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Area of Science:

  • Plant pathology
  • Microbial genetics
  • Bioinformatics

Background:

  • Plant pathogens secrete numerous small secreted proteins (SSPs) with unknown functions.
  • Understanding these SSPs is crucial for plant defense strategies.

Purpose of the Study:

  • To screen tomato pathogen SSPs for interactions with tomato defense hydrolases using AI.
  • To identify novel SSPs that inhibit plant immune responses.

Main Methods:

  • Utilized AlphaFold-Multimer (AFM) to predict interactions between 1879 SSPs and 6 tomato hydrolases.
  • Experimentally verified predicted inhibitors of pathogenesis-related subtilase P69B.

Main Results:

  • Identified 15 non-annotated SSPs predicted to inhibit chitinases and proteases.
  • Four SSPs, including Ecp36 and Six15, were confirmed as P69B inhibitors.
  • P69B acts as an effector hub targeted by diverse microbial pathogens.

Conclusions:

  • AI, specifically AFM, is powerful for predicting cross-kingdom interactions at the plant-pathogen interface.
  • Pathogen SSPs targeting P69B represent a conserved strategy across fungal, bacterial, and oomycete pathogens.
  • This study enhances our understanding of plant-pathogen molecular interactions and potential control strategies.