Computational Identification of Protein-Protein Interactions in Rice Based on the Predicted Rice Interactome Network

• Published in: Genomics, proteomics & bioinformatics Vol. 9; no. 4; pp. 128 - 137
• Main Authors: Zhu, Pengcheng, Gu, Haibin, Jiao, Yinming, Huang, Donglin, Chen, Ming
• Format: Journal Article
• Language: English
• Published: China Elsevier Ltd 01.10.2011; James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, China; James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, China%Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China%Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; Elsevier
• Subjects: Computational Biology; interolog; Oryza - genetics; Oryza - metabolism; Oryza sativa; pathway clustering; Protein Interaction Mapping; Protein Interaction Maps; protein-protein interactions; rice interactome; sub-network expansion; 基因组功能; 大米蛋白; 水稻; 生物学意义; 蛋白质相互作用网络; 计算; 识别; 预测; interolog; rice interactome; sub-network expansion; pathway clustering; protein-protein interactions
• ISSN: 1672-0229; 2210-3244
• 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 localizationprediction, 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 do- main-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.