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This article is part of the supplement: Selected articles from the 4th International Conference on Computational Systems Biology (ISB 2010)

Open Access Report

Conditional random field approach to prediction of protein-protein interactions using domain information

Morihiro Hayashida1*, Mayumi Kamada1, Jiangning Song23 and Tatsuya Akutsu1

Author Affiliations

1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan

2 Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia

3 Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China

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BMC Systems Biology 2011, 5(Suppl 1):S8  doi:10.1186/1752-0509-5-S1-S8

Published: 20 June 2011



For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins.


We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. The results suggest that our proposed methods with and without mutual information outperform EM (Expectation Maximization) method proposed by Deng et al., which is one of the best predictors based on domain-domain interactions.


We propose novel methods using conditional random fields with and without mutual information between domains. Our methods based on domain-domain interactions are useful for predicting protein-protein interactions.