Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

doi:10.1186/1471-2164-10-288

BMC Genomics
Volume 10

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Proost S
Vandepoele K
Rouzé P
Van de Peer Y

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Research article

Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

Stefanie De Bodt¹^,2 , Sebastian Proost¹^,2 , Klaas Vandepoele¹^,2 , Pierre Rouzé¹^,2 and Yves Van de Peer¹^,2

¹Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Technologiepark 927, B-9052 Gent, Belgium

²Department of Plant Biotechnology and Genetics, Gent University, Technologiepark 927, B-9052 Gent, Belgium

author email corresponding author email

BMC Genomics 2009, 10:288doi:10.1186/1471-2164-10-288


Published:	29 June 2009

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.

Conclusion

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.