Highly precise protein-protein interaction prediction based on consensus between template-based and de novo docking methods.
Highly precise protein-protein interaction prediction based on consensus between template-based and de novo docking methods.
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DOI：
10.1186/1753-6561-7-s7-s6
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发表时间：
2013-12-20
期刊：
影响因子：
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通讯作者：
Akiyama Y
中科院分区：
文献类型：
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作者：
Ohue M;Matsuzaki Y;Shimoda T;Ishida T;Akiyama Y
阐明蛋白质-蛋白质相互作用（PPI）网络对于理解疾病机制和药物发现具有重要意义。基于三级结构的有机硅PPI预测方法有两种典型的方法：一种是基于已知蛋白质结构的模板匹配方法，另一种是基于从头蛋白对接方法。然而，基于模板的方法由于使用了模板信息，适用范围较窄，基于从头对接的方法预测性能不佳。此外，这两种计算机预测方法的精度都不够，并且需要通过生物实验来验证预测的ppi，导致相当大的支出；因此，需要更高精度的PPI预测方法。将基于模板的预测与从头对接预测相结合，提出了一种基于结构的PPI预测方法。当我们将该方法应用于人类凋亡信号通路时，我们获得的精度值为0.333，高于传统方法（基于模板的PRISM方法为0.231，非基于模板的MEGADOCK方法为0.145），同时保持与传统方法（PRISM方法为0.296，MEGADOCK方法为0.220）相当的f测量值（0.285）。我们的共识方法成功地预测了PPI网络，比传统的模板/非模板方法具有更高的精度，因此可以减少通过实验室实验从预测的PPI中确认新PPI的验证成本。因此，我们的方法可以作为促进相互作用组分析的辅助手段。
Elucidation of protein-protein interaction (PPI) networks is important for understanding disease mechanisms and for drug discovery. Tertiary-structure-based in silico PPI prediction methods have been developed with two typical approaches: a method based on template matching with known protein structures and a method based on de novo protein docking. However, the template-based method has a narrow applicable range because of its use of template information, and the de novo docking based method does not have good prediction performance. In addition, both of these in silico prediction methods have insufficient precision, and require validation of the predicted PPIs by biological experiments, leading to considerable expenditure; therefore, PPI prediction methods with greater precision are needed. We have proposed a new structure-based PPI prediction method by combining template-based prediction and de novo docking prediction. When we applied the method to the human apoptosis signaling pathway, we obtained a precision value of 0.333, which is higher than that achieved using conventional methods (0.231 for PRISM, a template-based method, and 0.145 for MEGADOCK, a non-template-based method), while maintaining an F-measure value (0.285) comparable to that obtained using conventional methods (0.296 for PRISM, and 0.220 for MEGADOCK). Our consensus method successfully predicted a PPI network with greater precision than conventional template/non-template methods, which may thus reduce the cost of validation by laboratory experiments for confirming novel PPIs from predicted PPIs. Therefore, our method may serve as an aid for promoting interactome analysis.