Predicting receptor-ligand pairing preferences in plant-microbe interfaces via molecular dynamics and machine learning.

ABSTRACT: Microbiome assembly, structure, and dynamics significantly influence plant health. Secreted microbial signaling molecules initiate and mediate symbiosis by binding to structurally compatible plant receptors. For example, lipo-chitooligosaccharides (LCOs), produced by nitrogen-fixing rhizobial bacteria and various fungi, are recognized by plant lysin motif receptor-like kinases (LysM-RLKs), which activate the common symbiotic pathway. Accurately predicting these molecular interactions could reveal complementary signatures underlying the initial stages of endosymbiosis. Despite the breakthrough in protein-ligand structure prediction with deep learning-based tools, such as AlphaFold3, the large size and highly flexible nature of signaling compounds like LCOs present major challenges for detai

SUBMITTER: T Prates E 

PROVIDER: S-EPMC12270019 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature