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Inferring Protein–Protein Interactions from Multiple Protein Domain Combinations

  • Simon P. Kanaan
  • Chengbang Huang
  • Stefan Wuchty
  • Danny Z. Chen
  • Jesús A. Izaguirre
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 541)

Abstract

The ever accumulating wealth of knowledge about protein interactions and the domain architecture of involved proteins in different organisms offers ways to understand the intricate interplay between interactome and proteome. Ultimately, the combination of these sources of information will allow the prediction of interactions among proteins where only domain composition is known. Based on the currently available protein–protein interaction and domain data of Saccharomyces cerevisiae and Drosophila melanogaster we introduce a novel method, Maximum Specificity Set Cover (MSSC), to predict potential protein–protein interactions. Utilizing interactions and domain architectures of domains as training sets, this algorithm employs a set cover approach to partition domain pairs, which allows the explanation of the underlying protein interaction to the largest degree of specificity. While MSSC in its basic version only considers domain pairs as the driving force between interactions, we also modified the algorithm to account for combinations of more than two domains that govern a protein–protein interaction. This approach allows us to predict the previously unknown protein–protein interactions in S. cerevisiae and D. melanogaster, with a degree of sensitivity and specificity that clearly outscores other approaches. As a proof of concept we also observe high levels of co-expression and decreasing GO distances between interacting proteins. Although our results are very encouraging, we observe that the quality of predictions significantly depends on the quality of interactions, which were utilized as the training set of the algorithm. The algorithm is part of a Web portal available at http://ppi.cse.nd.edu.

Key words

Domain combinations set cover protein interaction prediction 

Notes

Acknowledgments

Danny Chen was supported in part by the NSF under Grant CCF-0515203. Jesús Izaguirre was supported by partial funding from NSF grants IOB-0313730, CCR-0135195, and DBI- 0450067. Stefan Wuchty was supported by the Northwestern Institute of Complexity (NICO).

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Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Simon P. Kanaan
    • 1
  • Chengbang Huang
    • 2
  • Stefan Wuchty
    • 2
  • Danny Z. Chen
    • 2
  • Jesús A. Izaguirre
    • 2
  1. 1.Department of Computer ScienceUniversity of Notre DameNotre DameUSA
  2. 2.Northwestern Institute of ComplexityNorthwestern UniversityEvanstonUSA

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