An Efficient Method for Discovering Functionally Important Motifs in a Group of Protein Structures

Abstract

Discovery of recurring structural motifs in a group of protein structures is key to many biomedical research and applications. In contrast to sequence motifs, structural motifs are more difficult to discover and the process demands more computational power. To date, a commonly used approach for discovering structural motifs shared by a pair of proteins is to represent protein structures as graphs, and then to find common maximal cliques using the product graph of the original graphs. In this approach, a maximal clique in the product graph corresponds to a common maximal clique shared by the original graphs. However, this method encounters computational challenge when the graph sizes are large. The challenge becomes even more daunting when the task is to find recurring spatial motifs shared by a group of protein structures, where many protein pairs need to be considered. To address this problem, we present an improved clique-based method that can quickly discover functionally important structural motifs shared by a group of proteins. The method achieves improved speed through exploiting the facts that functionally important residues are highly conserved and the graph edges that form a structural motif exist in all proteins that have the structural motif. Therefore, we filtered out non-conserved residues and edge types that are missing in some proteins. Since the edges of protein graphs are associated with continuous lengths, we developed a fast method to categorize edge types and find out edge types that exist in all proteins. The efficacy of the method was demonstrated by applying it to discover the catalytic triad motif on a set of protein structures that contain the motif. The results showed that the method could discover the catalytic triad motif with an improved speed, and at the same time the method reported much fewer biologically insignificant cliques, which allowed for a recursive approach to precisely discover functionally important spatial motifs shared by a group of protein structures.