Find-Health-Articles.com - making medical research available to everyone
Research article summary (published 29 Jun 2007):
Free Full Text!
See links below

Computational prediction of host-pathogen protein-protein interactions.

Full 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.

SUPPLEMENTARY INFORMATION:
Supplementary data are available at Bioinformatics online.

 

Learn Faster Today      Improve your study skills

Author information

Author/s: Dyer, Matthew D (MD); Murali, T M (TM); Sobral, Bruno W (BW);

Affiliation: Genetics, Bioinformatics and Computational Biology Program, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. dyermd(-atsign-)vbi.vt.edu

Journal and publication information

Publication Type: Journal Article; Research Support, U.S. Gov't, Non-P.H.S.

Journal: Bioinformatics (Oxford, England) (Bioinformatics), published in England. (Language: eng)

Reference: 2007-Jul; vol 23 (issue 13) : pp i159-66

Dates: Created 2007/07/24; Completed 2007/08/27; Revised 2007/11/15;

PMID: 17646292, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

External Links for this article (including full text providers, if available):

Click Electronic Full-text Provider Links to see options for finding the electronic full text links to this article. Note there may be a subscription or fee required for access to the full text. See our FAQ for information on finding FREE full text articles.

This article may also be located in paper journal collections available in many libraries. Use the Journal and Publication Information above to find the full article.

MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Associated Chemicals: Proteome (0)

Related articles

These are the highest related articles currently in the database:

See 100+ related articles.

Related Article Map

1/30/2006
10/24/2007
Higher Relevance Score (318/1000)
Lower Relevance Score (191/1000)

Legend: - FREE Full text Article. - Abstract only. - Title only. More help.

See a large map of 100+ related articles.

© Advanogy.com 2003-2008 (ACN 104 198 263) - All rights reserved. Terms of Use | Contact Us | Index