The leucine-rich repeat is a widespread structural motif of 20-30 amino acids with a characteristic repetitive sequence pattern rich in leucines. Leucine-rich repeat domains are built from tandems of two or more repeats and form curved solenoid structures that are particularly suitable for protein-protein interactions. Thousands of protein sequences containing leucine-rich repeats have been identified by automatic annotation methods. Three-dimensional structures of leucine-rich repeat domains determined to date reveal a degree of structural variability that translates into the considerable functional versatility of this protein superfamily. As the essential structural principles become well established, the leucine-rich repeat architecture is emerging as an attractive framework for structural prediction and protein engineering. This review presents an update of the current understanding of leucine-rich repeat structure at the primary, secondary, tertiary and quaternary levels and discusses specific examples from recently determined three-dimensional structures.

Leucine-rich repeat (LRR) proteins form a large and diverse family. They have a wide range of functions most of which involve the formation of protein-protein interactions. All known LRR structures form curved solenoids, although there is large variation in their curvature. It is this curvature that determines the shape and dimensions of the inner space available for ligand binding. Unfortunately, large-scale parameters such as the overall curvature of a protein domain are extremely difficult to predict. Here, we present a quantitative analysis of determinants of curvature of this family. Individual repeats typically range in length between 20 and 30 residues and have a variety of secondary structures on their convex side. The observed curvature of the LRR domains correlates poorly with the lengths of their individual repeats. We have, therefore, developed a scoring function based on the secondary structure of the convex side of the protein that allows prediction of the overall curvature with a high degree of accuracy. We also demonstrate the effectiveness of this method in selecting a suitable template for comparative modeling. We have developed an automated, quantitative protocol that can be used to predict accurately the curvature of leucine-rich repeat proteins of unknown structure from sequence alone. This protocol is available as an online resource at http://www.bioinf.manchester.ac.uk/curlrr/.