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Open Access Research

Physical and in silico approaches identify DNA-PK in a Tax DNA-damage response interactome

Emad Ramadan1, Michael Ward23, Xin Guo3, Sarah S Durkin37, Adam Sawyer3, Marcelo Vilela4, Christopher Osgood5, Alex Pothen6 and Oliver J Semmes23*

Author Affiliations

1 Department of Computer Science, Old Dominion University, Norfolk, VA, USA

2 George L. Wright Center for Biomedical Proteomics, Eastern Virginia Medical School, Norfolk, VA, USA

3 Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, VA, USA

4 Laboratorio do Cancer, Univeridade Federal de Vicosa, Minas Gerais, Brazil

5 Department of Biology, Old Dominion University, Norfolk, VA, USA

6 Department of Computer Sciences and Computing Research Institute, Purdue University, West Lafayette IN, USA

7 Department of Exploratory Biology, Pfizer Global Research and Development, La Jolla, CA, USA

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Retrovirology 2008, 5:92  doi:10.1186/1742-4690-5-92

Published: 15 October 2008

Abstract

Background

We have initiated an effort to exhaustively map interactions between HTLV-1 Tax and host cellular proteins. The resulting Tax interactome will have significant utility toward defining new and understanding known activities of this important viral protein. In addition, the completion of a full Tax interactome will also help shed light upon the functional consequences of these myriad Tax activities. The physical mapping process involved the affinity isolation of Tax complexes followed by sequence identification using tandem mass spectrometry. To date we have mapped 250 cellular components within this interactome. Here we present our approach to prioritizing these interactions via an in silico culling process.

Results

We first constructed an in silico Tax interactome comprised of 46 literature-confirmed protein-protein interactions. This number was then reduced to four Tax-interactions suspected to play a role in DNA damage response (Rad51, TOP1, Chk2, 53BP1). The first-neighbor and second-neighbor interactions of these four proteins were assembled from available human protein interaction databases. Through an analysis of betweenness and closeness centrality measures, and numbers of interactions, we ranked proteins in the first neighborhood. When this rank list was compared to the list of physical Tax-binding proteins, DNA-PK was the highest ranked protein common to both lists. An overlapping clustering of the Tax-specific second-neighborhood protein network showed DNA-PK to be one of three bridge proteins that link multiple clusters in the DNA damage response network.

Conclusion

The interaction of Tax with DNA-PK represents an important biological paradigm as suggested via consensus findings in vivo and in silico. We present this methodology as an approach to discovery and as a means of validating components of a consensus Tax interactome.