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ARS Home » Southeast Area » Stuttgart, Arkansas » Dale Bumpers National Rice Research Center » Research » Publications at this Location » Publication #418111
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: Receptor-ligand interactions in the rice blast system revealed by AlphaFold protein structure prediction

Author
item WANG, LI - Oak Ridge Institute For Science And Education (ORISE)
item Jia, Yulin
item OLSEN, KENNETH - Washington University
item HUANG, YIXIAO - Oak Ridge Institute For Science And Education (ORISE)
item Jia, Melissa
item SATHISH, PONNIAH - University Of Arkansas At Pine Bluff
item PEDROZO, RODRIGO - Oak Ridge Institute For Science And Education (ORISE)
item NICOLLI, CAMILA - Rice Research And Extension Center
item Edwards, Jeremy

Submitted to: Meeting Abstract
Publication Type: Proceedings
Publication Acceptance Date: 8/13/2024
Publication Date: 8/13/2024
Citation: Wang, L., Jia, Y., Olsen, K., Huang, Y., Jia, M.H., Sathish, P., Pedrozo, R., Nicolli, C., Edwards, J. 2024. Receptor-ligand interactions in the rice blast system revealed by AlphaFold protein structure prediction. Abstract. 2024 International Symposium on Rice Functional Genomics, September 9-11, 2024. Little Rock/Stuttgart, Arkansas.

Interpretive Summary:

Technical Abstract: Rice blast, caused by the fungus Magnaporthe oryzae, is a significant threat to rice production worldwide. Over the years, numerous researchers have identified more than 100 resistance (R) genes that provide some level of defense against this disease. A key aspect of plant innate immunity involves the recognition of pathogen avirulence (AVR) gene products by products of corresponding resistance (R) genes, often following a gene-for-gene model. This makes it essential to identify the corresponding AVR genes to fully understand the mechanisms of resistance. However, for most identified R genes, their interacting AVR genes remain unknown, and traditional biological experiments to discover these AVR genes are time-consuming, labor-intensive, and challenging. In contrast, Artificial Intelligence (AI)-based approaches, like the recent advancements in protein structure prediction by AlphaFold, offer significant advantages in this area. In this study, we present our progress in using AlphaFold to predict interactions between R genes and AVR genes, potentially paving the way for more targeted and effective strategies in rice blast resistance breeding.