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基于蛋白质网络功能模块的蛋白质功能预测
引用本文:卢宏超,石秋艳,石宝晨,张治华,赵 屹,唐素勤,熊 磊,王 强,陈润生.基于蛋白质网络功能模块的蛋白质功能预测[J].生物化学与生物物理进展,2006,33(5):446-451.
作者姓名:卢宏超  石秋艳  石宝晨  张治华  赵 屹  唐素勤  熊 磊  王 强  陈润生
作者单位:1. 中国科学院生物物理研究所,北京,100101;中国科学院计算技术研究所,北京,100080;中国科学院研究生院,北京,100049
2. 广西师范大学物理与信息工程学院,桂林,541004
3. 中国科学院生物物理研究所,北京,100101;中国科学院研究生院,北京,100049
4. 中国科学院计算技术研究所,北京,100080
5. 中国科学院生物物理研究所,北京,100101;中国科学院计算技术研究所,北京,100080
摘    要:在破译了基因序列的后基因组时代,随着系统生物学实验的快速发展,产生了大量的蛋白质相互作用数据,利用这些数据寻找功能模块及预测蛋白质功能在功能基因组研究中具有重要意义.打破了传统的基于蛋白质间相似度的聚类模式,直接从蛋白质功能团的角度出发,考虑功能团间的一阶和二阶相互作用,提出了模块化聚类方法(MCM),对实验数据进行聚类分析,来预测模块内未知蛋白质的功能.通过超几何分布P值法和增、删、改相互作用的方法对聚类结果进行预测能力分析和稳定性分析.结果表明,模块化聚类方法具有较高的预测准确度和覆盖率,有很好的容错性和稳定性.此外,模块化聚类分析得到了一些具有高预测准确度的未知蛋白质的预测结果,将会对生物实验有指导意义,其算法对其他具有相似结构的网络也具有普遍意义.

关 键 词:蛋白质相互作用网络  蛋白质功能预测  聚类
收稿时间:2005-11-04
修稿时间:2005-11-042005-12-30

Predicting Protein Function Based on Modularized Protein Interaction Network
LU Hong-Chao,SHI Qiu-Yan,SHI Bao-Chen,ZHANG Zhi-Hu,ZHAO Yi,TANG Su-Qin,XIONG Lei,WANG Qiang and CHEN Run-Sheng.Predicting Protein Function Based on Modularized Protein Interaction Network[J].Progress In Biochemistry and Biophysics,2006,33(5):446-451.
Authors:LU Hong-Chao  SHI Qiu-Yan  SHI Bao-Chen  ZHANG Zhi-Hu  ZHAO Yi  TANG Su-Qin  XIONG Lei  WANG Qiang and CHEN Run-Sheng
Affiliation:Institute of Biophysics, The Chinese Academy of Sciences, Beijing 100101, China;Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100080, China; Graduate University of The Chinese Academy of Sciences, Beijing 100049, China;College of Physics and Information Engineering, Guangxi Normal University, Guilin 541004, China;Institute of Biophysics, The Chinese Academy of Sciences, Beijing 100101, China;Graduate University of The Chinese Academy of Sciences, Beijing 100049, China;Institute of Biophysics, The Chinese Academy of Sciences, Beijing 100101, China;Graduate University of The Chinese Academy of Sciences, Beijing 100049, China;Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100080, China;College of Physics and Information Engineering, Guangxi Normal University, Guilin 541004, China;College of Physics and Information Engineering, Guangxi Normal University, Guilin 541004, China;College of Physics and Information Engineering, Guangxi Normal University, Guilin 541004, China;Institute of Biophysics, The Chinese Academy of Sciences, Beijing 100101, China;Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100080, China
Abstract:In the post-genomics era in which gene sequences have been decoded, large-scale protein-protein interaction data are generated with the rapid development of system biology experiments. It is important in functional genomics to search for function modules and predict protein functions from the data. A new method called modularized clustering method(MCM), which are based on the direct and second-order interactions of modules, is applied to the latest high-throughput protein-protein network of yeast to predict the function of unknown proteins in the modules. P value of hypergeometric cumulative distribution of modules and the disturbance analysis on the data, including adding, removing and rewiring interactions, are employed to evaluate the prediction quality and robustness of the method. The results show that MCM has high prediction precise rate and coverage, and it is robust to high false-positive data and missing data. The predicted results of unknown proteins with high prediction precise rate can be instructive in biological analysis and the algorithm can be generalized to other networks with the similar structures.
Keywords:protein-protein interaction network  predicting function of proteins  clustering
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