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Bioinformatics Advance Access published online on July 9, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth402
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received May 26, 2004
Revised June 21, 2004
Accepted June 22, 2004

Article

Conserved network motifs allow protein-protein interaction prediction

István Albert 1* Réka Albert 2

1 The Huck Institutes for the Life Sciences, Pennsylvania State University
2 The Huck Institutes for the Life Sciences, Pennsylvania State University; Department of Physics, Pennsylvania State University

* To whom correspondence should be addressed. E-mail: iua1{at}psu.edu.


  Abstract

Motivation: High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics.

Results: We show that we can use the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a "leave-one-out" approach we find average success rates between 20-50% for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network.

Availability: A reference implementation and a table with candidate interacting partners for each yeast protein are available at http://www.protsuggest.org.


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