Frequent subgraph mining for biologically meaningful structural motifs

Identification of biologically relevant motifs in proteins is a long-standing problem in bioinformatics, especially when considering distantly related proteins where sequence analysis alone becomes increasingly difficult. Here we present a novel approach to identify such motifs in protein three-dimensional structures without depending on sequence alignment by representing structures as graphs in the form of residue interaction networks and employing a modified frequent subgraph mining algorithm. These networks represent residues as vertices while contacts between residues are denoted by edges labeled with Euclidean distances. We use frequent subgraph mining to determine all subgraphs that are subgraph isomorphic to, i.e. are contained in, at least a given number of such networks generated from structures in the same protein family. For this we introduce two extensions of the classical frequent subgraph mining: approximate matching of distance-based labels to account for small variations between protein structures and scoring as well as score-based filtering of subgraphs in order to identify structurally conserved motifs and to counteract the expanding size of the search space. This approach was then validated by demonstrating that it can rediscover previously characterized functionally important structural motifs in selected protein families. For further validation we show that it is also able to identify motifs that correspond to patterns in the PROSITE database. We then applied our approach to all superfamilies in the SCOP database and found an enrichment of residues in the ligand binding site in the discovered motifs evidencing their functional importance. Finally we use the approach to discover a novel structural motif in jelly-roll capsid proteins found in members of the picornavirus-like superfamily. This is presented together with an efficient open source implementation of the algorithm called RINminer.