title: Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression.
abstract: BACKGROUND
  Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome.
  
  
  RESULTS
  In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization) and components (e.g. ARPs, actin-related proteins) exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively.
  
  
  CONCLUSIONS
  We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.
authors: Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Technologiepark 927, B-9052 Gent, Belgium. stefanie.debodt@psb.vib-ugent.be
authors: Stefanie
authors: S
authors: De Bodt
authors: Sebastian
authors: S
authors: Proost
authors: Klaas
authors: K
authors: Vandepoele
authors: Pierre
authors: P
authors: Rouzé
authors: Yves
authors: Y
authors: Van de Peer
mesh_terms: D017360:Arabidopsis / Q000235:genetics* / Q000378:metabolism; D029681:Arabidopsis Proteins / Q000235:genetics / Q000378:metabolism*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D025941:Protein Interaction Mapping / Q000379:methods*; D040901:Proteomics; D020539:Sequence Analysis, Protein
chemical_list: D029681:Arabidopsis Proteins