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Structure-based prediction of protein-protein interaction network in rice.
Sun, Fangnan; Deng, Yaxin; Ma, Xiaosong; Liu, Yuan; Zhao, Lingxia; Yu, Shunwu; Zhang, Lida.
Affiliation
  • Sun F; Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China.
  • Deng Y; Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China.
  • Ma X; Shanghai Academy of Agricultural Sciences, Shanghai Agrobiological Gene Center, Shanghai, China.
  • Liu Y; Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China.
  • Zhao L; Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China.
  • Yu S; Shanghai Academy of Agricultural Sciences, Shanghai Agrobiological Gene Center, Shanghai, China.
  • Zhang L; Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China.
Genet Mol Biol ; 47(1): e20230068, 2024.
Article in En | MEDLINE | ID: mdl-38314883
ABSTRACT
Comprehensive protein-protein interaction (PPI) maps are critical for understanding the functional organization of the proteome, but challenging to produce experimentally. Here, we developed a computational method for predicting PPIs based on protein docking. Evaluation of performance on benchmark sets demonstrated the ability of the docking-based method to accurately identify PPIs using predicted protein structures. By employing the docking-based method, we constructed a structurally resolved PPI network consisting of 24,653 interactions between 2,131 proteins, which greatly extends the current knowledge on the rice protein-protein interactome. Moreover, we mapped the trait-associated single nucleotide polymorphisms (SNPs) to the structural interactome, and computationally identified 14 SNPs that had significant consequences on PPI network. The protein structural interactome map provided a resource to facilitate functional investigation of PPI-perturbing alleles associated with agronomically important traits in rice.

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Genet Mol Biol Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Genet Mol Biol Year: 2024 Document type: Article