Reconstruction of human protein interolog network using evolutionary conserved network
1 Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan
2 Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
3 Division of Biostatistics and Bioinformatics, National Health Research Institutes, Taipei 115, Taiwan
4 Institute of Fishery Science, National Taiwan University, Taipei 106, Taiwan
5 Institute for Information Industry, Taipei 106, Taiwan
BMC Bioinformatics 2007, 8:152 doi:10.1186/1471-2105-8-152Published: 10 May 2007
The recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. Specifically, the availability of information about experimental protein-protein interactions and other protein features on the Internet enables human protein-protein interactions to be computationally predicted from co-evolution events (interolog). This study also considers other protein interaction features, including sub-cellular localization, tissue-specificity, the cell-cycle stage and domain-domain combination. Computational methods need to be developed to integrate these heterogeneous biological data to facilitate the maximum accuracy of the human protein interaction prediction.
This study proposes a relative conservation score by finding maximal quasi-cliques in protein interaction networks, and considering other interaction features to formulate a scoring method. The scoring method can be adopted to discover which protein pairs are the most likely to interact among multiple protein pairs. The predicted human protein-protein interactions associated with confidence scores are derived from six eukaryotic organisms – rat, mouse, fly, worm, thale cress and baker's yeast.
Evaluation results of the proposed method using functional keyword and Gene Ontology (GO) annotations indicate that some confidence is justified in the accuracy of the predicted interactions. Comparisons among existing methods also reveal that the proposed method predicts human protein-protein interactions more accurately than other interolog-based methods.