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Predicting Protein-Protein Interactions Between Rice and Blast Fungus Using Structure-Based Approaches.
Zheng, Cunjian; Liu, Yuan; Sun, Fangnan; Zhao, Lingxia; Zhang, Lida.
Afiliación
  • Zheng C; Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China.
  • Liu Y; Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China.
  • Sun F; Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China.
  • Zhao L; Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China.
  • Zhang L; Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China.
Front Plant Sci ; 12: 690124, 2021.
Article en En | MEDLINE | ID: mdl-34367213
ABSTRACT
Rice blast, caused by the fungus Magnaporthe oryzae, is the most devastating disease affecting rice production. Identification of protein-protein interactions (PPIs) is a critical step toward understanding the molecular mechanisms underlying resistance to blast fungus in rice. In this study, we presented a computational framework for predicting plant-pathogen PPIs based on structural information. Compared with the sequence-based methods, the structure-based approach showed to be more powerful in discovering new PPIs between plants and pathogens. Using the structure-based method, we generated a global PPI network consisted of 2,018 interacting protein pairs involving 1,344 rice proteins and 418 blast fungus proteins. The network analysis showed that blast resistance genes were enriched in the PPI network. The network-based prediction enabled systematic discovery of new blast resistance genes in rice. The network provided a global map to help accelerate the identification of blast resistance genes and advance our understanding of plant-pathogen interactions.
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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Plant Sci Año: 2021 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Plant Sci Año: 2021 Tipo del documento: Article