CBGP researchers have developed a computational pipeline that predicts interactions between plant immunity receptors and glycoligands from microbial or plant cell walls, which trigger immunity.These results provide the bases for the identification and rational design of novel immunoglycans to be used in sustainable crops disease resistance.
Plants have a robust immune system that comprise a set of Pattern Recognition Receptors (PRRs) that trigger immune response and disease resistance upon recognition of "non-self" ligands derived from microorganisms (Microbe-Associated Molecular Patterns, MAMPs) or plant “self” ligands released upon infection (Damage-Associated Molecular Patterns, DAMPs). Among these MAMPs/DAMPs are some carbohydrate-based structures (glycoligands) that are derived from the cell wall of microbial and plant cells. About half of the vast number of plant PRRs could potentially bind glycoligands, based on the predicted structure of PRRs extracellular ectodomains (ECDs) that bind MAMPs / DAMPs. Despite this prediction, only a handful of plant PRR/glycan pairs have been determined so far. To progress in the discovery of novel PRR/glycan pairs, researchers from Molina lab, at the Center of Plant Biotechnology and Genomics (CBGP, UPM-INIA), in collaboration with the group of Julia Santiago (Lausanne University), have developed a computational pipeline that might accelerate the discovery of protein-glycan interactions and provide information on immune processes activated by glycoligands in plants. This novel pipeline is based on docking and molecular dynamics computational methods, but it is designed to avoid most of the heavy computing requirements and the challenging parameterization and configuration typically required. In the article by Irene de Hierro et al. recently published in The Plant Journal, the authors describe this novel computational methodology and also validate it with PRRs of the LysM family of Arabidopsis, that are involved in the activation of the immune response triggered by some glycans, like hesaccharides from fungal chitin and laminarin glycans. The computational LysM/glycan binding predictions obtained were experimentally validated by in vitro ECD/glycan binding experiments using Isothermal Titration Calorimetry and by in vivo analyses of the immune responses triggered in Arabidopsis wild-type and LysM mutants by these glycoligands. The computational predictive glycans/PRRs binding method described in the article might accelerate protein-glycan binding discovery and provide information on plant immune responses. The identified glycoligands and PRRs will be used to develop more sustainable crop protection strategies by replacing chemical pesticides for this novel generation of biological immunomodulators enhance that enhance crops disease resistance.
Hierro, I. del, Mélida, H., Broyart, C., Santiago, J., Molina, A. 2020. Computational prediction method to decipher receptor-glycoligand interactions in plant immunity. The Plant Journal. DOI: https://doi.org/10.1111/tpj.15133.