Analysis on multi-domain cooperation for predicting protein-protein interactions
- Equal contributors
1 School of Information, Renmin University of China, Beijing 100872, China
2 Department of Electronics Information and Communication Engineering, Osaka Sangyo University, Osaka 574-8530, Japan
3 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China
4 Institute of Systems Biology, Shanghai University, Shanghai 200444, China
5 ERATO Aihara Complexity Modelling Project, JST, Tokyo 151-0064, Japan
6 Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
BMC Bioinformatics 2007, 8:391 doi:10.1186/1471-2105-8-391Published: 16 October 2007
Domains are the basic functional units of proteins. It is believed that protein-protein interactions are realized through domain interactions. Revealing multi-domain cooperation can provide deep insights into the essential mechanism of protein-protein interactions at the domain level and be further exploited to improve the accuracy of protein interaction prediction.
In this paper, we aim to identify cooperative domains for protein interactions by extending two-domain interactions to multi-domain interactions. Based on the high-throughput experimental data from multiple organisms with different reliabilities, the interactions of domains were inferred by a Linear Programming algorithm with Multi-domain pairs (LPM) and an Association Probabilistic Method with Multi-domain pairs (APMM). Experimental results demonstrate that our approach not only can find cooperative domains effectively but also has a higher accuracy for predicting protein interaction than the existing methods. Cooperative domains, including strongly cooperative domains and superdomains, were detected from major interaction databases MIPS and DIP, and many of them were verified by physical interactions from the crystal structures of protein complexes in PDB which provide intuitive evidences for such cooperation. Comparison experiments in terms of protein/domain interaction prediction justified the benefit of considering multi-domain cooperation.
From the computational viewpoint, this paper gives a general framework to predict protein interactions in a more accurate manner by considering the information of both multi-domains and multiple organisms, which can also be applied to identify cooperative domains, to reconstruct large complexes and further to annotate functions of domains. Supplementary information and software are provided in http://intelligent.eic.osaka-sandai.ac.jp/chenen/MDCinfer.htm webcite and http://zhangroup.aporc.org/bioinfo/MDCinfer webcite.