蛋白质相互作用研究中的计算方法综述  

A Survey of Computational Method in Protein-Protein Interaction Research

作　　者：李舟军[1,2] 陈义明[1,3] 刘军万[1,4] 陈火旺[1] 

机构地区：[1]国防科学技术大学计算机学院,长沙410073 [2]北京航空航天大学计算机学院,北京100083 [3]湖南农业大学信息科学技术学院,长沙410128 [4]中南林业科技大学计算机科学学院,长沙410004

出　　处：《计算机研究与发展》2008年第12期2129-2137,共9页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(60573057,60473057,90604007);湖南农业大学人才科学基金项目(06YJ16)~~

摘　　要：随着分子生物学的研究进入以蛋白质组学为标志的后基因组时代,蛋白质相互作用成为蛋白质组学研究的一个重要主题.因为计算方法代价低和周期短的特点,它被广泛地用来分析相互作用数据从而指导生物学家的实验设计.从蛋白质相互作用网络的构建到分析两个方面综述了蛋白质相互作用研究中的各种计算方法:介绍了通过机器学习方法预测、文本挖掘和评估相互作用的各种技术;特别详细地阐述了相互作用网络的重要参数和典型生物模型,并对运用图论方法分析和计算的各种算法进行了深入的剖析;最后,对蛋白质相互作用的计算研究进行了总结和展望.With molecular biology research coming into the post-genome era focusing on proteomics, protein-protein interaction （PPI） has become an important topic of proteomics. The computational methods are widely used to analyze PPI data and guide experimental design for biologists, because they only need lower cost and have a shorter experimental period. In two aspects of constructing PPI network and analyzing it, all kinds of computational methods are surveyed. When it comes to constructing PPI network, some technologies, which use machine learning methods, are introduced, such as predicting PPI from protein sequence, expression or PPI network, mining PPI from biological medical literature database, evaluating PPI from various PPI data sets and so on. For PPI network analysis, three important and typical network parameters, （network diameter, degree distribution and cluster coefficient of node）, and four models are illustrated in detail. Two PPI network modules with biological significance are graph clustering and network motif, and corresponding algorithms which use graph theory methods, are deeply analyzed. The concept and method about network alignment is also introduced. Finally, the computational research of protein-protein interaction is summarized and prospected.

关 键 词：蛋白质相互作用 相互作用预测 PPI网络参数 PPI网络模型 图论分析 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]