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


BACKGROUND
A C language implementation of the algorithm is available upon request.
Iqbal
Mudassar
M
Computing Laboratory and Centre for BioMedical Informatics, University of Kent, Canterbury, UK. mi26@kent.ac.uk
Freitas
Alex A
AA
Johnson
Colin G
CG
Vergassola
Massimo
M
D000465:Algorithms; D020816:Amino Acid Motifs; D001665:Binding Sites; D030562:Databases, Protein; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics