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

@article{citeulike:5413529, abstract = {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. Contact: xwchen@ku.edu Availability: http://www.ittc.ku.edu/[\~{}]xwchen/domainNetwork/ddinet.html Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btp480}, author = {Liu, Mei and Chen, Xue-wen and Jothi, Raja}, citeulike-article-id = {5413529}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btp480}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/content/25/19/2492.abstract}, citeulike-linkout-2 = {http://bioinformatics.oxfordjournals.org/content/25/19/2492.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19667081}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19667081}, day = {1}, doi = {10.1093/bioinformatics/btp480}, issn = {1460-2059}, journal = {Bioinformatics}, keywords = {prediction\_ddi}, month = {October}, number = {19}, pages = {2492--2499}, posted-at = {2009-08-11 02:24:38}, priority = {3}, title = {Knowledge-guided inference of domain-domain interactions from incomplete protein-protein interaction networks}, url = {http://dx.doi.org/10.1093/bioinformatics/btp480}, volume = {25}, year = {2009} }

TY - JOUR ID - citeulike:5413529 L3 - citeulike-article-id:5413529 N2 - 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. Contact: xwchen@ku.edu Availability: http://www.ittc.ku.edu/[~]xwchen/domainNetwork/ddinet.html Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btp480 IS - 19 JF - Bioinformatics SN - 1460-2059 EP - 2499 TI - Knowledge-guided inference of domain-domain interactions from incomplete protein-protein interaction networks VL - 25 SP - 2492 KW - prediction_ddi AU - Liu, Mei AU - Chen, Xue-wen AU - Jothi, Raja PY - 2009/10/01/ UR - http://dx.doi.org/10.1093/bioinformatics/btp480 ER -