Conserved network motifs allow protein-protein interaction prediction

Bioinformatics. 2004 Dec 12;20(18):3346-52. doi: 10.1093/bioinformatics/bth402. Epub 2004 Jul 9.


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

Publication types

  • Comparative Study
  • Evaluation Study

MeSH terms

  • Algorithms*
  • Amino Acid Motifs
  • Amino Acid Sequence
  • Binding Sites
  • Conserved Sequence
  • Molecular Sequence Data
  • Protein Binding
  • Protein Interaction Mapping / methods*
  • Saccharomyces cerevisiae / metabolism*
  • Saccharomyces cerevisiae Proteins / chemistry*
  • Saccharomyces cerevisiae Proteins / metabolism*
  • Sequence Alignment / methods*
  • Sequence Analysis, Protein / methods*
  • Sequence Homology, Amino Acid
  • Signal Transduction / physiology


  • Saccharomyces cerevisiae Proteins