Skip to main content
ARS Home » Southeast Area » Stuttgart, Arkansas » Dale Bumpers National Rice Research Center » Research » Publications at this Location » Publication #421896
Research Project: Broadening and Strengthening the Genetic Base of Rice for Adaptation to a Changing Climate, Crop Production Systems, and Markets

Location: Dale Bumpers National Rice Research Center

Title: AlphaFold application in the rice blast system

Author
item WANG, LI - Orise Fellow
item Jia, Yulin
item Edwards, Jeremy

Submitted to: Plant and Animal Genome Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 1/5/2025
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Rice blast disease, caused by the fungus Magnaporthe oryzae, is one of the most severe threats to sustainable rice production worldwide. Over the decades, researchers have characterized more than 50 major rice resistance (R) genes that offer robust defense against this potentially explosive pathogen. Genetic analysis shows that products of rice R genes detect products of pathogen avirulence (AVR) genes in a gene for gene manner. Majority of R proteins contain nucleotide binding sites and leucine rich repeat (NLR) predicted to be as receptors to detect the presences of pathogen ligands. The pathogen AVR proteins (ligand) to trigger resistance response are random molecules presumably involved in pathogenicity and fitness. The molecular mechanisms of NLR proteins detect AVR proteins remain largely unresolved, due in part to the complexity, time demands and labor intensity of protein purification and crystallization. Artificial Intelligence (AI)-based tools, particularly AlphaFold, provide transformative potential for uncovering these interaction mechanisms. In this study, we showed what proteins may interact across plant and pathogen kingdoms for initiating robust signal transduction pathways leading to plant innate immunity demonstrating the true power of AlphaFold in elucidating molecular mechanisms of plant disease resistance. Our findings underscore the promise of AI-based approaches for advancing fundamental plant pathology research in crop plants.