Yu Haiyuan, Zhu Xiaowei, Greenbaum Dov, Karro John, Gerstein Mark
Department of Molecular Biophysics and Biochemistry, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520, USA.
Nucleic Acids Res. 2004 Jan 14;32(1):328-37. doi: 10.1093/nar/gkh164. Print 2004.
Biological networks are a topic of great current interest, particularly with the publication of a number of large genome-wide interaction datasets. They are globally characterized by a variety of graph-theoretic statistics, such as the degree distribution, clustering coefficient, characteristic path length and diameter. Moreover, real protein networks are quite complex and can often be divided into many sub-networks through systematic selection of different nodes and edges. For instance, proteins can be sub-divided by expression level, length, amino-acid composition, solubility, secondary structure and function. A challenging research question is to compare the topologies of sub- networks, looking for global differences associated with different types of proteins. TopNet is an automated web tool designed to address this question, calculating and comparing topological characteristics for different sub-networks derived from any given protein network. It provides reasonable solutions to the calculation of network statistics for sub-networks embedded within a larger network and gives simplified views of a sub-network of interest, allowing one to navigate through it. After constructing TopNet, we applied it to the interaction networks and protein classes currently available for yeast. We were able to find a number of potential biological correlations. In particular, we found that soluble proteins had more interactions than membrane proteins. Moreover, amongst soluble proteins, those that were highly expressed, had many polar amino acids, and had many alpha helices, tended to have the most interaction partners. Interestingly, TopNet also turned up some systematic biases in the current yeast interaction network: on average, proteins with a known functional classification had many more interaction partners than those without. This phenomenon may reflect the incompleteness of the experimentally determined yeast interaction network.
生物网络是当前备受关注的一个话题，特别是随着一些大型全基因组相互作用数据集的发表。它们具有各种图论统计特征，如度分布、聚类系数、特征路径长度和直径。此外，真实的蛋白质网络相当复杂，通常可以通过系统地选择不同的节点和边分为许多子网。例如，蛋白质可以根据表达水平、长度、氨基酸组成、溶解度、二级结构和功能进行细分。一个具有挑战性的研究问题是比较子网的拓扑结构，寻找与不同类型蛋白质相关的全局差异。TopNet是一个自动化的网络工具，旨在解决这个问题，它可以计算和比较从任何给定蛋白质网络派生的不同子网的拓扑特征。它为嵌入在较大网络中的子网的网络统计计算提供了合理的解决方案，并给出了感兴趣子网的简化视图，使人们能够在其中浏览。构建TopNet之后，我们将其应用于目前可用的酵母相互作用网络和蛋白质类别。我们能够发现一些潜在的生物学关联。特别是，我们发现可溶性蛋白质比膜蛋白具有更多的相互作用。此外，在可溶性蛋白质中，那些高表达、含有许多极性氨基酸且含有许多α螺旋的蛋白质往往具有最多的相互作用伙伴。有趣的是，TopNet还揭示了当前酵母相互作用网络中的一些系统性偏差：平均而言，具有已知功能分类的蛋白质比没有的蛋白质具有更多的相互作用伙伴。这种现象可能反映了实验确定的酵母相互作用网络的不完整性。
