Computational prediction method to decipher receptor–glycoligand interactions in plant immunity

Microbial and plant cell walls have been selected by the immune system as a source of microbe- damage-associated molecular patterns (MAMPs/DAMPs) that are perceived extracellular ectodomains (ECDs) pattern recognition receptors (PRRs) triggering responses. From vast number ligands PRRs can bind, those composed carbohydrate moieties poorly studied, only handful PRR/glycan pairs determined. Here we present computational screening method, based on first step dynamics simulation, is able to predict putative ECD-PRR/glycan interactions. This method has developed optimized with Arabidopsis LysM-PRR members CERK1 LYK4, which involved in perception fungal MAMPs, chitohexaose (1,4-β-d-(GlcNAc)6) laminarihexaose (1,3-β-d-(Glc)6). Our silico results predicted interactions 1,4-β-d-(GlcNAc)6 whilst discarding its direct binding LYK4. In contrast, no interaction between CERK1/laminarihexaose was model despite being required for activation, suggesting may act co-receptor recognition. These were validated isothermal titration calorimetry assays these MAMPs recombinant ECDs-LysM-PRRs. The robustness further predicting does not bind DAMP 1,4-β-d-(Glc)6 (cellohexaose), then probing responses triggered this impaired cerk1 mutant. predictive glycan/PRR here might accelerate discovery protein–glycan provide information activated glycoligands.