shared dimer conformation characterized by an extended intermolecular β-sheet at the interface. Integrating evolutionary and structural approaches reveals clade-specific selective regimes and conserved structural features of CRK ECDs that likely underpin receptor–receptor interactions. Predicted high-confidence dimer interfaces suggest a general mode of CRK-ECD association that can guide targeted biochemical and genetic validation, accelerating functional dissection of this important receptor family.
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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".