Introducing… the DOMain-oriented Alignment of Interaction Networks (DOMAIN). Previous paradigms include the node-then-edge-alignment paradigm and direct-edge-alignment paradigm. In the latter, interactions are more likely to be conserved. Many studies have suggested that direct PPIs can be mediated by interactions of their domains.
Their method follows the direct-edge-alignment paradigm. In the method: try to find a set of alignable pairs of edgees (APEs), and then try to add some edges between the APEs. Finally they try to find high-scoring alignments. In step 1 (finding APEs) there are two assumptions: two proteins interact if at least one pair of their constituent domains interact, and second assumptions is that two DDIs are independent of each other. APEs are a pair of cross-species PPIs sharing at least one pair of DDIs. DDIs in common are plausibly responsible for PPIs. In scoring an APE, you esitmate species-specific DDI probabilities, and then calculate a mean as their joined probability. For all common DDI they yous a noisy-or formulation to calculate the score. The APE graph is the aligned network graph, and is motiviated by duplication-divergence models, and there are two parts: link dynamics and gene duplication.
To evaluate their method, they used data from DIP for 3 different species (PPI networks), and Pfam-A domains (protein-to-domain mapping), and the backbone DIP network (a subset of DIP). Two other similar methods are NetworkBLAST and MaWISh. In all of their metrics except one, they came out best. NetworkBLAST was the second best.
DOMAIN is the first algorithm to introduce domains to PPI network alignment problem, and the first attempt to align PPIs directly. It has better/similar performance than others, but it can only be applied to a subset of PPIs, however most functionally-annotated proteins are involved.
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