Computational and Structural Biotechnology JournalBioinformaticsPeer-reviewed journal article
Genetic diversity, predictive protein structures, and interaction networks of Cysteine-Rich Receptor-Like Kinases in Arabidopsis thaliana
Jente StouthameriD, Danilo PereiraiD, Sergio Martin‐RamireziD, Sumanth Kumar MutteiD, G. Adam MottiD, Elwira Smakowska‐LuzaniD
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 ({pi}) 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 {beta}-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.
- organism: Arabidopsis
- assay: protein structure
- research focus: gene regulatory network inference
- research focus: protein structure prediction
- approach: statistical model
- approach: deep learning
- Citations
- 2
- FWCI
- 7.44
- Field percentile
- top 4%
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