Journal Article

Knowledge-guided inference of domain–domain interactions from incomplete protein–protein interaction networks

Mei Liu, Xue-wen Chen and Raja Jothi

in Bioinformatics

Volume 25, issue 19, pages 2492-2499
Published in print October 2009 | ISSN: 1367-4803
Published online August 2009 | e-ISSN: 1460-2059 | DOI:

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Motivation: Protein-protein interactions (PPIs), though extremely valuable towards a better understanding of protein functions and cellular processes, do not provide any direct information about the regions/domains within the proteins that mediate the interaction. Most often, it is only a fraction of a protein that directly interacts with its biological partners. Thus, understanding interaction at the domain level is a critical step towards (i) thorough understanding of PPI networks; (ii) precise identification of binding sites; (iii) acquisition of insights into the causes of deleterious mutations at interaction sites; and (iv) most importantly, development of drugs to inhibit pathological protein interactions. In addition, knowledge derived from known domain–domain interactions (DDIs) can be used to understand binding interfaces, which in turn can help discover unknown PPIs.

Results: Here, we describe a novel method called K-GIDDI (knowledge-guided inference of DDIs) to narrow down the PPI sites to smaller regions/domains. K-GIDDI constructs an initial DDI network from cross-species PPI networks, and then expands the DDI network by inferring additional DDIs using a divide-and-conquer biclustering algorithm guided by Gene Ontology (GO) information, which identifies partial-complete bipartite sub-networks in the DDI network and makes them complete bipartite sub-networks by adding edges. Our results indicate that K-GIDDI can reliably predict DDIs. Most importantly, K-GIDDI's novel network expansion procedure allows prediction of DDIs that are otherwise not identifiable by methods that rely only on PPI data.



Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  7112 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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