Phytopathogen Effector Biology in the Burgeoning AI Era
Darcy Adam Bain Jones1, Sylvain Raffaele1
1Laboratoire des Interactions Plantes-Microbes-Environnement (LIPME), INRAE, CNRS, Université de Toulouse, Castanet-Tolosan, France; email: darcy.jones@inrae.fr, sylvain.raffaele@inrae.fr.
Annual Review of Phytopathology
|June 20, 2025
Summary
Artificial intelligence (AI) aids in identifying and understanding plant pathogen effector proteins. AI tools accelerate the study of effector functions and evolution, even without clear sequence similarity.
Area of Science:
- Plant pathology
- Computational biology
- Bioinformatics
Background:
- Plant pathogens secrete effector proteins to manipulate host responses during infection.
- Characterizing these effectors is difficult due to their diversity, rapid evolution, and host-specific interactions.
Purpose of the Study:
- To review the application of artificial intelligence (AI) in effector biology.
- To highlight AI's potential in identifying, functionally characterizing, and understanding the evolution of effector proteins.
Main Methods:
- Review of recent advancements in AI for protein biology.
- Focus on AI applications like subcellular localization prediction, protein structural modeling (e.g., AlphaFold), and protein language models.
Main Results:
- AI offers novel approaches to overcome challenges in effector protein characterization.
- AI tools can predict effector localization, structure, and function, complementing experimental methods.
Conclusions:
- AI significantly accelerates the investigation of effector protein functions and evolutionary histories.
- AI is valuable for studying effectors even when sequence similarity or known domains are absent.
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