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

BMC Genomics. 2009 Jun 29;10:288. doi: 10.1186/1471-2164-10-288.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Arabidopsis / genetics*
  • Arabidopsis / metabolism
  • Arabidopsis Proteins / genetics
  • Arabidopsis Proteins / metabolism*
  • Cluster Analysis
  • Computational Biology / methods*
  • Gene Expression Profiling
  • Protein Interaction Mapping / methods*
  • Proteomics
  • Sequence Analysis, Protein

Substances

  • Arabidopsis Proteins