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