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One of the most powerful techniques to study protein structures is to lookfor recurrent fragments (also called substructures or spatial motifs), then usethem as patterns to characterize the proteins under study. An emergent trendconsists in parsing proteins three-dimensional (3D) structures into graphs ofamino acids. Hence, the search of recurrent spatial motifs is formulated as aprocess of frequent subgraph discovery where each subgraph represents a spatialmotif. In this scope, several efficient approaches for frequent subgraphdiscovery have been proposed in the literature. However, the set of discoveredfrequent subgraphs is too large to be efficiently analyzed and explored in anyfurther process. In this paper, we propose a novel pattern selection approachthat shrinks the large number of discovered frequent subgraphs by selecting therepresentative ones. Existing pattern selection approaches do not exploit thedomain knowledge. Yet, in our approach we incorporate the evolutionaryinformation of amino acids defined in the substitution matrices in order toselect the representative subgraphs. We show the effectiveness of our approachon a number of real datasets. The results issued from our experiments show thatour approach is able to considerably decrease the number of motifs whileenhancing their interestingness.

Mining Representative Unsubstituted Graph Patterns Using Prior Similarity Matrix