Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
Genomics Proteomics Bioinformatics. 2011 Oct;9(4-5):128-37. doi: 10.1016/S1672-0229(11)60016-8.
Plant protein-protein interaction networks have not been identified by large-scale experiments. In order to better understand the protein interactions in rice, the Predicted Rice Interactome Network (PRIN; http://bis.zju.edu.cn/prin/) presented 76,585 predicted interactions involving 5,049 rice proteins. After mapping genomic features of rice (GO annotation, subcellular localization prediction, and gene expression), we found that a well-annotated and biologically significant network is rich enough to capture many significant functional linkages within higher-order biological systems, such as pathways and biological processes. Furthermore, we took MADS-box domain-containing proteins and circadian rhythm signaling pathways as examples to demonstrate that functional protein complexes and biological pathways could be effectively expanded in our predicted network. The expanded molecular network in PRIN has considerably improved the capability of these analyses to integrate existing knowledge and provide novel insights into the function and coordination of genes and gene networks.
植物蛋白质-蛋白质相互作用网络尚未通过大规模实验来鉴定。为了更好地了解水稻中的蛋白质相互作用，预测水稻互作网络（PRIN；http://bis.zju.edu.cn/prin/）呈现了涉及 5049 个水稻蛋白的 76585 个预测相互作用。在映射水稻的基因组特征（GO 注释、亚细胞定位预测和基因表达）之后，我们发现一个注释良好且具有生物学意义的网络足够丰富，可以捕获更高级别生物系统（如途径和生物过程）中的许多重要功能联系。此外，我们以 MADS 盒结构域蛋白和昼夜节律信号通路为例，证明了功能蛋白质复合物和生物途径可以在我们的预测网络中有效地扩展。PRIN 中的扩展分子网络极大地提高了这些分析的能力，使其能够整合现有知识，并为基因和基因网络的功能和协调提供新的见解。
