Combining functional and topological properties to identify core modules in Protein Interaction Networks
Combining functional and topological properties to identify core modules in Protein Interaction Networks
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DOI：
10.1002/prot.21071
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发表时间：
2006-09-01
影响因子：
2.9
通讯作者：
Olsson, Bjorn
中科院分区：
文献类型：
--
作者：
Lubovac, Zelmina;Gamalielsson, Jonas;Olsson, Bjorn
蛋白质组学中的大规模技术的进步，例如酵母两杂交筛选和质谱法，已使产生大型蛋白质相互作用网络（PIN）成为可能。在此类网络中识别密集子图的最新方法仅基于图理论特性。因此，有一种方法需要一种方法，该方法将使我们能够将特定领域的知识与拓扑特性相结合，以从大型网络中生成与功能相关的子图。本文介绍了两个用于分析引脚的替代网络测量，它们将功能信息与网络的拓扑特性结合在一起。这些措施称为加权聚类系数和加权平均最接近的邻居度，使用代表蛋白质之间相互作用的强度的权重，根据其语义相似性计算得出，这是基于蛋白质的基因本体论项。我们通过系统地将加权度量与其拓扑对应物进行比较，对酵母针的全球分析。为了显示加权度量的有用性，我们开发了一种用于识别功能模块的算法，称为Swemode（用于阐明模块的语义权重），该算法识别包含功能相似蛋白质的密集子图。所提出的方法基于节点的排名，即蛋白质，根据其加权邻域的凝聚力。排名最高的节点被认为是候选模块的种子。然后，根据所选参数，该算法通过每种种子蛋白的邻居迭代迭代，以鉴定具有高功能相似性的密集连接的蛋白质。使用实验确定的蛋白质 - 蛋白质相互作用的酵母两杂交数据集，我们证明了Swemode能够识别含有功能相似的蛋白质的致密簇。许多鉴定的模块对应于这些复合物的已知复合物或亚基。
Advances in large-scale technologies in proteomics, such as yeast two-hybrid screening and mass spectrometry, have made it possible to generate large Protein Interaction Networks (PINs). Recent methods for identifying dense sub-graphs in such networks have been based solely on graph theoretic properties. Therefore, there is a need for an approach that will allow us to combine domain-specific knowledge with topological properties to generate functionally relevant sub-graphs from large networks. This article describes two alternative network measures for analysis of PINs, which combine functional information with topological properties of the networks. These measures, called weighted clustering coefficient and weighted average nearest-neighbors degree, use weights representing the strengths of interactions between the proteins, calculated according to their semantic similarity, which is based on the Gene Ontology terms of the proteins. We perform a global analysis of the yeast PIN by systematically comparing the weighted measures with their topological counterparts. To show the usefulness of the weighted measures, we develop an algorithm for identification of functional modules, called SWEMODE (Semantic WEights for MODule Elucidation), that identifies dense sub-graphs containing functionally similar proteins. The proposed method is based on the ranking of nodes, i.e., proteins, according to their weighted neighborhood cohesiveness. The highest ranked nodes are considered as seeds for candidate modules. The algorithm then iterates through the neighborhood of each seed protein, to identify densely connected proteins with high functional similarity, according to the chosen parameters. Using a yeast two-hybrid data set of experimentally determined protein-protein interactions, we demonstrate that SWEMODE is able to identify dense clusters containing proteins that are functionally similar. Many of the identified modules correspond to known complexes or subunits of these complexes.