Research article

Interspecies data mining to predict novel ING-protein interactions in human

Paul MK Gordon^{* 1} , Mohamed A Soliman^{* 1,2,3} , Pinaki Bose^1,2 , Quang Trinh¹ , Christoph W Sensen¹ and Karl Riabowol^1,2

¹Department of Biochemistry & Molecular Biology, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada

²Department of Oncology, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada

³Department of Biochemistry, Faculty of Pharmacy, Cairo University, Cairo, Egypt

author email corresponding author email* Contributed equally

BMC Genomics 2008, 9:426doi:10.1186/1471-2164-9-426

Published:	18 September 2008

Abstract

Background

The INhibitor of Growth (ING) family of type II tumor suppressors (ING1–ING5) is involved in many cellular processes such as cell aging, apoptosis, DNA repair and tumorigenesis. To expand our understanding of the proteins with which the ING proteins interact, we designed a method that did not depend upon large-scale proteomics-based methods, since they may fail to highlight transient or relatively weak interactions. Here we test a cross-species (yeast, fly, and human) bioinformatics-based approach to identify potential human ING-interacting proteins with higher probability and accuracy than approaches based on screens in a single species.

Results

We confirm the validity of this screen and show that ING1 interacts specifically with three of the three proteins tested; p38MAPK, MEKK4 and RAD50. These novel ING-interacting proteins further link ING proteins to cell stress and DNA damage signaling, providing previously unknown upstream links to DNA damage response pathways in which ING1 participates. The bioinformatics approach we describe can be used to create an interaction prediction list for any human proteins with yeast homolog(s).

Conclusion

None of the validated interactions were predicted by the conventional protein-protein interaction tools we tested. Validation of our approach by traditional laboratory techniques shows that we can extract value from the voluminous weak interaction data already elucidated in yeast and fly databases. We therefore propose that the weak (low signal to noise ratio) data from large-scale interaction datasets are currently underutilized.