Background: The assembly of reliable and complete protein-protein interaction (PPI) maps remains one of the significant challenges in systems biology. Computational methods which integrate and prioritize interaction data can greatly aid in approaching this goal.

Results: We developed a Bayesian inference framework which uses phylogenetic relationships to guide the integration of PPI evidence across multiple datasets and species, providing more accurate predictions. We apply our framework to reconcile seven eukaryotic interactomes: H. sapiens, M. musculus, R. norvegicus, D. melanogaster, C. elegans, S. cerevisiae and A. thaliana. Comprehensive GO-based quality assessment indicates a 5% to 44% score increase in predicted interactomes compared to the input data. Further support is provided by gold-standard MIPS, CYC2008 and HPRD datasets. We demonstrate the ability to recover known PPIs in well-characterized yeast and human complexes (26S proteasome, endosome and exosome) and suggest possible new partners interacting with the putative SWI/SNF chromatin remodeling complex in A. thaliana.

Conclusion: Our phylogeny-guided approach compares favorably to two standard methods for mapping PPIs across species. Detailed analysis of predictions in selected functional modules uncovers specific PPI profiles among homologous proteins, establishing interaction-based partitioning of protein families. Provided evidence also suggests that interactions within core complex subunits are in general more conserved and easier to transfer accurately to other organisms, than interactions between these subunits.

• PMID: 19948065
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Journal Article | Research Support, Non-U.S. Gov't

Authors

Dutkowski J, Tiuryn J

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