title: Computational identification of protein-protein interactions in rice based on the predicted rice interactome network.
abstract: 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.
authors.affiliation: Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
authors.forename: Pengcheng
authors.initials: P
authors.lastname: Zhu
authors.forename: Haibin
authors.initials: H
authors.lastname: Gu
authors.forename: Yinming
authors.initials: Y
authors.lastname: Jiao
authors.forename: Donglin
authors.initials: D
authors.lastname: Huang
authors.forename: Ming
authors.initials: M
authors.lastname: Chen
mesh_terms: D019295:Computational Biology; D012275:Oryza / Q000235:genetics* / Q000378:metabolism*; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps