Accurate prediction of protein-protein interactions from sequence alignments using a Bayesian method
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
ID 156667
Author(s) Burger, Lukas; van Nimwegen, Erik
Author(s) at UniBasel van Nimwegen, Erik
Year 2008
Title Accurate prediction of protein-protein interactions from sequence alignments using a Bayesian method
Journal Molecular systems biology
Volume 4
Pages / Article-Number 165
Keywords Bayesian network, polyketide synthases, protein-protein interaction, signaling network, two-component systems
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.
Publisher EMBO
ISSN/ISBN 1744-4292
Full Text on edoc No
Digital Object Identifier DOI 10.1038/msb4100203
PubMed ID
ISI-Number WOS:000253761300004
Document type (ISI) Article

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