Jon, P.F.; (2006) Computational analysis of protein-protein interaction networks. Doctoral thesis , University of London.

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Abstract

Protein-protein interactions play a crucial role in all biological systems and an increasing emphasis has been placed on identifying the full repertoire of interacting proteins in cellular systems. Recent developments have enabled large-scale screening of protein interactions, which has yielded extensive information on protein-protein interactions. These efforts have been com plemented by a number of methods aimed at predicting interactions in silico, based on a variety of factors, ranging from sequence to structural features. This work explores the theme of protein-protein interactions, starting with the molecular aspect of proteins, leading on to predicting interaction partners and, at the top level, examining genome-scale protein-protein in teraction networks. On the molecular level, the sequence and structural details of proteins were examined, particularly focusing on the location of intron-exon boundaries in relation to protein interfaces. In addition, a homology-based method for predicting protein-protein interactions was de veloped, along with a scoring function for estimating the confidence of the prediction. Large-scale protein networks or 'interactomes' for key species were constructed, followed by a validation of the scoring function which confirmed its usefulness as an indicator of prediction reliability. The value of the predicted interactomes was demonstrated by two sep arate studies. First, the overall topology of the human interactome was ex amined and the network properties of cancer-related proteins compared to non-cancer proteins. Cancer-related proteins were shown to display net work characteristics that differed markedly from non-cancer proteins. The second study was aimed at identified key proteins likely to be implicated in cancer metastasis. This was done by mapping gene expression data from highly metastatic rat cell-lines onto the rat interactome. A cluster analysis of the data revealed distinct, tightly interconnected protein communities that play a role in metastasis.

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