ArchDB 2014: structural classification of loops in proteins

Jaume Bonet,Joan Planas-Iglesias,Javier Garcia-Garcia,Baldo Oliva,Manuel A. Marín-López,Narcis Fernandez-Fuentes

doi:10.1093/nar/gkt1189

Abstract

The function of a protein is determined by its three-dimensional structure, which is formed by regular (i.e. β-strands and α-helices) and non-periodic structural units such as loops. Compared to regular structural elements, non-periodic, non-repetitive conformational units enclose a much higher degree of variability—raising difficulties in the identification of regularities, and yet represent an important part of the structure of a protein. Indeed, loops often play a pivotal role in the function of a protein and different aspects of protein folding and dynamics. Therefore, the structural classification of protein loops is an important subject with clear applications in homology modelling, protein structure prediction, protein design (e.g. enzyme design and catalytic loops) and function prediction. ArchDB, the database presented here (freely available at http://sbi.imim.es/archdb), represents such a resource and has been an important asset for the scientific community throughout the years. In this article, we present a completely reworked and updated version of ArchDB. The new version of ArchDB features a novel, fast and user-friendly web-based interface, and a novel graph-based, computationally efficient, clustering algorithm. The current version of ArchDB classifies 149,134 loops in 5739 classes and 9608 subclasses.

Highlights

The three-dimensional (3D) structure of a protein is key to determine its function [1,2]
Structural similarity inferred from these classifications has been used, with different degrees of success, to predict protein functions [4] and interactions [5]
Most of these techniques are based on mapping domains over protein sequences via assignation or protein structure modelling [1,3]

Summary

Introduction

The three-dimensional (3D) structure of a protein is key to determine its function [1,2]. Structural similarity inferred from these classifications has been used, with different degrees of success, to predict protein functions [4] and interactions [5]. In a previous work we used the density search (DS) algorithm to combine the geometrical relationship between two secondary structures and the conformation of their linking loop to obtain an automated classification [16].

Results

Conclusion