Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data

Roded Sharan, Trey Ideker, Brian R. Kelley, Ron Shamir, Richard M. Karp

Research output: Contribution to conferencePaperpeer-review

Abstract

Mounting evidence shows that many protein complexes are conserved in evolution. Here we use conservation to find complexes that are common to yeast 5. Cerevisiae and bacteria H. pylori. Our analysis combines protein interaction data, that are available for each of the two species, and orthology information based on protein sequence comparison. We develop a detailed probabilistic model for protein complexes in a single species, and a model for the conservation of complexes between two species. Using these models, one can recast the question of finding conserved complexes as a problem of searching for heavy subgraphs in an edge- and node-weighted graph, whose nodes are orthologous protein pairs. We tested this approach on the data currently available for yeast and bacteria and detected 11 significantly conserved complexes. Several of these complexes match very well with prior experimental knowledge on complexes in yeast only, and serve for validation of our methodology. The complexes suggest new functions for a variety of uncharacterized proteins. By identifying a conserved complex whose yeast proteins function predominantly in the nuclear pore complex, we propose that the corresponding bacterial proteins function as a coherent cellular membrane transport system. We also compare our results to two alternative methods for detecting complexes, and demonstrate that our methodology obtains a much higher specificity.

Original languageEnglish
Pages282-289
Number of pages8
DOIs
StatePublished - 2004
EventRECOMB 2004 - Proceedings of the Eight Annual International Conference on Research in Computational Molecular Biology - San Diego, CA., United States
Duration: 27 Mar 200431 Mar 2004

Conference

ConferenceRECOMB 2004 - Proceedings of the Eight Annual International Conference on Research in Computational Molecular Biology
Country/TerritoryUnited States
CitySan Diego, CA.
Period27/03/0431/03/04

Keywords

  • Bacteria
  • Comparative analysis
  • Conservation
  • Heavy subgraph
  • Probabilistic model
  • Protein complex
  • Protein interaction network
  • Yeast

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