Computational Biology and Chemistry
Algorithms for detecting significantly mutated pathways in cancer
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Tutorial on biological networks
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
International Journal of Knowledge Discovery in Bioinformatics
Protein Interactions for Functional Genomics
International Journal of Knowledge Discovery in Bioinformatics
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Motivation: The study of interactomes, or networks of protein-protein interactions, is increasingly providing valuable information on biological systems. Here we report a study of cancer proteins in an extensive human protein-protein interaction network constructed by computational methods. Results: We show that human proteins translated from known cancer genes exhibit a network topology that is different from that of proteins not documented as being mutated in cancer. In particular, cancer proteins show an increase in the number of proteins they interact with. They also appear to participate in central hubs rather than peripheral ones, mirroring their greater centrality and participation in networks that form the backbone of the proteome. Moreover, we show that cancer proteins contain a high ratio of highly promiscuous structural domains, i.e., domains with a high propensity for mediating protein interactions. These observations indicate an underlying evolutionary distinction between the two groups of proteins, reflecting the central roles of proteins, whose mutations lead to cancer. Contact: paul.bates@cancer.org.uk Supplementary information: The interactome data are available though the PIP (Potential Interactions of Proteins) web server at http://bmm.cancerresearchuk.org/servers/pip. Further additional material is available at http://bmm.cancerresearchuk.org/servers/pip/bioinformatics/