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Bioinformatics. 2004 Dec 12;20(18):3346-52. Epub 2004 Jul 9.

Conserved network motifs allow protein-protein interaction prediction.

Author information

1
The Huck Institutes for the Life Sciences, Pennsylvania State University, PA 16802, USA. iua1@psu.edu <iua1@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 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.

PMID:
15247093
DOI:
10.1093/bioinformatics/bth402
[Indexed for MEDLINE]

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