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Topological analysis of structural roles of proteins in interactome networks

Published: 02 August 2010 Publication History

Abstract

The systematic analysis of protein-protein interactions is one of the most fundamental challenges to understand cellular organizations, processes and functions. The interaction between two proteins provides significant insight into their functional association. Recent high-throughput experiments have determined protein-protein interactions in the genome scale, called interactome. A wide range of graph theoretic, computational approaches have been presented to characterize protein functions from the interactome networks. In this work, we quantitatively analyze topological features of interactome networks. Our topological model is based on hierarchical modularity in the scale-free nature. First, we use connectivity and betweenness centrality to measure the likelihood of bridging two clusters for each node and edge. Next, we propose an efficient algorithm to detect clusters by collapsing the bridging nodes and edges. To assess the measurement of bridges, we compute the clustering coefficients of the networks which are built from successive deletion of bridging nodes. The alteration pattern of the clustering coefficients approximates the amount of bridging nodes in a network. We also investigate the biological importance of bridging nodes based on protein lethality information. The modularization results show that our approach accurately identifies functional modules in the interactome network of S. cerevisiae. We finally apply our approach to predicting biological functions of uncharacterized proteins in S. cerevisiae.

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cover image ACM Conferences
BCB '10: Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
August 2010
705 pages
ISBN:9781450304382
DOI:10.1145/1854776
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 August 2010

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Author Tags

  1. biological networks
  2. graph clustering
  3. graph mining
  4. protein interaction networks

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