Message-passing algorithms for the prediction of protein domain interactions from protein–protein interaction data

Iqbal, Musaddar and Freitas, Alex A. and Johnson, Colin G. and Vergassola, Massimo (2008) Message-passing algorithms for the prediction of protein domain interactions from protein–protein interaction data. Bioinformatics, 24 (18). pp. 2064-2070. ISSN 1460-2059. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Motivation: Cellular processes often hinge upon specific interactions among proteins, and knowledge of these processes at a system level constitutes a major goal of proteomics. In particular, a greater understanding of protein-protein interactions can be gained via a more detailed investigation of the protein domain interactions that mediate the interactions of proteins. Existing high-throughput experimental techniques assay protein-protein interactions, yet they do not provide any direct information on the interactions among domains. Inferences concerning the latter can be made by analysis of the domain composition of a set of proteins and their interaction map. This inference problem is non-trivial, however, due to the high level of noise generally present in experimental data concerning protein-protein interactions. This noise leads to contradictions, i.e. the impossibility of having a pattern of domain interactions compatible with the protein-protein interaction map. Results: We formulate the problem of prediction of protein domain interactions in a form that lends itself to the application of belief propagation, a powerful algorithm for such inference problems, which is based on message passing. The input to our algorithm is an interaction map among a set of proteins, and a set of domain assignments to the relevant proteins. The output is a list of probabilities of interaction between each pair of domains. Our method is able to effectively cope with errors in the protein-protein interaction dataset and systematically resolve contradictions. We applied the method to a dataset concerning the budding yeast Saccharomyces cerevisiae and tested the quality of our predictions by cross-validation on this dataset, by comparison with existing computational predictions, and finally with experimentally available domain interactions. Results compare favourably to those by existing algorithms. Availability: A C language implementation of the algorithm is available upon request.

Item Type: Article
Uncontrolled keywords: bioinformatics; proteins; machine learning; message-passing; belief propagation
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 29 Mar 2010 12:10
Last Modified: 02 May 2014 13:51
Resource URI: (The current URI for this page, for reference purposes)
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