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This article is part of the supplement: APBioNet – Fifth International Conference on Bioinformatics (InCoB2006)

Open Access Proceedings

Fly-DPI: database of protein interactomes for D. melanogaster in the approach of systems biology

Chung-Yen Lin123*, Shu-Hwa Chen4, Chi-Shiang Cho1, Chia-Ling Chen1, Fan-Kai Lin1, Chieh-Hua Lin1, Pao-Yang Chen1, Chen-Zen Lo1 and Chao A Hsiung1*

Author Affiliations

1 Division of Biostatistics and Bioinformatics, National Health Research Institutes. No. 35 Keyan Rd. Zhunan, Miaoli County 350, Taiwan

2 Institute of Information Science, Academia Sinica, No. 128 Yan-Chiu-Yuan Rd., Sec. 2, Taipei 115, Taiwan

3 Institute of Fishery Science, National Taiwan University, No. 1, Sec 4, Roosevelt Road, Taipei, 10617, Taiwan

4 Stem Cell/Regenerative Medicine Program, Genomics Research Center, Academia Sinica., No. 128 Yan-Chiu-Yuan Rd., Sec. 2, Taipei 115, Taiwan

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BMC Bioinformatics 2006, 7(Suppl 5):S18  doi:10.1186/1471-2105-7-S5-S18

Published: 18 December 2006

Abstract

Background

Proteins control and mediate many biological activities of cells by interacting with other protein partners. This work presents a statistical model to predict protein interaction networks of Drosophila melanogaster based on insight into domain interactions.

Results

Three high-throughput yeast two-hybrid experiments and the collection in FlyBase were used as our starting datasets. The co-occurrences of domains in these interactive events are converted into a probability score of domain-domain interaction. These scores are used to infer putative interaction among all available open reading frames (ORFs) of fruit fly. Additionally, the likelihood function is used to estimate all potential protein-protein interactions.

All parameters are successfully iterated and MLE is obtained for each pair of domains. Additionally, the maximized likelihood reaches its converged criteria and maintains the probability stable. The hybrid model achieves a high specificity with a loss of sensitivity, suggesting that the model may possess major features of protein-protein interactions. Several putative interactions predicted by the proposed hybrid model are supported by literatures, while experimental data with a low probability score indicate an uncertain reliability and require further proof of interaction.

Fly-DPI is the online database used to present this work. It is an integrated proteomics tool with comprehensive protein annotation information from major databases as well as an effective means of predicting protein-protein interactions. As a novel search strategy, the ping-pong search is a naïve path map between two chosen proteins based on pre-computed shortest paths. Adopting effective filtering strategies will facilitate researchers in depicting the bird's eye view of the network of interest. Fly-DPI can be accessed at http://flydpi.nhri.org.tw webcite.

Conclusion

This work provides two reference systems, statistical and biological, to evaluate the reliability of protein interaction. First, the hybrid model statistically estimates both experimental and predicted protein interaction relationships. Second, the biological information for filtering and annotation itself is a strong indicator for the reliability of protein-protein interaction. The space-temporal or stage-specific expression patterns of genes are also critical for identifying proteins involved in a particular situation.