Sun Fangnan, Deng Yaxin, Ma Xiaosong, Liu Yuan, Zhao Lingxia, Yu Shunwu, Zhang Lida
Shanghai Jiao Tong University, School of Agriculture and Biology, Department of Plant Science, Shanghai, China.
Shanghai Academy of Agricultural Sciences, Shanghai Agrobiological Gene Center, Shanghai, China.
Genet Mol Biol. 2024 Feb 2;47(1):e20230068. doi: 10.1590/1678-4685-GMB-2023-0068. eCollection 2024.
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.
全面的蛋白质-蛋白质相互作用（PPI）图谱对于理解蛋白质组的功能组织至关重要，但通过实验生成具有挑战性。在此，我们开发了一种基于蛋白质对接预测PPI的计算方法。在基准集上对性能的评估证明了基于对接的方法能够使用预测的蛋白质结构准确识别PPI。通过采用基于对接的方法，我们构建了一个结构解析的PPI网络，该网络由2131个蛋白质之间的24653个相互作用组成，极大地扩展了目前关于水稻蛋白质-蛋白质相互作用组的知识。此外，我们将性状相关的单核苷酸多态性（SNP）映射到结构相互作用组，并通过计算鉴定出14个对PPI网络有显著影响的SNP。蛋白质结构相互作用组图谱为促进对与水稻重要农艺性状相关的PPI干扰等位基因的功能研究提供了一种资源。
