This article is part of the supplement: Seventh International Conference on Bioinformatics (InCoB2008) .

Research

Ortholog-based protein-protein interaction prediction and its application to inter-species interactions

Sheng-An Lee^{* 1,4} , Cheng-hsiung Chan^{* 1} , Chi-Hung Tsai⁵ , Jin-Mei Lai⁶ , Feng-Sheng Wang⁷ , Cheng-Yan Kao^4,5 and Chi-Ying F Huang^1,2,3,4

¹Institute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan

²Institute of Bio-Pharmaceutical Sciences, National Yang-Ming University, Taipei 112, Taiwan

³Institute of Biotechnology in Medicine, National Yang-Ming University, Taipei 112, Taiwan

⁴Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan

⁵Institute for Information Industry, Taipei, Taiwan

⁶Department of Life Science, Fu-Jen Catholic University, Taipei Hsien 242, Taiwan

⁷Department of Chemical Engineering, National Chung Cheng University, Chia-Yi 621, Taiwan

author email corresponding author email* Contributed equally

BMC Bioinformatics 2008, 9(Suppl 12):S11doi:10.1186/1471-2105-9-S12-S11

Published:	12 December 2008

Abstract

Background

The rapid growth of protein-protein interaction (PPI) data has led to the emergence of PPI network analysis. Despite advances in high-throughput techniques, the interactomes of several model organisms are still far from complete. Therefore, it is desirable to expand these interactomes with ortholog-based and other methods.

Results

Orthologous pairs of 18 eukaryotic species were expanded and merged with experimental PPI datasets. The contributions of interologs from each species were evaluated. The expanded orthologous pairs enable the inference of interologs for various species. For example, more than 32,000 human interactions can be predicted. The same dataset has also been applied to the prediction of host-pathogen interactions. PPIs between P. falciparum calmodulin and several H. sapiens proteins are predicted, and these interactions may contribute to the maintenance of host cell Ca²⁺ concentration. Using comparisons with Bayesian and structure-based approaches, interactions between putative HSP40 homologs of P. falciparum and the H. sapiens TNF receptor associated factor family are revealed, suggesting a role for these interactions in the interference of the human immune response to P. falciparum.

Conclusion

The PPI datasets are available from POINT http://point.bioinformatics.tw/ webcite and POINeT http://poinet.bioinformatics.tw/ webcite. Further development of methods to predict host-pathogen interactions should incorporate multiple approaches in order to improve sensitivity, and should facilitate the identification of targets for drug discovery and design.