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Quantitative analysis and prediction of curvature in leucine‐rich repeat proteins
Author(s) -
Hindle K. Lauren,
Bella Jordi,
Lovell Simon C.
Publication year - 2009
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.22440
Subject(s) - curvature , leucine rich repeat , computational biology , protein structure , range (aeronautics) , function (biology) , computer science , biology , algorithm , bioinformatics , biological system , mathematics , genetics , geometry , materials science , biochemistry , composite material , kinase
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/.