Bioinformatics Advance Access originally published online on July 9, 2004
Bioinformatics 2004 20(18):3346-3352; doi:10.1093/bioinformatics/bth402
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Bioinformatics vol. 20 issue 18 © Oxford University Press 2004; all rights reserved.
Conserved network motifs allow protein–protein interaction prediction
1 The Huck Institutes for the Life Sciences and 2 Department of Physics, Pennsylvania State University, PA 16802, USA
Received on May 26, 2004; revised on June 21, 2004; accepted on June 22, 2004
Advance Access Publication July 9, 2004
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 and 40% 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.
Contact: iua1{at}psu.edu
* To whom correspondence should be addressed.
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