Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 2Plant Molecular Biology ResearchPlant-Microbe Interactions and ImmunityProtein Structure and Dynamics

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Genetic Diversity, Predictive Protein Structures, and Interaction Networks of Cysteine-Rich Receptor-Like Kinases in Arabidopsis thaliana

Open access
Stouthamer, Jente, Pereira, Danilo, Martin-Ramirez, Sergio, Mutte, Sumanth, Mott, G. Adam, Smakowska-Luzan, Elwira·2026·Computational and Structural Biotechnology Journal
DOI 10.34133/csbj.0043PMC PMC13058244

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Cysteine-rich receptor-like kinases (CRKs) are a large subfamily of plant receptor-like kinases 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 characterize the Arabidopsis thaliana CRK family. Phylogenetic reconstruction from 69 natural accessions resolved 5 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 modeling 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 reveal 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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.34133/csbj.0043

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1. CRK phylogeny and natural genetic diversity. (A) Phylogenetic reconstruction based on the CRK coding sequence region across Arabidopsis ecotypes. Best model fit according to BIC score: TPM3u+F+I+R4. Numbers correspond to bootstrap values generated from 1,000 ultrafast bootstraps. (B) Nucleotide diversity (π) and (C) Tajima’s D per CRK grouped per phylogenetic group. White dots correspond to the mean, and light gray dots correspond to individual CRK members belonging to that group. Phylogenetic groups are represented in the x-axis; n corresponds to the number of CRKs per phylogenetic group.
Fig. 1. CRK phylogeny and natural genetic diversity. (A) Phylogenetic reconstruction based on the CRK coding sequence region across Arabidopsis ecotypes. Best model fit according to BIC score: TPM3u+F+I+R4. Numbers correspond to bootstrap values generated from 1,000 ultrafast bootstraps. (B) Nucleotide diversity (π) and (C) Tajima’s D per CRK grouped per phylogenetic group. White dots correspond to the mean, and light gray dots correspond to individual CRK members belonging to that group. Phylogenetic groups are represented in the x-axis; n corresponds to the number of CRKs per phylogenetic group.CC BY · PubMed Central
Fig. 2. Structural alignments of predicted CRK–ECDs and their homologs. (A) Structure alignment of PDLP5 (brown, PDB: 6GRE [9]) to the predicted CRK42–ECD (pink, Group I). Group I CRKs are most similar to PDLPs, including 12 conserved cysteines (marked in orange). Structure alignment of GNK2 (brown, PDB: 4XRE [20]) to the predicted models of (B) CRK42–ECD (pink, Group I) and CRK8–ECD (teal, Group V) DUF26-A and (C) CRK42–ECD and CRK8–ECD DUF26-B. Mannose is indicated in green. The 3 residues involved in mannose-binding by GNK2 (N11, R93, and E104) and the respective AA in a similar position in the CRKs are represented as sticks.
Fig. 2. Structural alignments of predicted CRK–ECDs and their homologs. (A) Structure alignment of PDLP5 (brown, PDB: 6GRE [9]) to the predicted CRK42–ECD (pink, Group I). Group I CRKs are most similar to PDLPs, including 12 conserved cysteines (marked in orange). Structure alignment of GNK2 (brown, PDB: 4XRE [20]) to the predicted models of (B) CRK42–ECD (pink, Group I) and CRK8–ECD (teal, Group V) DUF26-A and (C) CRK42–ECD and CRK8–ECD DUF26-B. Mannose is indicated in green. The 3 residues involved in mannose-binding by GNK2 (N11, R93, and E104) and the respective AA in a similar position in the CRKs are represented as sticks.CC BY · PubMed Central
Fig. 3. CRK–ECDs have multiple glycosylation sites. Predicted N-glycosylation sites of aligned CRK–ECDs per phylogenetic group based on the N-X-S/T motif (where X is not Pro). Asparagine residues that can be glycosylated are colored by phylogenetic group color. Three sites were identified where Group II to V CRKs have a conserved glycosylation site. Two sites are on loops of DUF26-A (green arrows) and DUF26-B (red arrows); the third site is on the edge β-strand of DUF26-A (blue arrows).
Fig. 3. CRK–ECDs have multiple glycosylation sites. Predicted N-glycosylation sites of aligned CRK–ECDs per phylogenetic group based on the N-X-S/T motif (where X is not Pro). Asparagine residues that can be glycosylated are colored by phylogenetic group color. Three sites were identified where Group II to V CRKs have a conserved glycosylation site. Two sites are on loops of DUF26-A (green arrows) and DUF26-B (red arrows); the third site is on the edge β-strand of DUF26-A (blue arrows).CC BY · PubMed Central
Fig. 4. The distribution of the cysteine residues in CRK–ECDs. (A) Predicted models of representative CRKs for each phylogenetic group (CRK 42—Group I, CRK29—Group II, CRK39—Group III, CRK11—Group IV, and CRK8—Group V). The cysteines are marked in orange. The representative CRKs were chosen based on the highest average amino acid pairwise percentage identity of each CRK–ECD to the other members of the same phylogenetic group (Fig. S3). (B) The nonconserved cysteines are positioned on the first β-strand in DUF26-B of CRKs. There are 4 possible options in this position, from left to right: a disulfide bridge connecting secondary structural elements (Group I), a vicinal disulfide bridge (Groups II and IV), a single cysteine (Group III), or no cysteine (Group V).
Fig. 4. The distribution of the cysteine residues in CRK–ECDs. (A) Predicted models of representative CRKs for each phylogenetic group (CRK 42—Group I, CRK29—Group II, CRK39—Group III, CRK11—Group IV, and CRK8—Group V). The cysteines are marked in orange. The representative CRKs were chosen based on the highest average amino acid pairwise percentage identity of each CRK–ECD to the other members of the same phylogenetic group (Fig. S3). (B) The nonconserved cysteines are positioned on the first β-strand in DUF26-B of CRKs. There are 4 possible options in this position, from left to right: a disulfide bridge connecting secondary structural elements (Group I), a vicinal disulfide bridge (Groups II and IV), a single cysteine (Group III), or no cysteine (Group V).CC BY · PubMed Central
Fig. 5. AF predicts the dimerization of CRKs. Heat maps of (A) ipTM + pTM and (B) iPAE scores for all CRK–CRK predictions. (C) The number of CRK–ECD pairs, out of all 741 pairs tested, that pass our ipTM + pTM, iPAE score cutoffs. (D) A pie chart representing the CRK–ECD pairs out of 174 that have more or less than 29 residues in the interaction interface suggesting high-confidence models. (E) Average number of CRK–ECD pairs that pass cutoffs per number of CRKs in a group.
Fig. 5. AF predicts the dimerization of CRKs. Heat maps of (A) ipTM + pTM and (B) iPAE scores for all CRK–CRK predictions. (C) The number of CRK–ECD pairs, out of all 741 pairs tested, that pass our ipTM + pTM, iPAE score cutoffs. (D) A pie chart representing the CRK–ECD pairs out of 174 that have more or less than 29 residues in the interaction interface suggesting high-confidence models. (E) Average number of CRK–ECD pairs that pass cutoffs per number of CRKs in a group.CC BY · PubMed Central
Fig. 6. Orientation and distribution of predicted CRK–ECD dimers. (A) Three different classifications of orientations were identified, left to right: Standard, Flipped, and Flipped and shifted. The CRK pairs modeled are from left to right CRK31–CRK27 (Group IV and Group II), CRK38–CRK27 (Group III and Group II), and CRK11–CRK7 (Group IV and Group V). The DUF26-A position is marked, indicating the orientation of the ECD. (B) The percentage of each orientation that occurs in our datasets. CRK dimers are in the Standard orientation in 78% of the models. Other orientations occur less often. (C) Occurrence of each orientation per phylogenetic group. Per phylogenetic group, the dimer orientation of each dimer containing a CRK of that phylogenetic group was assessed. CRKs in Groups II, IV, and V all have a similar frequency at which the 3 orientations occur. Groups I and III have very few models in the Standard orientation.
Fig. 6. Orientation and distribution of predicted CRK–ECD dimers. (A) Three different classifications of orientations were identified, left to right: Standard, Flipped, and Flipped and shifted. The CRK pairs modeled are from left to right CRK31–CRK27 (Group IV and Group II), CRK38–CRK27 (Group III and Group II), and CRK11–CRK7 (Group IV and Group V). The DUF26-A position is marked, indicating the orientation of the ECD. (B) The percentage of each orientation that occurs in our datasets. CRK dimers are in the Standard orientation in 78% of the models. Other orientations occur less often. (C) Occurrence of each orientation per phylogenetic group. Per phylogenetic group, the dimer orientation of each dimer containing a CRK of that phylogenetic group was assessed. CRKs in Groups II, IV, and V all have a similar frequency at which the 3 orientations occur. Groups I and III have very few models in the Standard orientation.CC BY · PubMed Central
Fig. 7. Properties of the various CRK–ECD interaction interfaces. (A) Seventy-two percent of CRK–ECD pairs have a predicted dimer interface on the β-strand of DUF26-B of both ECDs. In most Standard orientation models, the DUF26-A domains remain further apart. Dimer model shows CRK13–CRK28 (Group IV and Group II). (B) In a small subset, DUF26-A is also predicted to be in close proximity. Dimer model shows CRK27–CRK31 (Group II and Group IV). (C) Zoomed-in view of the DUF26-B β-strand interaction shown in (B). The β-strands mainly form hydrogen bonds through the amino acid backbone. (D) Amino acid sequence alignment of the interface β-strand. The residues in the interface are not conserved but are more hydrophobic. (E) Surface charge predictions of 3 models of CRK–ECD dimer pairs (CRK13–CRK28 [Group IV–Group II], CRK27–CRK31 [Group II–Group IV], and CRK10–CRK11 [Group V–Group IV]) show surface charge patches surrounding and inside the interface. Surface charge electrostatic potential ranges from −5 kJ/mol/e (red) to 5 kJ/mol/e (blue) [97]. APBS electrostatics was used in PyMOL to generate the maps. (F) CRK18–CRK18 pair (Group IV) showcasing the proximity of the vicinal disulfides to each other in the dimer. (Vicinal) disulfides are marked in orange.
Fig. 7. Properties of the various CRK–ECD interaction interfaces. (A) Seventy-two percent of CRK–ECD pairs have a predicted dimer interface on the β-strand of DUF26-B of both ECDs. In most Standard orientation models, the DUF26-A domains remain further apart. Dimer model shows CRK13–CRK28 (Group IV and Group II). (B) In a small subset, DUF26-A is also predicted to be in close proximity. Dimer model shows CRK27–CRK31 (Group II and Group IV). (C) Zoomed-in view of the DUF26-B β-strand interaction shown in (B). The β-strands mainly form hydrogen bonds through the amino acid backbone. (D) Amino acid sequence alignment of the interface β-strand. The residues in the interface are not conserved but are more hydrophobic. (E) Surface charge predictions of 3 models of CRK–ECD dimer pairs (CRK13–CRK28 [Group IV–Group II], CRK27–CRK31 [Group II–Group IV], and CRK10–CRK11 [Group V–Group IV]) show surface charge patches surrounding and inside the interface. Surface charge electrostatic potential ranges from −5 kJ/mol/e (red) to 5 kJ/mol/e (blue) [97]. APBS electrostatics was used in PyMOL to generate the maps. (F) CRK18–CRK18 pair (Group IV) showcasing the proximity of the vicinal disulfides to each other in the dimer. (Vicinal) disulfides are marked in orange.CC BY · PubMed Central
Fig. 8. Interaction network of CRK–ECD AF predicted dimers. (A) The 145 AF CRK–ECD dimer predictions that passed our cutoffs were used to generate an interaction network. Each number represents a CRK–ECD, and the colored circles or nodes correspond to the phylogenetic group to which the CRK belongs. The node size corresponds to the number of interactions a CRK has. A light gray outline of the node indicates homodimerization. Lines connecting nodes indicate heterodimers. Communities of interconnected CRKs, as identified with the WalkTrap algorithm [33], are marked by a yellow background and are numbered I, II, and III. (B) Interaction networks refined using expression data of CRKs in seedlings and senescent plants from TravaDB [35]. (C) Interaction networks refined using expression data from Bjornson et al. [34] of CRKs in mock-treated seedlings or seedlings treated with 1 μM flg22 for 90 min.
Fig. 8. Interaction network of CRK–ECD AF predicted dimers. (A) The 145 AF CRK–ECD dimer predictions that passed our cutoffs were used to generate an interaction network. Each number represents a CRK–ECD, and the colored circles or nodes correspond to the phylogenetic group to which the CRK belongs. The node size corresponds to the number of interactions a CRK has. A light gray outline of the node indicates homodimerization. Lines connecting nodes indicate heterodimers. Communities of interconnected CRKs, as identified with the WalkTrap algorithm [33], are marked by a yellow background and are numbered I, II, and III. (B) Interaction networks refined using expression data of CRKs in seedlings and senescent plants from TravaDB [35]. (C) Interaction networks refined using expression data from Bjornson et al. [34] of CRKs in mock-treated seedlings or seedlings treated with 1 μM flg22 for 90 min.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 93

  1. 10.1104/pp.126.2.473 · DOI
  2. Martin-Ramirez S, Stouthamer J, Smakowska-Luzan E. More questions than answers: Insights into potential cysteine-rich receptor-like kinases redox signalling in Arabidopsis. Plant J. 2025;122(2): Article e70176. · DOI
  3. 10.1146/annurev-arplant-042916-040957 · DOI · Paper page
  4. Shiu SH, Bleecker AB. Plant receptor-like kinase gene family: Diversity, function, and signaling. Sci STKE. 2001;2001(113):re22.
  5. Bourdais G, Burdiak P, Gauthier A, Nitsch L, Salojärvi J, Rayapuram C, Idänheimo N, Hunter K, Kimura S, et al. Large-scale phenomics identifies primary and fine-tuning roles for CRKs in responses related to oxidative stress. PLOS Genet. 2015;11(7): Article e1005373. · DOI
  6. 10.1073/pnas.181141598 · DOI · Paper page
  7. 10.1105/tpc.020834 · DOI · Paper page
  8. 10.1016/j.tplants.2023.03.028 · DOI
  9. Vaattovaara A, Brandt B, Rajaraman S, Safronov O, Veidenberg A, Luklová M, Kangasjärvi J, Löytynoja A, Hothorn M, Salojärvi J, et al. Mechanistic insights into the evolution of DUF26-containing proteins in land plants. Commun. Biol. 2019;2:56. · DOI
  10. Yadeta KA, Elmore JM, Creer AY, Feng B, Franco JY, Rufian JS, He P, Phinney B, Coaker G. A cysteine-rich protein kinase associates with a membrane immune complex and the cysteine residues are required for cell death. Plant Physiol. 2017;173(1):771–787. · DOI
  11. Restrepo-Montoya D, Brueggeman R, McClean PE, Osorno JM. Computational identification of receptor-like kinases ‘rLK’ and receptor-like proteins ‘rLP’ in legumes. BMC Genomics. 2020;21(1):459. · DOI
  12. del Hierro I, Mélida H, Broyart C, Santiago J, Molina A. Computational prediction method to decipher receptor–glycoligand interactions in plant immunity. Plant J. 2021;105(6):1710. · DOI
  13. Ali F. Patterns of change in nucleotide diversity over gene length. Genome Biol Evol. 2024;16(4):evae078. · DOI
  14. Tatarinova TV, Chekalin E, Nikolsky Y, Bruskin S, Chebotarov D, McNally KL, Alexandrov N. Nucleotide diversity analysis highlights functionally important genomic regions. Sci Rep. 2016;6:35730. · DOI
  15. 10.1038/s44319-024-00122-9 · DOI
  16. 10.1016/j.cub.2024.07.061 · DOI
  17. Lomize AL, Schnitzer KA, Tood SC, Cherepanov S, Outerial C, Deane CM, Pogozheva ID. Membranome 3.0: Database of single-pass membrane proteins with AlphaFold models. Protein Sci. 2022;31(5): Article e4318. · DOI
  18. Dobson L, Szekeres LI, Gerdán C, Langó T, Zeke A, Tusnády GE. TmAlphaFold database: Membrane localization and evaluation of AlphaFold2 predicted alpha-helical transmembrane protein structures. Nucleic Acids Res. 2023;51(D1):D517–D522. · DOI
  19. 10.1104/pp.114.242636 · DOI
  20. Cui W, Lee JY. Arabidopsis callose synthases CalS1/8 regulate plasmodesmal permeability during stress. Nat Plants. 2016;2(5):16034. · DOI
  21. Sawano Y, Miyakawa T, Yamazaki H, Tanokura M, Hatano KI. Purification, characterization, and molecular gene cloning of an antifungal protein from Ginkgo biloba seeds. Biol Chem. 2007;388(3):273–280. · DOI
  22. Miyakawa T, Miyazono KI, Sawano Y, Hatano K-I, Tanokura M. Crystal structure of ginkbilobin-2 with homology to the extracellular domain of plant cysteine-rich receptor-like kinases. Proteins. 2009;77(1):247–251. · DOI
  23. 10.1074/jbc.M109.063073 · DOI
  24. 10.1038/s41477-024-01841-6 · DOI
  25. 10.1016/j.plantsci.2018.05.007 · DOI
  26. de Oliveira MVV, Xu G, Li B, de Souza Vespoli L, Meng X, Chen X, Yu X, de Souza SA, Intorne A, de Manhães AM, et al. Specific control of Arabidopsis BAK1/SERK4-regulated cell death by protein glycosylation. Nat Plants. 2016;2:15218. · DOI
  27. 10.1110/ps.03234503 · DOI
  28. 10.1016/j.jmb.2017.03.017 · DOI
  29. Evans R O’Neill M Pritzel A Antropova N Senior A Green T Žídek A Bates R Blackwell S Yim J et al. Protein complex prediction with AlphaFold-Multimer. bioRxiv. 2021. https://doi.org/10.1101/2021.10.04.463034 · DOI · Paper page
  30. Yan C, Wu F, Jernigan RL, Dobbs D, Honavar V. Characterization of protein-protein interfaces. Protein J. 2008;27(1):59–70. · DOI
  31. Wei X, Liu X, Zhang X, Guo S, Shi J. Structural insights into ligand recognition and receptor activation of plant leucine-rich repeat (LRR) transmembrane receptors. N Crops. 2024;1: Article 100022. · DOI
  32. Liu W, Pellegrini M, Wang X. Detecting communities based on network topology. Sci Rep. 2014;4:5739. · DOI
  33. 10.1038/s41477-021-00874-5 · DOI · Paper page
  34. 10.1111/tpj.13312 · DOI
  35. 10.1111/nph.18305 · DOI
  36. Pasion J, Hind SR. Utilizing Tajima’s D to identify potential microbe-associated molecular patterns in Xanthomonas euvesicatoria and X. perforans. Physiol Mol Plant Pathol. 2021;116: Article 101744. · DOI
  37. Steidele CE, Stam R. Multi-omics approach highlights differences between RLP classes in Arabidopsis thaliana. BMC Genomics. 2021;22(1):557. · DOI
  38. Kileeg Z, Mott GA. A species-wide inventory of receptor-like kinases in Arabidopsis thaliana. BMC Biol. 2025;23(1):266. · DOI
  39. Ngou BPM, Wyler M, Schmid MW, Kadota Y, Shirasu K. Evolutionary trajectory of pattern recognition receptors in plants. Nat Commun. 2024;15(1):308. · DOI
  40. Fischer I, Diévart A, Droc G, Dufayard JF, Chantret N. Evolutionary dynamics of the leucine-rich repeat receptor-like kinase (LRR-RLK) subfamily in angiosperms. Plant Physiol. 2016;170(3):1595–1610. · DOI
  41. Stouthamer J, Martin-Ramirez S, Smakowska-Luzan E. An interactome assay for detecting interactions between extracellular domains of receptor kinases. Methods Mol Biol. 2023;2690:193–204. · DOI
  42. Butenko MA, Wildhagen M, Albert M, Jehle A, Kalbacher H, Aalen RB, Felix G. Tools and strategies to match peptide-ligand receptor pairs. Plant Cell. 2014;26(5):1838–1847. · DOI
  43. Miyakawa T, Sawano Y, Miyazono K-I, Hatano K-I, Tanokura M. Crystallization and preliminary X-ray analysis of ginkbilobin-2 from Ginkgo biloba seeds: A novel antifungal protein with homology to the extracellular domain of plant cysteine-rich receptor-like kinases. Acta Crystallogr Sect F Struct Biol Cryst Commun. 2007;63(Pt 9):737–739. · DOI
  44. Man J, Harrington TA, Lally K, Bartlett ME. Asymmetric evolution of protein domains in the leucine-rich repeat receptor-like kinase family of plant signaling proteins. Mol Biol Evol. 2023;40(10):msad220. · DOI
  45. Hurst CH, Wright KM, Turnbull D, Leslie K, Jones S, Hemsley PA. Juxta-membrane S-acylation of plant receptor-like kinases is likely fortuitous and does not necessarily impact upon function. Sci Rep. 2019;9(1):12818. · DOI
  46. Li C, Wu HM, Cheung AY. FERONIA and her pals: Functions and mechanisms. Plant Physiol. 2016;171(4):2379. · DOI
  47. Li C, Yeh F-L, Cheung AY, Duan Q, Kita D, Liu M-C, Maman J, Luu EJ, Wu BW, Gates L, et al. Glycosylphosphatidylinositol-anchored proteins as chaperones and co-receptors for FERONIA receptor kinase signaling in Arabidopsis. ELife. 2015;4:1–21.
  48. Trempel F, Kajiura H, Ranf S, Grimmer J, Westphal L, Zipfel C, Scheel D, Fujiyama K, Lee J. Altered glycosylation of exported proteins, including surface immune receptors, compromises calcium and downstream signaling responses to microbe-associated molecular patterns in Arabidopsis thaliana. BMC Plant Biol. 2016;16:31. · DOI
  49. Czernic P, Visser B, Sun W, Savouré A, Deslandes L, Marco Y, Montagu MV, Verbruggen N. Characterization of an Arabidopsis thaliana receptor-like protein kinase gene activated by oxidative stress and pathogen attack. Plant J. 1999;18(3):321–327. · DOI
  50. Hung C-W, Koudelka T, Anastasi C, Becker A, Moali C, Tholey A. Characterization of post-translational modifications in full-length human BMP-1 confirms the presence of a rare vicinal disulfide linkage in the catalytic domain and highlights novel features of the EGF domain. J Proteome. 2016;138:136–145. · DOI
  51. Butera D, Passam F, Ju L, Cook KM, Woon H, Aponte-Santamaría C, Gardiner E, Davis AK, Murphy DA, Bronowska A, et al. Autoregulation of von Willebrand factor function by a disulfide bond switch. Sci Adv. 2018;4(2). · DOI
  52. 10.1073/pnas.052706099 · DOI · Paper page
  53. Levy ED, Teichmann S. Structural, evolutionary, and assembly principles of protein oligomerization. Prog Mol Biol Transl Sci. 2013;117:25–51. · DOI
  54. 10.1016/j.bbrc.2014.02.013 · DOI
  55. 10.1093/jxb/erad080 · DOI
  56. Imai S, Hirozawa H, Sugahara S, Ishizaki C, Higuchi M, Matsushita Y, Suzuki T, Mochizuki N, Nagatani A, Ueguchi C. The CRK14 gene encoding a cysteine-rich receptor-like kinase is implicated in the regulation of global proliferative arrest in Arabidopsis thaliana. Genes Cells. 2024;29(9):735–745. · DOI
  57. Kimura S, Waszczak C, Hunter K, Wrzaczek M. Bound by fate: The role of reactive oxygen species in receptor-like kinase signaling. Plant Cell. 2017;29(4):638–654. · DOI
  58. 10.1038/s41586-020-2032-3 · DOI · Paper page
  59. Wrzaczek M, Brosché M, Salojärvi J, Kangasjärvi S, Idänheimo N, Mersmann S, Robatzek S, Karpiński S, Karpińska B, Kangasjärvi J. Transcriptional regulation of the CRK/DUF26 group of receptor-like protein kinases by ozone and plant hormones in Arabidopsis. BMC Plant Biol. 2010;10:95. · DOI · Paper page
  60. 10.1093/jxb/erad236 · DOI
  61. 10.1016/j.jmb.2010.09.027 · DOI
  62. 10.1016/j.freeradbiomed.2022.12.001 · DOI
  63. 10.1093/jxb/eraa414 · DOI
  64. Molina A, Sánchez-Vallet A, Jordá L, Carrasco-López C, Rodríguez-Herva JJ, López-Solanilla E. Plant cell walls: Source of carbohydrate-based signals in plant-pathogen interactions. Curr Opin Plant Biol. 2024;82: Article 102630. · DOI
  65. Ma L-S, Wang L, Trippel C, Mendoza-Mendoza A, Ullmann S, Moretti M, Carsten A, Kahnt J, Reissmann S, Zechmann B, et al. The Ustilago maydis repetitive effector Rsp3 blocks the antifungal activity of mannose-binding maize proteins. Nat Commun. 2018;9(1):1711. · DOI
  66. 10.1093/jxb/erab328 · DOI
  67. Pierdzig L Seinsche C Kalla S Armiento S Schulz L.M. Rismondo J Molinaro A De Castro C Petutschnig EK Lipka V. Cysteine-rich receptor-like kinases mediate wall teichoic acid perception in Arabidopsis. bioRxiv. 2025. https://doi.org/10.1101/2025.09.18.677107 · DOI
  68. 10.1038/s41477-024-01836-3 · DOI
  69. 10.1016/j.cell.2022.07.012 · DOI
  70. Terwilliger TC, Liebschner D, Croll TI, Williams CJ, McCoy AJ, Poon BK, Afonine PV, Oeffner RD, Richardson JS, Read RJ, et al. AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination. Nat Methods. 2023;21(1):110–116. · DOI · Paper page
  71. 10.1016/j.csbj.2024.09.010 · DOI
  72. 10.1016/j.csbj.2025.07.021 · DOI
  73. Lian Q, Huettel B, Walkemeier B, Mayjonade B, Lopez-Roques C, Gil L, Roux F, Schneeberger K, Mercier R. A pan-genome of 69 Arabidopsis thaliana accessions reveals a conserved genome structure throughout the global species range. Nat Genet. 2024;56(6):982–991. · DOI
  74. 10.1186/1471-2105-10-421 · DOI · Paper page
  75. Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Withwham A, Keane T, McCarthy SA, Davies RM, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10(2):giab008. · DOI · Paper page
  76. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol. 2013;30(4):772–780. · DOI · Paper page
  77. Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25(15):1972–1973. · DOI · Paper page
  78. 10.1093/molbev/msaa015 · DOI · Paper page
  79. Letunic I, Bork P. Interactive tree of life (iTOL) v6: Recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res. 2024;52(W1):W78–W82. · DOI · Paper page
  80. Leigh JW, Bryant D. Popart: Full-feature software for haplotype network construction. Methods Ecol Evol. 2015;6(9):1110–1116. · DOI
  81. Clement M Snell Q Walke P Posada D Crandall K. TCS: Estimating gene genealogies. In: Proceedings - International Parallel and Distributed Processing Symposium IPDPS. IEEE; 2002. · DOI
  82. Pond SLK, Frost SDW, Muse SV. HyPhy: Hypothesis testing using phylogenies. Bioinformatics. 2005;21(5):676–679. · DOI · Paper page
  83. Murrell B, Wertheim JO, Moola S, Weighill T, Scheffler K, Pond SLK. Detecting individual sites subject to episodic diversifying selection. PLoS Genet. 2012;8(7): Article e1002764. · DOI · Paper page
  84. 10.1093/molbev/mst030 · DOI · Paper page
  85. Pond SLK, Frost SDW. Not so different after all: A comparison of methods for detecting amino acid sites under selection. Mol Biol Evol. 2005;22(5):1208–1222. · DOI · Paper page
  86. Larsson A. AliView: A fast and lightweight alignment viewer and editor for large datasets. Bioinformatics. 2014;30(22):3276–3278. · DOI
  87. Tareen A, Kinney JB. Logomaker: Beautiful sequence logos in python. Bioinformatics. 2020;36(7):2272–2274. · DOI
  88. 10.1038/msb.2011.75 · DOI · Paper page
  89. 10.1002/pro.3290 · DOI · Paper page
  90. Edgar RC. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–1797. · DOI · Paper page
  91. Madeira F, Madhusoodanan N, Lee J, Eusebi A, Niewielska A, Tivey AR, Lopez R, Butcher S. The EMBL-EBI job dispatcher sequence analysis tools framework in 2024. Nucleic Acids Res. 2024;52(W1):W521–W525. · DOI · Paper page
  92. Krissinel E, Henrick K. Inference of macromolecular assemblies from crystalline state. J Mol Biol. 2007;372(3):774–797. · DOI · Paper page
  93. 10.1002/pro.3280 · DOI

Metadata

Authors
Stouthamer, J., Pereira, D., Martin-Ramirez, S., Mutte, S., Mott, G. A., & Smakowska-Luzan, E.
Affiliations
  • Wageningen University & Research (NL)
  • Laboratory of Biochemistry, Wageningen University and Research, 6708 WE Wageningen, The Netherlands.
  • University of Toronto Scarborough (CA)
ORCID
Venue
Computational and Structural Biotechnology Journal
Publisher
American Association for the Advancement of Science (AAAS)
Published
2026-03-01
Volume
35
Issue
1
Pages
0043-0043
ISSN
2001-0370 (ISSN-L)
DOI
10.34133/csbj.0043
PMC
PMC13058244
OpenAlex
W7137929205
Cited by
2
References
3
Citations by year
2026: 2
FWCI
7.36
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Computational and Structural Biotechnology Journal (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, public-domain)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Socio-Environmental Systems Modeling (submittedVersion, cc-by)
Funders
  • NWO Talent Programme Vidi (VI.Vidi.193.074)
  • Natural Sciences and Engineering Research Council of Canada through a Discovery Grant Award (RGPIN-2019-06395)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Molecular Biology Research
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant-Microbe Interactions and Immunity
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
cysteine-rich receptor-like kinases, Arabidopsis thaliana, genetic diversity, extracellular domain structure, protein dimerization, receptor-receptor interactions, positive and negative selection, phylogenetic clades, plant immunity, population genetics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.