MOTIVATION: The increase in the amount of available protein-protein interaction (PPI) data enables us to develop computational methods for protein complex predictions. A protein complex is a group of proteins that interact with each other at the same time and place. The protein complex generally corresponds to a cluster in PPI network (PPIN). However, clusters correspond not only to protein complexes but also to sets of proteins that interact dynamically with each other. As a result, conventional graph-theoretic clustering methods that disregard interaction dynamics show high false positive rates in protein complex predictions. RESULTS: In this article, a method of refining PPIN is proposed that uses the structural interface data of protein pairs for protein complex predictions. A simultaneous protein interaction network (SPIN) is introduced to specify mutually exclusive interactions (MEIs) as indicated from the overlapping interfaces and to exclude competition from MEIs that arise during the detection of protein complexes. After constructing SPINs, naive clustering algorithms are applied to the SPINs for protein complex predictions. The evaluation results show that the proposed method outperforms the simple PPIN-based method in terms of removing false positive proteins in the formation of complexes. This shows that excluding competition between MEIs can be effective for improving prediction accuracy in general computational approaches involving protein interactions. AVAILABILITY: http://code.google.com/p/simultaneous-pin/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we propose a formulated method for the analysis of conserved domain combinations and report an overview of domain combinations by identifying domain patterns and analyzing their functional annotations. The proposed method measures co-occurrence frequency and mutual dependency of domains in a domain combination using association rules. The method is useful to estimate the meaningfulness of a given domain combination in terms of conservation. Using the method, we extracted domain patterns in S.cerevisiae proteins and investigated GO term annotations of the domains. According to the investigation, domains in S.cerevisiae proteins are turned out to form patterns in which the members of the patterns are highly affiliated to one another. Also, extracted patterns are revealed to have a tendency of being associated with molecular functions.