Conserved network motifs allow protein-protein interaction prediction.
BACKGROUND
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.


BACKGROUND
A reference implementation and a table with candidate interacting partners for each yeast protein are available at http://www.protsuggest.org.
Albert
István
I
The Huck Institutes for the Life Sciences, Pennsylvania State University, PA 16802, USA. iua1@psu.edu <iua1@psu.edu>
Albert
Réka
R
D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D001665:Binding Sites; D017124:Conserved Sequence; D008969:Molecular Sequence Data; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry* / Q000378:metabolism*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D015398:Signal Transduction / Q000502:physiology
D029701:Saccharomyces cerevisiae Proteins