摘要
一个蛋白质可能在不同条件或不同时刻与不同的蛋白质发生相互作用,这称为蛋白质的动态特性.蛋白质在分子处理的不同阶段参与到不同的模块,与其他的蛋白质共同完成某项功能.因此,动态蛋白质相互作用的研究有助于提高蛋白质功能预测的准确率.结合蛋白质相互作用网络和时间序列基因表达数据,构建动态蛋白质相互作用网络.为降低PPI网络中假阴性对功能预测产生的负面影响,结合结构域信息和复合物信息,预测和产生新的相互作用,并对相互作用加权.基于构建的动态加权网络,提出一种功能预测方法 D-PIN(Dynamic protein interaction networks).基于三个不同的酵母相互作用网络实验结果表明,D-PIN方法的综合性能比现有方法提高了14%以上.结果验证了构建的动态加权蛋白质相互网络的有效性.
A protein would interact with different proteins under different conditions or at different time instants, which is the dynamic attribute of interactions. Proteins participate in different functional modules in different stages of molecular processing to perform different functions with other proteins. So, research of dynamic protein-protein interaction would contribute to the accuracy improvement of protein functions prediction. We construct a dynamic protein interaction network（D-PIN） by integrating protein-protein interaction network and time course gene expression data. To reduce the negative effect of false ＂negative＂ on the protein function prediction, we predict and generate some new protein interactions which combine with proteins domain information and protein complexes information and weight all the interactions. Based on the weighted dynamic network, we propose a method for predicting protein function, named DPIN. Experimental results compared with using three different yeast interactome networks indicate that the comprehensive performance of D-PIN is 14 % higher other competing methods. Results also verify the effectiveness of the constructed dynamic-weighted protein interactome network.
出处
《自动化学报》
EI
CSCD
北大核心
2015年第11期1893-1900,共8页
Acta Automatica Sinica
基金
国家自然科学基金(11501054)
湖南省自然科学基金项目(13JJ4106
14JJ3138)
湖南省教育厅项目(10C0408
15C0124)
湖南省科技计划项目(2010FJ3044
2015GK3072)资助~~
关键词
动态加权网络
功能预测
蛋白质相互作用网络
基因表达
Dynamic weighted network
functions prediction
protein interactome network
gene expression
