title: Computational prediction of domain interactions.
abstract: Conserved domains carry many of the functional features found in the proteins of an organism. This includes not only catalytic activity, substrate binding, and structural features but also molecular adapters, which mediate the physical interactions between proteins or proteins with other molecules. In addition, two conserved domains can be linked not by physical contact but by a common function like forming a binding pocket. Although a wealth of experimental data has been collected and carefully curated for protein-protein interactions, as of today little useful data is available from major databases with respect to relations on the domain level. This lack of data makes computational prediction of domain-domain interactions a very important endeavor. In this chapter, we discuss the available experimental data (iPfam) and describe some important approaches to the problem of identifying interacting and/or functionally linked domain pairs from different kinds of input data. Specifically, we will discuss phylogenetic profiling on the level of conserved protein domains on one hand and inference of domain-interactions from observed or predicted protein-protein interactions datasets on the other. We explore the predictive power of these predictions and point out the importance of deploying as many different methods as possible for the best results.
authors.forename: Philipp
authors.initials: P
authors.lastname: Pagel
authors.forename: Normann
authors.initials: N
authors.lastname: Strack
authors.forename: Matthias
authors.initials: M
authors.lastname: Oesterheld
authors.forename: Volker
authors.initials: V
authors.lastname: Stümpflen
authors.forename: Dmitrij
authors.initials: D
authors.lastname: Frishman
mesh_terms: D002384:Catalysis; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D013379:Substrate Specificity
chemical_list: D011506:Proteins