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Accurate prediction of protein-protein interactions from sequence alignments using a Bayesian method

Burger, L. and van Nimwegen, E.. (2008) Accurate prediction of protein-protein interactions from sequence alignments using a Bayesian method. Molecular systems biology, Vol. 4 , 165.

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Official URL: http://edoc.unibas.ch/dok/A5259626

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Abstract

Accurate and large-scale prediction of protein-protein interactions directly from amino-acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino-acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two-component systems and comprehensively reconstruct two-component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome-wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome-wide two-component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of 'hub' nodes that distribute and integrate signals to and from up to tens of different interaction partners.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Bioinformatics (van Nimwegen)
UniBasel Contributors:van Nimwegen, Erik
Item Type:Article, refereed
Bibsysno:Link to catalogue
Publisher:EMBO
ISSN:1744-4292
Note:Publication type according to Uni Basel Research Database: Journal article
Last Modified:11 Oct 2012 15:16
Deposited On:22 Mar 2012 13:31

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