Graduate School of System Informatics, Kobe University, Rokkodai, Nada, Kobe 657-8501, Japan.
BMC Bioinformatics. 2011 Feb 15;12 Suppl 1(Suppl 1):S39. doi: 10.1186/1471-2105-12-S1-S39.
Recently, revealing the function of proteins with protein-protein interaction (PPI) networks is regarded as one of important issues in bioinformatics. With the development of experimental methods such as the yeast two-hybrid method, the data of protein interaction have been increasing extremely. Many databases dealing with these data comprehensively have been constructed and applied to analyzing PPI networks. However, few research on prediction interaction sites using both PPI networks and the 3D protein structures complementarily has explored.
We propose a method of predicting interaction sites in proteins with unknown function by using both of PPI networks and protein structures. For a protein with unknown function as a target, several clusters are extracted from the neighboring proteins based on their structural similarity. Then, interaction sites are predicted by extracting similar sites from the group of a protein cluster and the target protein. Moreover, the proposed method can improve the prediction accuracy by introducing repetitive prediction process.
The proposed method has been applied to small scale dataset, then the effectiveness of the method has been confirmed. The challenge will now be to apply the method to large-scale datasets.
最近，揭示蛋白质-蛋白质相互作用（PPI）网络的功能被认为是生物信息学中的重要问题之一。随着酵母双杂交等实验方法的发展，蛋白质相互作用的数据呈指数级增长。许多综合处理这些数据的数据库已经被构建并应用于分析 PPI 网络。然而，很少有研究探索同时利用 PPI 网络和三维蛋白质结构互补来预测相互作用位点。
我们提出了一种利用 PPI 网络和蛋白质结构预测未知功能蛋白质相互作用位点的方法。对于一个未知功能的目标蛋白质，我们根据其结构相似性从其邻近蛋白质中提取出几个簇。然后，通过从蛋白质簇和目标蛋白质的组中提取相似的位点来预测相互作用位点。此外，该方法通过引入重复预测过程可以提高预测准确性。
该方法已经应用于小规模数据集，然后验证了该方法的有效性。现在的挑战是将该方法应用于大规模数据集。
