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

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