In silico prediction method for plant Nucleotide-binding leucine-rich repeat- and pathogen effector interactions

Abstract

Plant Nucleotide-binding leucine-rich repeat (NLR) proteins play a crucial role in effector recognition and activation of Effector triggered immunity following pathogen infection. Genome sequencing advancements have led to the identification of a myriad of NLRs in numerous agriculturally important plant species. However, deciphering which NLRs recognize specific pathogen effectors remains challenging. Predicting NLR–effector interactions in silico will provide a more targeted approach for experimental validation, critical for elucidating function, and advancing our understanding of NLR-triggered immunity. In this study, NLR–effector protein complex structures were predicted using AlphaFold2-Multimer for all experimentally validated NLR–effector interactions reported in literature. Binding affinities- and energies were predicted using 97 machine learning models from Area-Affinity. We show that AlphaFold2-Multimer predicted structures have acceptable accuracy and can be used to investigate NLR–effector interactions in silico. Binding affinities for 58 NLR–effector complexes ranged between −8.5 and −10.6 log(K), and binding energies between −11.8 and −14.4 kcal/mol⁻¹, depending on the Area-Affinity model used. For 2427 “forced” NLR–effector complexes, these estimates showed larger variability, enabling identification of novel NLR–effector interactions with 99% accuracy using an Ensemble machine learning model. The narrow range of binding energies- and affinities for “true” interactions suggest a specific change in Gibbs free energy, and thus conformational change, is required for NLR activation. This is the first study to provide a method for predicting NLR–effector interactions, applicable to all pathosystems. Finally, the NLR–Effector Interaction Classification (NEIC) resource can streamline research efforts by identifying NLRs important for plant–pathogen resistance, advancing our understanding of plant immunity.