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Bioinformatics 2007 23(13):i159-i166; doi:10.1093/bioinformatics/btm208
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Computational prediction of host-pathogen protein–protein interactions

Matthew D. Dyer 1,2,*, T. M. Murali 3 and Bruno W. Sobral 2

1Genetics, Bioinformatics and Computational Biology Program, 2Virginia Bioinformatics Institute and 3Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg,VA 24061, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Infectious diseases such as malaria result in millions of deaths each year. An important aspect of any host-pathogen system is the mechanism by which a pathogen can infect its host. One method of infection is via protein–protein interactions (PPIs) where pathogen proteins target host proteins. Developing computational methods that identify which PPIs enable a pathogen to infect a host has great implications in identifying potential targets for therapeutics.

Results: We present a method that integrates known intra-species PPIs with protein-domain profiles to predict PPIs between host and pathogen proteins. Given a set of intra-species PPIs, we identify the functional domains in each of the interacting proteins. For every pair of functional domains, we use Bayesian statistics to assess the probability that two proteins with that pair of domains will interact. We apply our method to the Homo sapiens – Plasmodium falciparum host-pathogen system. Our system predicts 516 PPIs between proteins from these two organisms. We show that pairs of human proteins we predict to interact with the same Plasmodium protein are close to each other in the human PPI network and that Plasmodium pairs predicted to interact with same human protein are co-expressed in DNA microarray datasets measured during various stages of the Plasmodium life cycle. Finally, we identify functionally enriched sub-networks spanned by the predicted interactions and discuss the plausibility of our predictions.

Availability: Supplementary data are available at http://staff.vbi.vt.edu/dyermd/publications/dyer2007a.html

Contact: dyermd{at}vbi.vt.edu

Supplementary information: Supplementary data are available at Bioinformatics online.


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